travel demand patterns

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9 Introduction to Transportation Modeling: Travel Demand Modeling and Data Collection

Chapter overview.

Chapter 9 serves as an introduction to travel demand modeling, a crucial aspect of transportation planning and policy analysis. As explained in previous chapters, the spatial distribution of activities such as employment centers, residential areas, and transportation systems mutually influence each other. The utilization of travel demand forecasting techniques leads to dynamic processes in urban areas. A comprehensive grasp of travel demand modeling is imperative for individuals involved in transportation planning and implementation.

This chapter covers the fundamentals of the traditional four-step travel demand modeling approach. It delves into the necessary procedures for applying the model, including establishing goals and criteria, defining scenarios, developing alternatives, collecting data, and conducting forecasting and evaluation.

Following this chapter, each of the four steps will be discussed in detail in Chapters 10 through 13.

Learning Objectives

• Describe the need for travel demand modeling in urban transportation and relate it to the structure of the four-step model (FSM).
• Summarize each step of FSM and the prerequisites for each in terms of data requirement and model calibration.
• Summarize the available methods for each of the first three steps of FSM and compare their reliability.
• Identify assumptions and limitations of each of the four steps and ways to improve the model.

Introduction

Transportation planning and policy analysis heavily rely on travel demand modeling to assess different policy scenarios and inform decision-making processes. Throughout our discussion, we have primarily explored the connection between urban activities, represented as land uses, and travel demands, represented by improvements and interventions in transportation infrastructure. Figure 9.1 provides a humorous yet insightful depiction of the transportation modeling process. In preceding chapters, we have delved into the relationship between land use and transportation systems, with the houses and factories in the figure symbolizing two crucial inputs into the transportation model: households and jobs. The output of this model comprises transportation plans, encompassing infrastructure enhancements and programs. Chapter 9 delves into a specific model—travel demand modeling. For further insights into transportation planning and programming, readers are encouraged to consult the UTA OERtransport book, “Transportation Planning, Policies, and History.”

Travel demand models forecast how people will travel by processing thousands of individual travel decisions. These decisions are influenced by various factors, including living arrangements, the characteristics of the individual making the trip, available destination options, and choices regarding route and mode of transportation. Mathematical relationships are used to represent human behavior in these decisions based on existing data.

Through a sequential process, transportation modeling provides forecasts to address questions such as:

• What will the future of the area look like?
• What is the estimated population for the forecasting year?
• How are job opportunities distributed by type and category?
• What are the anticipated travel patterns in the future?
• How many trips will people make? ( Trip Generation )
• Where will these trips end? ( Trip Distribution )
• Which transportation mode will be utilized? ( Mode Split )
• What will be the demand for different corridors, highways, and streets? ( Traffic Assignment )
• Lastly, what impact will this modeled travel demand have on our area? (Rahman, 2008).

9.2 Four-step Model

According to the questions above, Transportation modeling consists of two main stages, regarding the questions outlined above. Firstly, addressing the initial four questions involves demographic and land use analysis, which incorporates the community vision collected through citizen engagement and input. Secondly, the process moves on to the four-step travel demand modeling (FSM), which addresses questions 5 through 8. While FSM is generally accurate for aggregate calculations, it may occasionally falter in providing a reliable test for policy scenarios. The limitations of this model will be explored further in this chapter.

In the first stage, we develop an understanding of the study area from demographic information and urban form (land-use distribution pattern). These are important for all the reasons we discussed in this book. For instance, we must obtain the current age structure of the study area, based on which we can forecast future birth rates, death, and migrations  (Beimborn & Kennedy, 1996).

Regarding economic forecasts, we must identify existing and future employment centers since they are the basis of work travel, shopping travel, or other travel purposes. Empirically speaking, employment often grows as the population grows, and the migration rate also depends on a region’s economic growth. A region should be able to generate new employment while sustaining the existing ones based upon past trends and form the basis for judgment for future trends (Mladenovic & Trifunovic, 2014).

After forecasting future population and employment, we must predict where people go (work, shop, school, or other locations). Land-use maps and plans are used in this stage to identify the activity concentrations in the study area. Future urban growth and land use can follow the same trend or change due to several factors, such as the availability of open land for development and local plans and  zoning ordinances (Beimborn & Kennedy, 1996). Figure 9.3 shows different possible land-use patterns frequently seen in American cities.

Land-use pattern can also be forecasted through the integration of land use and transportation as we explored in previous chapters.

Figure 9.3 above shows a simple structure of the second stage of FSM.

Once the number and types of trips are predicted, they are assigned to various destinations and modes. In the final step, these trips are allocated to the transportation network to compute the total demand for each road segment. During this second stage, additional choices such as the time of travel and whether to travel at all can be modeled using choice models (McNally, 2007). Travel forecasting involves simulating human behavior through mathematical series and calculations, capturing the sequence of decisions individuals make within an urban environment.

The first attempt at this type of analysis in the U.S. occurred during the post-war development period, driven by rapid economic growth. The influential study by Mitchell and Rapkin (1954) emphasized the need to establish a connection between travel and activities, highlighting the necessity for a comprehensive framework. Initial development models for trip generation, distribution, and diversion emerged in the 1950s, leading to the application of the four-step travel demand modeling (FSM) approach in a transportation study in the Chicago area. This model was primarily highway-oriented, aiming to compare new facility development and improved traffic engineering. In the 1960s, federal legislation mandated comprehensive and continuous transportation planning, formalizing the use of FSM. During the 1970s, scholars recognized the need to revise the model to address emerging concerns such as environmental issues and the rise of multimodal transportation systems. Consequently, enhancements were made, leading to the development of disaggregate travel demand forecasting and equilibrium assignment methods that complemented FSM. Today, FSM has been instrumental in forecasting travel demand for over 50 years (McNally, 2007; Weiner, 1997).

Initially outlined by Mannheim (1979), the basic structure of FSM was later expanded by Florian, Gaudry, and Lardinois (1988). Figure 9.3 illustrates various influential components of travel demand modeling. In this representation, “T” represents transportation, encompassing all elements related to the transportation system and its services. “A” denotes the activity system, defined according to land-use patterns and socio-demographic conditions. “P” refers to transportation network performance. “D,” which stands for demand, is generated based on the land-use pattern. According to Florian, Gaudry, and Lardinois (1988), “L” and “S” (location and supply procedures) are optional parts of FSM and are rarely integrated into the model.

A crucial aspect of the process involves understanding the input units, which are defined both spatially and temporally. Demand generates person trips, which encompass both time and space (e.g., person trips per household or peak-hour person trips per zone). Performance typically yields a level of service, defined as a link volume capacity ratio (e.g., freeway vehicle trips per hour or boardings per hour for a specific transit route segment). Demand is primarily defined at the zonal level, whereas performance is evaluated at the link level.

It is essential to recognize that travel forecasting models like FSM are continuous processes. Model generation takes time, and changes may occur in the study area during the analysis period.

Before proceeding with the four steps of FSM, defining the study area is crucial. Like most models discussed, FSM uses traffic analysis zones (TAZs) as the geographic unit of analysis. However, a higher number of TAZs generally yield more accurate results. The number of TAZs in the model can vary based on its purpose, data availability, and vintage. These zones are characterized or categorized by factors such as population and employment. For modeling simplicity, FSM assumes that trip-making begins at the center of a zone (zone centroid) and excludes very short trips that start and end within a TAZ, such as those made by bike or on foot.

Furthermore, highway systems and transit systems are considered as networks in the model. Highway or transit line segments are coded as links, while intersections are represented as nodes. Data regarding network conditions, including travel times, speeds, capacity, and directions, are utilized in the travel simulation process. Trips originate from trip generation zones, traverse a network of links and nodes, and conclude at trip attraction zones.

Trip Generation

Trip generation is the first step in the FSM model. This step defines the magnitude of daily travel in the study area for different trip purposes. It will also provide us with an estimate of the total trips to and from each zone, creating a trip production and attraction matrix for each trip’s purpose. Trip purposes are typically categorized as follows:

• Home-based work trips (work trips that begin or end at home),
• Home-based shopping trips,
• Home-based other trips,
• School trips,
• Non-home-based trips (trips that neitherbeginnorendathome),
• Trucktrips,and
• Taxitrips(Ahmed,2012).

Trip attractions are based on the level of employment in a zone. In the trip generation step, the assumptions and limitations are listed below:

• Independent decisions: Travel behavior is affected by many factors generated within a household; the model ignores most of these factors. For example, childcare may force people to change their travel plans.
• Limited trip purposes: This model consists of a limited number of trip purposes for simplicity, giving rise to some model limitations. Take shopping trips, for example; they are all considered in the same weather conditions. Similarly, we generate home-based trips for various purposes (banking, visiting friends, medical reasons, or other purposes), all of which are affected by factors ignored by the model.
• Trip combinations: Travelers are often willing to combine various trips into a chain of short trips. While this behavior creates a complex process, the FSM model treats this complexity in a limited way.
• Feedback, cause, and effect problems: Trip generation often uses factors that are a function of the number of trips. For instance, for shopping trip attractions in the FSM model, we assume they are a retail employment function. However, it is logical to assume how many customers these retail centers attract. Alternatively, we can assume that the number of trips a household makes is affected by the number of private cars they own. Nevertheless, the activity levels of families determine the total number of cars.

As mentioned, trip generation process estimations are done separately for each trip purpose. Equations 1 and 2 show the function of trip generation and attraction:

where Oi and Dj trip are generated and attracted respectively, x refers to socio-economic characteristics, and y refers to land-use properties.

Generally, FSM aggregates different trip purposes previously listed into three categories: home-based work trips (HBW) , home-based other (or non-work) trips (HBO) , and non-home-based trips (NHB) . Trip ends are either the origin (generation) or destination (attraction), and home-end trips comprise most trips in a study area. We can also model trips at different levels, such as zones, households, or person levels (activity-based models). Household-level models are the most common scale for trip productions, and zonal-level models are appropriate for trip attractions (McNally, 2007).

There are three main methods for a trip generation or attraction.

• The first method is multiple regression based on population, jobs, and income variables.
• The second method in this step is experience-based analysis, which can show us the ratio of trips generated frequently.
• The third method is cross-classification . Cross-classification is like the experience-based analysis in that it uses trip rates but in an extended format for different categories of trips (home-based trips or non-home-based trips) and different attributes of households, such as car ownership or income.

Elaborating on the differences between these methods, category analysis models are more common for the trip generation model, while regression models demonstrate better performance for trip attractions (Meyer, 2016). Production models are recognized to be influenced by a range of explanatory and policy-sensitive variables (e.g., car ownership, household income, household size, and the number of workers). However, estimation is more problematic for attraction models because regional travel surveys are at the household level (thus providing more accurate data for production models) and not for nonresidential land uses (which is important for trip attraction). Additionally, estimation can be problematic because explanatory trip attraction variables may usually underperform (McNally, 2007). For these reasons, survey data factoring is required prior to relating sample trips to population-level attraction variables, typically achieved via regression analysis. Table 9.1 shows the advantages and disadvantages of each of these two models.

Trip Distribution

Thus far, the number of trips beginning or ending in a particular zone have been calculated. The second step explores how trips are distributed between zones and how many trips are exchanged between two zones. Imagine a shopping trip. There are multiple options for accessible shopping malls accessible. However, in the end, only one will be selected for the destination. This information is modeled in the second step as a distribution of trips. The second step results are usually a very large Origin-Destination (O-D) matrix for each trip purpose. The O-D matrix can look like the table below (9.2), in which sum of Tij by j shows us the total number of trips attracted in zone J and the sum of Tij by I yield the total number of trips produced in zone I.

Up to this point, we have calculated the number of trips originating from or terminating in a specific zone. The next step involves examining how these trips are distributed across different zones and how many trips are exchanged between pairs of zones. To illustrate, consider a shopping trip: there are various options for reaching shopping malls, but ultimately, only one option is chosen as the destination. This process is modeled in the second step as the distribution of trips. The outcome of this step typically yields a large Origin-Destination (O-D) matrix for each trip purpose. An O-D matrix might resemble the table below (9.2), where the sum of Tij by j indicates the total number of trips attracted to zone J, and the sum of Tij by I represents the total number of trips originating from zone I.

T ij = trips produced at I and attracted at j

P i = total trip production at I

A j = total trip attraction at j

F ij = a calibration term for interchange ij , (friction factor) or travel

time factor ( F ij =C/t ij n )

C= calibration factor for the friction factor

K ij = a socioeconomic adjustment factor for interchange ij

i = origin zone

n = number of zones

Different methods (units) in the gravity model can be used to perform distance measurements. For instance, distance can be represented by time, network distance, or travel costs. For travel costs, auto travel cost is the most common and straightforward way of monetizing distance. A combination of different costs, such as travel time, toll payments, parking payments, etc., can also be used. Alternatively, a composite cost of both car and transit costs can be used (McNally, 2007).

Generalized travel costs can be a function of time divided into different segments. For instance, public transit time can be divided into the following segments: in-vehicle time, walking time, waiting time, interchange time, fare, etc. Since travelers perceive time value differently for each segment (like in-vehicle time vs. waiting time), weights are assigned based on the perceived value of time (VOT). Similarly, car travel costs can be categorized into in-vehicle travel time or distance, parking charge, tolls, etc.

As with the first step in the FSM model, the second step has assumptions and limitations that are briefly explained below.

• Constant trip times: In order to utilize the model for prediction, it assumes that the duration of trips remains constant. This means that travel distances are measured by travel time, and the assumption is that enhancements in the transportation system, which reduce travel times, are counterbalanced by the separation of origins and destinations.
• Automobile travel times to represent distance: We utilize travel time as a proxy for travel distance. In the gravity model, this primarily relies on private car travel time and excludes travel times via other modes like public transit. This leads to a broader distribution of trips.
• Limited consideration of socio-economic and cultural factors: Another drawback of the gravity model is its neglect of certain socio-economic or cultural factors. Essentially, this model relies on trip production and attraction rates along with travel times between them for predictions. Consequently, it may overestimate trip rates between high-income groups and nearby low-income Traffic Analysis Zones (TAZs). Therefore, incorporating more socio-economic factors into the model would enhance accuracy.
• Feedback issues: The gravity model’s reliance on travel times is heavily influenced by congestion levels on roads. However, measuring congestion proves challenging, as discussed in subsequent sections. Typically, travel times are initially assumed and later verified. If the assumed values deviate from actual values, they require adjustment, and the calculations need to be rerun.

Mode choice

FSM model’s third step is a mode-choice estimation that helps identify what types of transportation travelers use for different trip purposes to offer information about users’ travel behavior. This usually results in generating the share of each transportation mode (in percentages) from the total number of trips in a study area using the utility function (Ahmed, 2012). Performing mode-choice estimations is crucial as it determines the relative attractiveness and usage of various transportation modes, such as public transit, carpooling, or private cars. Modal split analysis helps evaluate improvement programs or proposals (e.g., congestion pricing or parking charges) aimed at enhancing accessibility or service levels. It is essential to identify the factors contributing to the utility and disutility of different modes for different travel demands (Beimborn & Kennedy, 1996). Comparing the disutility of different modes between two points aids in determining mode share. Disutility typically refers to the burdens of making a trip, such as time, costs (fuel, parking, tolls, etc.). Once disutility is modeled for different trip purposes between two points, trips can be assigned to various modes based on their utility. As discussed in Chapter 12, a mode’s advantage in terms of utility over another can result in a higher share of trips using that mode.

The assumptions and limitations for this step are outlined as follows:

• Choices are only affected by travel time and cost: This model assumes that changes in mode choices occur solely if transportation cost or travel time in the transportation network or transit system is altered. For instance, a more convenient transit mode with the same travel time and cost does not affect the model’s results.
• Omitted factors: Certain factors like crime, safety, and security, which are not included in the model, are assumed to have no effect, despite being considered in the calibration process. However, modes with different attributes regarding these omitted factors yield no difference in the results.
• Simplified access times: The model typically overlooks factors related to the quality of access, such as neighborhood safety, walkability, and weather conditions. Consequently, considerations like walkability and the impact of a bike-sharing program on the attractiveness of different modes are not factored into the model.
• Constant weights: The model assumes that the significance of travel time and cost remains constant for all trip purposes. However, given the diverse nature of trip purposes, travelers may prioritize travel time and cost differently depending on the purpose of their trip.

The most common framework for mode choice models is the nested logit model, which can accommodate various explanatory variables. However, before the final step, results need to be aggregated for each zone (Koppelman & Bhat, 2006).

A generalized modal split chart is depicted in Figure 9.5.

In our analysis, we can use binary logit models (dummy variable for dependent variable) if we have two modes of transportation (like private cars and public transit only). A binary logit model in the FSM model shows us if changes in travel costs would occur, such as what portion of trips changes by a specific mode of transport. The mathematical form of this model is:

where: P_ij  1= The proportion of trips between i and j by mode 1 . Tij  1= Trips between i and j by mode 1.

Cij 1= Generalized cost of travel between i and j by mode 1 .

Cij^2= Generalized cost of travel between i and j by mode 2 .

b= Dispersion Parameter measuring sensitivity to cost.

It is also possible to have a hierarchy of transportation modes for using a binary logit model. For instance, we can first conduct the analysis for the private car and public transit and then use the result of public transit to conduct a binary analysis between rail and bus.

Trip assignment

After breaking down trip counts by mode of transportation, we analyze the routes commuters take from their starting point to their destination, especially for private car trips. This process is known as trip assignment and is the most intricate stage within the FSM model. Initially, the minimum path assigns trips for each origin-destination pair based on either travel costs or time. Subsequently, the assigned volume of trips is compared to the capacity of the route to determine if congestion would occur. If congestion does happen (meaning that traffic volume exceeds capacity), the speed of the route needs to be decreased, resulting in increased travel costs or time. When the Volume/Capacity ratio (v/c ratio) changes due to congestion, it can lead to alterations in both speed and the shortest path. This characteristic of the model necessitates an iterative process until equilibrium is achieved.

The process for public transit is similar, but with one distinction: instead of adjusting travel times, headways are adjusted. Headway refers to the time between successive arrivals of a vehicle at a stop. The duration of headways directly impacts the capacity and volume for each transit vehicle. Understanding the concept of equilibrium in the trip assignment step is crucial because it guides the iterative process of the model. The conclusion of this process is marked by equilibrium, a concept known as Wardrop equilibrium. In Wardrop equilibrium, traffic naturally organizes itself in congested networks so that individual commuters do not switch routes to reduce travel time or costs. Additionally, another crucial factor in this step is the time of day.

Like previous steps, the following assumptions and limitations are pertinent to the trip assignment step:

1.    Delays on links: Most traffic assignment models assume that delays occur on the links, not the intersections. For highways with extensive intersections, this can be problematic because intersections involve highly complex movements. Intersections are excessively simplified if the assignment process does not modify control systems to reach an equilibrium.

2.    Points and links are only for trips: This model assumes that all trips begin and finish at a single point in a zone (centroids), and commuters only use the links considered in the model network. However, these points and links can vary in the real world, and other arterials or streets might be used for commutes.

3.    Roadway capacities: In this model, a simple assumption helps determine roadways’ capacity. Capacity is found based on the number of lanes a roadway provides and the type of road (highway or arterial).

4.    Time of the day variations: Traffic volume varies greatly throughout the day and week. In this model, a typical workday of the week is considered and converted to peak hour conditions. A factor used for this step is called the hour adjustment factor. This value is critical because a small number can result in a massive difference in the congestion level forecasted on the model.

5.    Emphasis on peak hour travel: The model forecasts for the peak hour but does not forecast for the rest of the day. The models make forecasts for a typical weekday but neglect specific conditions of that time of the year. After completing the fourth step, precise approximations of travel demand or traffic count on each road are achieved. Further models can be used to simulate transportation’s negative or positive externalities. These externalities include air pollution, updated travel times, delays, congestion, car accidents, toll revenues, etc. These need independent models such as emission rate models (Beimborn & Kennedy, 1996).

The basic equilibrium condition point calculation is an algorithm that involves the computation of minimum paths using an all-or-nothing (AON) assignment model to these paths. However, to reach equilibrium, multiple iterations are needed. In AON, it is assumed that the network is empty, and a free flow is possible. The first iteration of the AON assignment requires loading the traffic by finding the shortest path. Due to congestion and delayed travel times, the

previous shortest paths may no longer be the best minimum path for a pair of O-D. If we observe a notable decrease in travel time or cost in subsequent iterations, then it means the equilibrium point has not been reached, and we must continue the estimation. Typically, the following factors affect private car travel times: distance, free flow speed on links, link capacity, link speed capacity, and speed flow relationship .

The relationship between the traffic flow and travel time equation used in the fourth step is:

t= link travel time per length unit

t 0 =free-flow travel time

v=link flow

c=link capacity

a, b, and n are model (calibrated) parameters

Model improvement

Improvements to FSM continue to generate more accurate results. Since transportation dynamics in urban and regional areas are under the complex influence of various factors, the existing models may not be able to incorporate all of them. These can be employer-based trip reduction programs, walking and biking improvement schemes, a shift in departure (time of the day), or more detailed information on socio-demographic and land-use-related factors. However, incorporating some of these variables is difficult and can require minor or even significant modifications to the model and/or computational capacities or software improvements. The following section identifies some areas believed to improve the FSM model performance and accuracy.

•      Better data: An effective way of improving the model accuracy is to gather a complete dataset that represents the general characteristics of the population and travel pattern. If the data is out- of-date or incomplete, we will get poor results.

•      Better modal split: As you saw in previous sections, the only modes incorporated into the model are private car and public transit trips, while in some cities, a considerable fraction of trips are made by bicycle or by walking. We can improve our models by producing methods to consider these trips in the first and third steps.

•      Auto occupancy: In contemporary transportation planning practices, especially in the US, some new policies are emerging for carpooling. We can calculate auto occupancy rates using different mode types, such as carpooling, sensitive to private car trips’ disutility, parking costs, or introducing a new HOV lane.

•      Time of the day: In this chapter, the FSM framework discussed is oriented toward peak hour (single time of the day) travel patterns. Nonetheless, understanding the nature of congestion in other hours of the day is also helpful for understanding how travelers choose their travel time.

•      A broader trip purpose: Additional trip purposes may provide a better understanding of the

factors affecting different trip purposes and trip-chaining behaviors. We can improve accuracy by having more trip purposes (more disaggregate input and output for the model).

• The concept of access: As discussed, land-use policies that encourage public transit use or create amenities for more convenient walking are not present in the model. Developing factors or indices that reflect such improvements in areas with high demand for non-private vehicles and incorporating them in choice models can be a good improvement.
• Land use feedback: To better understand interactions between land use and travel demand, a land-use simulation model can be added to these steps to determine how a proposed transportation change will lead to a change in land use.
• Intersection delays: As mentioned in the fourth step, intersections in major highways create significant delays. Incorporating models that calculate delays at these intersections, such as stop signs, could be another improvement to the model.

A Simple Example of the FSM model

An example of FSM is provided in this section to illustrate a typical application of this model in the U.S. In the first phase, the specifications about the transportation network and household data are needed. In this hypothetical example, 5 percent of households in each TAZ were sampled and surveyed, which generated 1,955 trips in 200 households. As a hypothetical case study, this sample falls below the standard required for statistical significance but is relevant to demonstrate FSM.

A home interview survey was carried out to gather data from a five percent sample of households in each TAZ. This survey resulted in 1,852 trips from 200 households. It is important to note that the sample size in this example falls below the minimum required for statistical significance, as it is intended for learning purposes only.

Table 9.3 provides network information such as speed limits, number of lanes, and capacity. Table 9.4 displays the total number of households and jobs in three industry sectors for each zone. Additionally, Table 9.5 breaks down the household data into three car ownership groups, which is one of the most significant factors influencing trip making.

In the first step (trip generation), a category model (i.e., cross-classification) helped estimate trips. The sampled population’s sociodemographic and trip data for different purposes helped calculate this estimate. Since research has shown the significant effect of auto ownership on private car trip- making (Ben-Akiva & Lerman, 1974), disaggregating the population based on the number of private cars generates accurate results. Table 9.7 shows the trip-making rate for different income and auto ownership groups.

Also, as mentioned in previous sections, multiple regression estimation analysis can be used to generate the results for the attraction model. Table 9.7 shows the equations for each of the trip purposes.

After estimating production and attraction, the models are used for population data to generate results for the first step. Next, comparing the results of trip production and attraction, we can observe that the total number of trips for each purpose is different. This can be due to using different methods for production and attraction. Since the production method is more reliable, attraction is typically normalized by  production. Also, some external zones in our study area are either attracting trips from our zones or generating them. In this case, another alternative is to extend the boundary of the study area and include more zones.

As mentioned, the total number of trips produced and attracted are different in these results. To address this mismatch, we can use a balance factor to come up with the same trip generation and attraction numbers if we want to keep the number of zones within our study area. Alternatively, we can consider some external stations in addition to designated zones. In this example, using the latter seems more rational because, as we saw in Table 9.4, there are more jobs than the number of households aggregately, and our zone may attract trips from external locations.

For the trip distribution step, we use the gravity model. For internal trips, the gravity model is:

and f(tij) is some function of network level of service (LOS)

To apply the gravity model, we need to calculate the impedance function first, which is represented here by travel cost. Table 9.9 shows the minimum travel path between each pair of zones ( skim tree ) in a matrix format in which each cell represents travel time required to travel between the corresponding row and column of that cell.

Table 9.9-Travel cost table (skim tree)

Note. Table adapted from “The Four-Step Model” by M. McNally, In D. A. Hensher, & K. J. Button (Eds.), Handbook of transport modelling , Volume1, p. 5, Bingley, UK: Emerald Publishing. Copyright 2007 by Emerald Publishing.

With having minimum travel costs between each pair of zones, we can calculate the impedance function for each trip purpose using the formula

Table 9.10 shows the model parameters for calculating the impedance function for different trip purposes:

After calculating the impedance function , we can calculate the result of the trip distribution. This stage generates trip matrices since we calculate trips between each zone pair. These matrices are usually in “Origin-Destination” (OD) format and can be disaggregated by the time of day. Field surveys help us develop a base-year trip distribution for different periods and trip purposes. Later, these empirical results will help forecast trip distribution. When processing the surveys, the proportion of trips from the production zone to the attraction zone (P-A) is also generated. This example can be seen in Table 9.11.  Looking at a specific example, the first row in table is for the 2-hour morning peak commute time period. The table documents that the production to attraction factor for the home-based work trip is 0.3.  Unsurprisingly, the opposite direction, attraction to production zone is 0.0 for this time of day. Additionally, the table shows that the factor for HBO and NHB trips are low but do occur during this time period. This could represent shopping trips or trips to school. Table 9.11 table also contains the information for average occupancy levels of vehicles from surveys. This information can be used to convert person trips to vehicle trips or vice versa.

Table 9.11 Trip distribution rates for different time of the day and trip purposes

The O-D trip table is calculated by adding the  multiplication of the P-to-A factor by corresponding cell of the P-A trip table and adding the corresponding cell of the transposed P-A trip table multiplied by the A-to-P factor. These results, which are the final output of second step, are shown in Table 9.12.

Once the Production-Attraction (P-A) table is transformed into Origin-Destination (O-D) format and the complete O-D matrix is computed, the outcomes will be aggregated for mode choice and traffic assignment modeling. Further elaboration on these two steps will be provided in Chapters 11 and 12.

In this chapter, we provided a comprehensive yet concise overview of four-step travel demand modeling including the process, the interrelationships and input data, modeling part and extraction of outputs. The complex nature of cities and regions in terms of travel behavior, the connection to the built environment and constantly growing nature of urban landscape, necessitate building models that are able to forecast travel patterns for better anticipate and prepare for future conditions from multiple perspectives such as environmental preservation, equitable distribution of benefits, safety, or efficiency planning. As we explored in this book, nearly all the land-use/transportation models embed a transportation demand module or sub model for translating magnitude of activities and interconnections into travel demand such as VMT, ridership, congestion, toll usage, etc. Four-step models can be categorized as gravity-based, equilibrium-based models from the traditional approaches. To improve these models, several new extensions has been developed such as simultaneous mode and destination choice, multimodality (more options for mode choice with utility), or microsimulation models that improve granularity of models by representing individuals or agents rather than zones or neighborhoods.

Travel demand modeling are models that predicts the flow of traffic or travel demand between zones in a city using a sequence of steps.

• Intermodality refers to the concept of utilizing two or more travel modes for a trip such as biking to a transit station and riding the light rail.
• Multimodality is a type of transportation network in which a variety of modes such as public transit, rail, biking networks, etc. are offered.

Zoning ordinances is legal categorization of land use policies that permits or prohibits certain built environment factors such as density.

Volume capacity ratio is ratio that divides the demand on a link by the capacity to determine the level of service.

• Zone centroid is usually the geometric center of a zone in modeling process where all trips originate and end.

Home-based work trips (HBW) are the trips that originates from home location to work location usually in the AM peak.

•  Home-based other (or non-work) trips (HBO) are the trips that originates from home to destinations other than work like shopping or leisure.

Non-home-based trips (NHB) are the trips that neither origin nor the destination are home or they are part of a linked trip.

Cross-classification is a method for trip production estimation that disaggregates trip rates in an extended format for different categories of trips like home-based trips or non-home-based trips and different attributes of households such as car ownership or income.

• Generalized travel costs is a function of time divided into sections such as in vehicle time vs. waiting time or transfer time in a transit trip.

Binary logit models is a type of logit model where the dependent variable can take only a value of 0 or 1.

• Wardrop equilibrium is a state in traffic assignment model where are drivers are reluctant to change their path because the average travel time is at a minimum.

All-or-nothing (AON) assignment model is a model that assumes all trips between two zones uses the shortest path regardless of volume.

Speed flow relationship is a function that determines the speed based on the volume (flow)

skim tree is structure of travel time by defining minimum cost path for each section of a trip.

Key Takeaways

In this chapter, we covered:

• What travel demand modeling is for and what the common methods are to do that.
• How FSM is structured sequentially, what the relationships between different steps are, and what the outputs are.
• What the advantages and disadvantages of different methods and assumptions in each step are.
• What certain data collection and preparation for trip generation and distribution are needed through a hypothetical example.

Prep/quiz/assessments

• What is the need for regular travel demand forecasting, and what are its two major components?
• Describe what data we require for each of the four steps.
• What are the advantages and disadvantages of regression and cross-classification methods for a trip generation?
• What is the most common modeling framework for mode choice, and what result will it provide us?
• What are the main limitations of FSM, and how can they be addressed? Describe the need for travel demand modeling in urban transportation and relate it to the structure of the four-step model (FSM).

Ahmed, B. (2012). The traditional four steps transportation modeling using a simplified transport network: A case study of Dhaka City, Bangladesh. International Journal of Advanced Scientific Engineering and Technological Research ,  1 (1), 19–40. https://discovery.ucl.ac.uk/id/eprint/1418961/

ALMEC, C . (2015). The Project for capacity development on transportation planning and database management in the republic of the Philippines: MMUTIS update and enhancement project (MUCEP) : Project Completion Report . Japan International Cooperation Agency. (JICA) Department of Transportation and Communications (DOTC) . https://books.google.com/books?id=VajqswEACAAJ .

Beimborn, E., and  Kennedy, R. (1996). Inside the black box: Making transportation models work for livable communities . Washington, DC: Citizens for a Better Environment and the Environmental Defense Fund. https://www.piercecountywa.gov/DocumentCenter/View/755/A-GuideToModeling?bidId

Ben-Akiva, M., & Lerman, S. R. (1974). Some estimation results of a simultaneous model of auto ownership and mode choice to work.  Transportation ,  3 (4), 357–376. https://doi.org/10.1007/bf00167966

Ewing, R., & Cervero, R. (2010). Travel and the built environment: A meta-analysis. Journal of the American Planning Association , 76 (3), 265–294. https://doi.org/10.1080/01944361003766766

Florian, M., Gaudry, M., & Lardinois, C. (1988). A two-dimensional framework for the understanding of transportation planning models.  Transportation Research Part B: Methodological ,  22 (6), 411–419. https://doi.org/10.1016/0191-2615(88)90022-7

Hadi, M., Ozen, H., & Shabanian, S. (2012).  Use of dynamic traffic assignment in FSUTMS in support of transportation planning in Florida.  Florida International University Lehman Center for Transportation Research. https://rosap.ntl.bts.gov/view/dot/24925

Hansen, W. (1959). How accessibility shapes land use.” Journal of the American Institute of Planners 25 (2): 73–76. https://doi.org/10.1080/01944365908978307

Gavu, E. K. (2010).  Network based indicators for prioritising the location of a new urban transport connection: Case study Istanbul, Turkey (Master’s thesis, University of Twente). International Institute for Geo-Information Science and Earth Observation Enschede. http://essay.utwente.nl/90752/1/Emmanuel%20Kofi%20Gavu-22239.pdf

Karner, A., London, J., Rowangould, D., & Manaugh, K. (2020). From transportation equity to transportation justice: Within, through, and beyond the state. Journal of Planning Literature , 35 (4), 440–459. https://doi.org/10.1177/0885412220927691

Kneebone, E., & Berube, A. (2013). Confronting suburban poverty in America . Brookings Institution Press.

Koppelman, Frank S, and Chandra Bhat. (2006). A self instructing course in mode choice modeling: multinomial and nested logit models. U.S. Department of Transportation Federal Transit Administration https://www.caee.utexas.edu/prof/bhat/COURSES/LM_Draft_060131Final-060630.pdf

‌Manheim, M. L. (1979).  Fundamentals of transportation systems analysis. Volume 1: Basic Concepts . The MIT Press https://mitpress.mit.edu/9780262632898/fundamentals-of-transportation-systems-analysis/

McNally, M. G. (2007). The four step model. In D. A. Hensher, & K. J. Button (Eds.), Handbook of transport modelling , Volume1 (pp.35–53). Bingley, UK: Emerald Publishing.

Meyer, M. D., & Institute Of Transportation Engineers. (2016).  Transportation planning handbook . Wiley.

Mladenovic, M., & Trifunovic, A. (2014). The shortcomings of the conventional four step travel demand forecasting process. Journal of Road and Traffic Engineering , 60 (1), 5–12.

Mitchell, R. B., and C. Rapkin. (1954). Urban traffic: A function of land use . Columbia University Press. https://doi.org/10.7312/mitc94522

Rahman, M. S. (2008). “ Understanding the linkages of travel behavior with socioeconomic characteristics and spatial Environments in Dhaka City and urban transport policy applications .” Hiroshima: (Master’s thesis, Hiroshima University.) Graduate School for International Development and Cooperation. http://sr-milan.tripod.com/Master_Thesis.pdf

Rodrigue, J., Comtois, C., & Slack, B. (2020). The geography of transport systems . London ; New York Routledge.

Shen, Q. (1998). Location characteristics of inner-city neighborhoods and employment accessibility of low-wage workers. Environment and Planning B: Planning and Design , 25 (3), 345–365.

Sharifiasl, S., Kharel, S., & Pan, Q. (2023). Incorporating job competition and matching to an indicator-based transportation equity analysis for auto and transit in Dallas-Fort Worth Area. Transportation Research Record , 03611981231167424. https://doi.org/10.1177/03611981231167424

Weiner, Edward. 1997. Urban transportation planning in the United States: An historical overview . US Department of Transportation. https://rosap.ntl.bts.gov/view/dot/13691

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Home-based other (or non-work) trips (HBO) are the trips that originates from home to destinations other than work like shopping or leisure.

gravity model is a type of accessibility measurement in which the employment in destination and population in the origin defines thee degree of accessibility between the two zones.

Impedance function is a function that convert travel costs (usually time or distance) to the level of difficulty of getting from one location to the other.

Transportation Land-Use Modeling & Policy Copyright © by Mavs Open Press. All Rights Reserved.

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How travel demand modeling gets a boost from on-demand traffic data analytics

What is travel demand modeling.

Travel demand models use current travel behavior to predict future travel patterns from a sample of travel behavior data. As you might expect, models are critical tools for planners and engineers who use them to forecast the transportation needs of the communities they serve. They also help transportation professionals assess the pros and cons of different options for meeting those needs.

Is Travel Demand Modeling the same thing as transportation modeling?

Travel demand models are a subset of transportation models that specifically focus on — you guessed it — travel demand. They are commonly developed within a region or, more broadly, across an entire state. The regional “demand” for trip-making, informed by land use and economic conditions, and the “supply” of the transportation network determine when, where, and how trips are made within a region. These existing travel conditions can be extrapolated to various future scenarios to predict the impacts of transportation policy and infrastructure improvements.

But other types of transportation models also use mobility data to predict future travel patterns based on current inputs. The scale and complexity of these models can vary significantly, depending on the data inputs available and the level of detail required from the model forecasts. Outputs from these models can inform decisions ranging from traffic signal timing at an intersection to congestion management policies on a corridor to multi-billion dollar highway or transit infrastructure investments.

Transportation models are a critical tool utilized by planners, engineers, and decision-makers to understand the performance of a transportation network today so that the impacts of future changes in demographics, land use, policy decisions, and infrastructure improvements can be forecasted for different future scenarios. For example, a microsimulation model may use existing travel behaviors to predict how localized roadway changes (such as work zones or detours) will impact weaving, congestion, speeding, and other traffic patterns.

Other transportation modeling use cases include:

Getting the right data for travel demand models

Travel demand models — and transportation models in general— are only as accurate as their input data. Without an accurate understanding of real-world conditions today, planners and engineers cannot predict future demand.

Traditional data collection methods like physical sensors and surveys help gather information about existing conditions, but they’re also expensive and time-consuming to implement . This means these methods must be targeted at high-priority locations and may cover only a brief time period, resulting in low sample sizes that limit the precision of travel demand models.

On-demand transportation analytics — or “big data” analytics — address these limitations by using data collected from Connected Devices and the Internet of Things (IoT) to generate larger and more granular datasets that cover any road or time period. When used alongside traditional data collection methods, they help fill data gaps and enhance the efficacy of travel demand models .

In this guide, we’ll explore how travel demand models are built and calibrated and how transportation analytics help modelers get more actionable insights from these models.

WEBINAR: Build effective travel demand models with data that reflects changing mobility

Creating travel demand models

There are many ways to build a model, but no matter what, building accurate travel demand models requires detailed information about:

• Where groups of people go
• The ways their travel behavior changes during specific conditions

Traditionally, travel demand models use a four-step process to analyze regional transportation planning:

• Trip generation (the number of trips to be made)
• Trip distribution (where those trips go)
• Mode choice (how the trips will be divided among the available modes of travel)
• Trip assignment (predicting the route trips will take)

The results obtained through this four-step process vary widely, since they depend on the quality of the assumptions and data used, as well as the particular model’s sophistication. Micro models , or “microsimulations,” for small areas usually give users an estimate of highway volumes for individual roadways or intersections, where as “macro” and “meso” models look at entire counties, regions, and even state and multi-state geographies.

In the following example, traffic engineers with WSP USA used Big Data to build and calibrate a microsimulation to understand post-COVID travel demand on I-75 in Phoenix, looking specifically at weaving during peak hours and traffic interchange with a nearby highway.

No matter their size, the most sophisticated models incorporate and analyze highly granular data, such as commercial truck activity, HOV lane usage, tolling behavior, and more. Route choice between the same two locations, for example, can vary dramatically depending on time of day and other factors that influence drivers.

To illustrate, let’s say that our friend Sarah needs to drive from her home in Pittsburgh’s east end to the downtown business district. Most of the time, she finds Penn Avenue to be the fastest and most enjoyable route, but she also knows it is foolish to take that route during rush hour, a sports game, a major concert, or a visit from the President.

Ultimately, accurate travel demand models require quality input data. Outdated or incomplete data results in imprecise models, and your predictions could fail to account for the decisions that drivers like Sarah make almost subconsciously.

Using Big Data for origin-destination matrices

One of the most important data inputs for any model is an origin-destination (“O-D”) matrix—data that tells you where people are coming from (origin) and where they are going (destination) after visiting or passing through a specific location. In the past, planners utilized surveys, a set of “gravity” assumptions, and/or license plate studies to create O-D matrices, which are all costly and typically have small sample sizes.

Transportation experts can now use on-demand traffic data to quickly create precise, accurate and comprehensive O-D matrices , using algorithmic techniques that analyze trillions of data points and organize them by location and time stamp.

When transportation experts have analytics that incorporate this many data points, they can create traditional O-D matrices that represent a far greater percentage of the population and a longer timespan than could be captured through surveys or license plate studies. Planners can also capture short trips more accurately, like a quick run into the grocery store while on the commute home. Those types of short trips tend to get undercounted or completely overlooked in a typical travel survey.

In a nutshell, trips and series of activities are created by:

• Identifying the pings that occur when Connected Vehicles and IoT devices begin moving (the origins);
• Following the series of pings that occur as these vehicles or devices move (the route); and
• Identifying the final pings when vehicles or devices come to rest (the destinations).

It’s important, however, to recognize that these data points are messy at the outset. No single person could manage trillions of data points using Microsoft Excel! That means sophisticated processing techniques are critical  to making these data sets manageable and effective for planning transportation projects.

Using on-demand analytics for these matrices helps deliver the fine resolution needed for accurate travel demand modeling. But that’s only the beginning. Trip data (such as the time stamps that identify home and work locations) can be combined with contextual data sets, such as parcel boundaries and aggregate demographic information from the Census. Now, your traditional O-D matrices can be analyzed in terms of trip purpose.

Inferring trip purpose

Trip purpose is also a key input for transportation modelers to consider. With Big Data, you can infer why groups of people travel from one location to another by analyzing aggregated behavior over a longer period. Big Data can help you estimate the portion of trips in a study that are:

• Home-Based Work: Travel between home and work in either direction
• Home-Based Other: Travel to and from the home, to anywhere other than work
• Non-Home Based: All travel not to or from home

For example, StreetLight InSight ® — that’s StreetLight Data’s interactive, online platform that aggregates and simplifies those trillions of data points — can provide the trip purposes for the purposes listed above for any O-D matrix in just a few minutes.

Planners can benefit from our Home-Work Trip Purpose Metrics because they no longer need to get this information from models or surveys, which are expensive to conduct and typically have low response rates. With comprehensive analytics derived from Big Data, planners can now gather spatially precise trip purpose information using real-world data. For example, when creating a traditional, four-step, Travel Demand Model, the Home-Work Trip Purpose Metric will be a huge asset.

Data requirements for dynamic traffic assignment

Another great modeling technique where one can use Big Data is Dynamic Traffic Assignment (DTA). DTA is better at modeling user response to issues such as peak spreading, freight analysis, and congestion at fine resolutions. But DTA is a meticulous modeling technique, so it requires detailed, rigorous data to be done right. The old approach requires collection from at least 6 different sources, and many of them are very cumbersome and expensive. It also requires a huge effort to integrate, calibrate, and check that data integration. The result is not only expensive and time consuming, but also has a lot of assumptions. It’s simply clunky and messy.

Working with Big Data, you can attack DTA with a direct, data-driven approach. Using fine-tuned origin-destination studies based on Big Data, modelers can uncover precise analytics such as how left-hand turns are affected by time of day and type of trip . Route choice can vary dramatically by time of day, so understanding the ways that behavior changes during specific conditions is critical to building accurate models.

This detailed modeling technique is particularly effective for modeling user response to issues such as peak spreading, freight analysis and congestion, in fine resolution. To be effective, however, DTA modeling requires detailed, rigorous data. When you use location data from mobile devices, you can get the information you need to create a DTA quickly and easily.

Stay ahead of fast-changing mobility trends with effective travel demand models

Big Data integration with other modeling tools

Beyond Big Data being a great source of information on its own, it can also be easily integrated into other modeling and simulation tools . CSV files of our Metrics can be downloaded directly from StreetLight InSight®, and then users can input that information into their preferred modeling tools . We have also developed easy-to-use transportation APIs , including the StreetLight InSight® API that can be used to integrate our analytics into many mapping and modeling tools.

Real-world case studies

Hundreds of transportation agencies are benefiting from using Big Data in travel demand models. It’s helping them understand travel behavior more comprehensively, precisely, and accurately. If your goals are to collect high-quality data cost-effectively, there is no better way to understand group travel patterns.

For example, when the City of Edmonton, Canada was unable to run its typically active annual traffic count program due to COVID, access to on-demand traffic data helped fill the gap and zero in on pandemic-driven shifts in travel behaviors, calibrating their model to these recent changes and allowing for more actionable insights to guide infrastructure projects and temporary traffic measures.

Similarly, in the video below, you’ll see how an AEC firm used on-demand analytics to update the Butte County Association of Governments (BCAG) travel demand model, using O-D analyses and Zone Activity analyses to understand interregional travel, paying special attention to key tourist and special event destinations.

Beyond travel demand modeling

As we’ve discussed, Big Data is an essential tool for understanding the ways behavior changes during specific conditions, and building accurate travel demand models. But there are a number of other applications that are transforming transportation.

Here are seven other ways to harness the power of Big Data in your planning and projects:

• Build climate-forward infrastructure and decarbonize transportation
• Diagnose and mitigate traffic congestion
• Improve mobility on high-priority corridors
• Win federal grant funding for infrastructure projects
• Eliminate traffic fatalities and make streets safer
• Build equity-first transportation systems

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Effectively manage operations by accessing on-demand visitation metrics that show when, how, and from where people travel to key sites..

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Travel Demand Forecasting: Parameters and Techniques (2012)

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N A T I O N A L C O O P E R A T I V E H I G H W A Y R E S E A R C H P R O G R A M NCHRP REPORT 716 Travel Demand Forecasting: Parameters and Techniques Cambridge Systematics, Inc. Cambridge, MA Vanasse Hangen Brustlin, Inc. Silver Spring, MD Gallop Corporation Rockville, MD Chandra R. Bhat Austin, TX Shapiro Transportation Consulting, LLC Silver Spring, MD Martin/Alexiou/Bryson, PLLC Raleigh, NC Subscriber Categories Highways • Operations and Traffic Management • Planning and Forecasting • Safety and Human Factors TRANSPORTAT ION RESEARCH BOARD WASHINGTON, D.C. 2012 www.TRB.org Research sponsored by the American Association of State Highway and Transportation Officials in cooperation with the Federal Highway Administration

NATIONAL COOPERATIVE HIGHWAY RESEARCH PROGRAM Systematic, well-designed research provides the most effective approach to the solution of many problems facing highway administrators and engineers. Often, highway problems are of local interest and can best be studied by highway departments individually or in cooperation with their state universities and others. However, the accelerating growth of highway transportation develops increasingly complex problems of wide interest to highway authorities. These problems are best studied through a coordinated program of cooperative research. In recognition of these needs, the highway administrators of the American Association of State Highway and Transportation Officials initiated in 1962 an objective national highway research program employing modern scientific techniques. This program is supported on a continuing basis by funds from participating member states of the Association and it receives the full cooperation and support of the Federal Highway Administration, United States Department of Transportation. The Transportation Research Board of the National Academies was requested by the Association to administer the research program because of the Board’s recognized objectivity and understanding of modern research practices. The Board is uniquely suited for this purpose as it maintains an extensive committee structure from which authorities on any highway transportation subject may be drawn; it possesses avenues of communications and cooperation with federal, state and local governmental agencies, universities, and industry; its relationship to the National Research Council is an insurance of objectivity; it maintains a full-time research correlation staff of specialists in highway transportation matters to bring the findings of research directly to those who are in a position to use them. The program is developed on the basis of research needs identified by chief administrators of the highway and transportation departments and by committees of AASHTO. Each year, specific areas of research needs to be included in the program are proposed to the National Research Council and the Board by the American Association of State Highway and Transportation Officials. Research projects to fulfill these needs are defined by the Board, and qualified research agencies are selected from those that have submitted proposals. Administration and surveillance of research contracts are the responsibilities of the National Research Council and the Transportation Research Board. The needs for highway research are many, and the National Cooperative Highway Research Program can make significant contributions to the solution of highway transportation problems of mutual concern to many responsible groups. The program, however, is intended to complement rather than to substitute for or duplicate other highway research programs. Published reports of the NATIONAL COOPERATIVE HIGHWAY RESEARCH PROGRAM are available from: Transportation Research Board Business Office 500 Fifth Street, NW Washington, DC 20001 and can be ordered through the Internet at: http://www.national-academies.org/trb/bookstore Printed in the United States of America NCHRP REPORT 716 Project 08-61 ISSN 0077-5614 ISBN 978-0-309-21400-1 Library of Congress Control Number 2012935156 © 2012 National Academy of Sciences. All rights reserved. COPYRIGHT INFORMATION Authors herein are responsible for the authenticity of their materials and for obtaining written permissions from publishers or persons who own the copyright to any previously published or copyrighted material used herein. Cooperative Research Programs (CRP) grants permission to reproduce material in this publication for classroom and not-for-profit purposes. 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The report was reviewed by the technical panel and accepted for publication according to procedures established and overseen by the Transportation Research Board and approved by the Governing Board of the National Research Council. The opinions and conclusions expressed or implied in this report are those of the researchers who performed the research and are not necessarily those of the Transportation Research Board, the National Research Council, or the program sponsors. The Transportation Research Board of the National Academies, the National Research Council, and the sponsors of the National Cooperative Highway Research Program do not endorse products or manufacturers. Trade or manufacturers’ names appear herein solely because they are considered essential to the object of the report.

The National Academy of Sciences is a private, nonprofit, self-perpetuating society of distinguished scholars engaged in scientific and engineering research, dedicated to the furtherance of science and technology and to their use for the general welfare. On the authority of the charter granted to it by the Congress in 1863, the Academy has a mandate that requires it to advise the federal government on scientific and technical matters. Dr. Ralph J. Cicerone is president of the National Academy of Sciences. The National Academy of Engineering was established in 1964, under the charter of the National Academy of Sciences, as a parallel organization of outstanding engineers. It is autonomous in its administration and in the selection of its members, sharing with the National Academy of Sciences the responsibility for advising the federal government. The National Academy of Engineering also sponsors engineering programs aimed at meeting national needs, encourages education and research, and recognizes the superior achievements of engineers. Dr. Charles M. Vest is president of the National Academy of Engineering. The Institute of Medicine was established in 1970 by the National Academy of Sciences to secure the services of eminent members of appropriate professions in the examination of policy matters pertaining to the health of the public. The Institute acts under the responsibility given to the National Academy of Sciences by its congressional charter to be an adviser to the federal government and, on its own initiative, to identify issues of medical care, research, and education. Dr. Harvey V. Fineberg is president of the Institute of Medicine. The National Research Council was organized by the National Academy of Sciences in 1916 to associate the broad community of science and technology with the Academy’s purposes of furthering knowledge and advising the federal government. Functioning in accordance with general policies determined by the Academy, the Council has become the principal operating agency of both the National Academy of Sciences and the National Academy of Engineering in providing services to the government, the public, and the scientific and engineering communities. The Council is administered jointly by both Academies and the Institute of Medicine. Dr. Ralph J. Cicerone and Dr. Charles M. Vest are chair and vice chair, respectively, of the National Research Council. The Transportation Research Board is one of six major divisions of the National Research Council. The mission of the Transporta- tion Research Board is to provide leadership in transportation innovation and progress through research and information exchange, conducted within a setting that is objective, interdisciplinary, and multimodal. The Board’s varied activities annually engage about 7,000 engineers, scientists, and other transportation researchers and practitioners from the public and private sectors and academia, all of whom contribute their expertise in the public interest. The program is supported by state transportation departments, federal agencies including the component administrations of the U.S. Department of Transportation, and other organizations and individu- als interested in the development of transportation. www.TRB.org www.national-academies.org

C O O P E R A T I V E R E S E A R C H P R O G R A M S AUTHOR ACKNOWLEDGMENTS The research project reported herein was performed under NCHRP Project 8-61 by Cambridge Systematics, Inc. in association with Dr. Chandra R. Bhat, Vanasse Hangen Brustlin, Inc., Martin/ Alexiou/Bryson, PLLC, Gallop Corporation, and Shapiro Transportation Consulting, LLC. Cambridge Systematics served as prime contractor. Thomas Rossi of Cambridge Systematics served as project director and principal investigator. Major roles in this project were also performed by David Kurth, John (Jay) Evans, Daniel Beagan, Bruce Spear, Robert Schiffer, and Ramesh Thammiraju of Cambridge Systematics; Dr. Chandra Bhat; Richard Roisman, formerly of Vanasse Hangen Brustlin; Philip Shapiro of Shapiro Transportation Consulting (formerly of Vanasse Hangen Brustlin); C.Y. Jeng of Gallop Corporation; William Martin of Martin/Alexiou/Bryson; and Amlan Banerjee and Yasasvi Popuri, formerly of Cambridge Systematics. A peer review panel provided valuable comments on the draft report. The research team wishes to thank the peer review panel members: Charles Baber of the Baltimore Metropolitan Council, Bart Benthul of the Bryan-College Station Metropolitan Planning Organization (MPO), Mike Conger of the Knoxville Regional Transportation Planning Organization, Ken Kaltenbach of the Corradino Group, Phil Matson of the Indian River MPO, Phil Mescher of the Iowa Department of Transportation, Jeremy Raw of the Federal Highway Administration, and David Schmitt of AECOM. The research team wishes to thank the following organizations and individuals for their assistance: Nancy McGuckin, Ron Milone of the Metropolitan Washington Council of Governments, and Jeffrey Agee-Aguayo of the Sheboygan MPO. CRP STAFF FOR NCHRP REPORT 716 Christopher W. Jenks, Director, Cooperative Research Programs Crawford F. Jencks, Deputy Director, Cooperative Research Programs Nanda Srinivasan, Senior Program Officer Charlotte Thomas, Senior Program Assistant Eileen P. Delaney, Director of Publications Natalie Barnes, Senior Editor NCHRP PROJECT 08-61 PANEL Field of Transportation Planning—Area of Forecasting Thomas J. Kane, Thomas J. Kane Consulting, Urbandale, IA (Chair) Michael S. Bruff, North Carolina DOT, Raleigh, NC Ed J. Christopher, Berwyn, IL Nathan S. Erlbaum, New York State DOT, Albany, NY Jerry D. Everett, University of Tennessee, Knoxville, TN Bruce Griesenbeck, Sacramento Area Council of Governments, Sacramento, CA Herbert S. Levinson, Wallingford, CT Richard H. Pratt, Richard H. Pratt, Consultant, Inc., Garrett Park, MD Bijan Sartipi, California DOT, Oakland, CA Shuming Yan, Washington State DOT, Seattle, WA Ken Cervenka, FTA Liaison Kimberly Fisher, TRB Liaison

F O R E W O R D This report is an update to NCHRP Report 365: Travel Estimation Techniques for Urban Planning and provides guidelines on travel demand forecasting procedures and their application for solving common transportation problems. The report presents a range of approaches that allow users to determine the level of detail and sophistication in select- ing modeling and analysis techniques most appropriate to their situations and addresses straight-forward techniques, optional use of default parameters, and appropriate references to other more sophisticated techniques. In 1978, TRB published NCHRP Report 187: Quick-Response Urban Travel Estimation Techniques and Transferable Parameters. This report described default parameters, factors, and manual techniques for doing simple planning analysis. The report and its default data were used widely by the transportation planning profession for almost 20 years. In 1998, drawing on several newer data sources including the 1990 Census and National Personal Household Travel Survey, an update to NCHRP Report 187 was published as NCHRP Report 365: Travel Estimation Techniques for Urban Planning. Since NCHRP Report 365 was published, significant changes have occurred affecting the complexity, scope, and context of transportation planning. Planning concerns have grown beyond “urban” to include rural, statewide, and special-use lands. Transportation planning tools have evolved and proliferated, enabling improved and more flexible analyses to support decisions. The demands on transportation planning have expanded into special populations (e.g., tribal, immigrant, older, and young) and broader issues (e.g., safety, congestion, pricing, air quality, environment, and freight). In addition, the default data and parameters in NCHRP Report 365 needed to be updated to reflect the planning requirements of today and the next 10 years. Thus, the objective of this research was to revise and update NCHRP Report 365 to reflect current travel characteristics and to provide guidance on travel demand forecasting procedures and their application for solving common transportation problems. The research was performed by Cambridge Systematics, Inc. in association with Vanasse Hangen Brustlin, Inc., Gallop Corporation, Dr. Chandra R. Bhat, Shapiro Transportation Consulting, LLC, and Martin/Alexiou/Bryson, PLLC. Information was gathered via liter- ature review, interviews with practitioners, and a database of parameters collected from metropolitan planning organizations as well as from the 2009 National Household Travel Survey. Planners can make use of the information presented in this report in two primary ways: (1) to develop travel model components when local data suitable for model develop- ment are insufficient or unavailable and (2) to check the reasonableness of model outputs. By Nanda Srinivasan Staff Officer Transportation Research Board

C O N T E N T S 1 Chapter 1 Introduction 1 1.1 Background 2 1.2 Travel Demand Forecasting: Trends and Issues 3 1.3 Overview of the Four-Step Travel Modeling Process 5 1.4 Summary of Techniques and Parameters 5 1.5 Model Validation and Reasonableness Checking 5 1.6 Advanced Travel Analysis Procedures 5 1.7 Case Study Applications 5 1.8 Glossary of Terms Used in This Report 7 Chapter 2 Planning Applications Context 7 2.1 Types of Planning Analyses 10 2.2 Urban Area Characteristics Affecting Planning and Modeling 14 Chapter 3 Data Needed for Modeling 14 3.1 Introduction 14 3.2 Socioeconomic Data and Transportation Analysis Zones 18 3.3 Network Data 24 3.4 Validation Data 27 Chapter 4 Model Components 27 4.1 Introduction 31 4.2 The Logit Model 33 4.3 Vehicle Availability 37 4.4 Trip Generation 43 4.5 Trip Distribution 48 4.6 External Travel 53 4.7 Mode Choice 58 4.8 Automobile Occupancy 62 4.9 Time of Day 65 4.10 Freight/Truck Modeling 72 4.11 Highway Assignment 77 4.12 Transit Assignment 80 Chapter 5 Model Validation and Reasonableness Checking 80 5.1 Introduction 80 5.2 Model Validation Overview 81 5.3 Model Validation and Reasonableness Checking Procedures for Existing Models 86 5.4 Model Validation and Reasonableness Checking Procedures for Models or Model Components Developed from Information Contained in Chapter 4 88 5.5 Cautions Regarding Use of This Report for Validation

89 Chapter 6 Emerging Modeling Practices 90 6.1 The Activity-Based Approach 92 6.2 Activity-Based Travel Model Systems in Practice 96 6.3 Integration with Other Model Systems 99 6.4 Summary 100 Chapter 7 Case Studies 100 7.1 Introduction 100 7.2 Model Reasonableness Check 108 7.3 Model Development Case Study for a Smaller Area without Data for Model Estimation 114 References A-1 Appendix A Federal Planning and Modeling Requirements B-1 Appendix B Review of Literature on Transferability Studies C-1 Appendix C Transferable Parameters Note: Many of the photographs, figures, and tables in this report have been converted from color to grayscale for printing. The electronic version of the report (posted on the Web at www.trb.org) retains the color versions.

TRB’s National Cooperative Highway Research Program (NCHRP) Report 716: Travel Demand Forecasting: Parameters and Techniques provides guidelines on travel demand forecasting procedures and their application for helping to solve common transportation problems.

The report presents a range of approaches that are designed to allow users to determine the level of detail and sophistication in selecting modeling and analysis techniques based on their situations. The report addresses techniques, optional use of default parameters, and includes references to other more sophisticated techniques.

Errata: Table C.4, Coefficients for Four U.S. Logit Vehicle Availability Models in the print and electronic versions of the publications of NCHRP Report 716 should be replaced with the revised Table C.4 .

NCHRP Report 716 is an update to NCHRP Report 365 : Travel Estimation Techniques for Urban Planning .

In January 2014 TRB released NCHRP Report 735 : Long-Distance and Rural Travel Transferable Parameters for Statewide Travel Forecasting Models , which supplements NCHRP Report 716.

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Home > Books > Models and Technologies for Smart, Sustainable and Safe Transportation Systems

Recent Progress in Activity-Based Travel Demand Modeling: Rising Data and Applicability

Submitted: 18 May 2020 Reviewed: 31 August 2020 Published: 17 September 2020

DOI: 10.5772/intechopen.93827

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Over 30 years have passed since activity-based travel demand models (ABMs) emerged to overcome the limitations of the preceding models which have dominated the field for over 50 years. Activity-based models are valuable tools for transportation planning and analysis, detailing the tour and mode-restricted nature of the household and individual travel choices. Nevertheless, no single approach has emerged as a dominant method, and research continues to improve ABM features to make them more accurate, robust, and practical. This paper describes the state of art and practice, including the ongoing ABM research covering both demand and supply considerations. Despite the substantial developments, ABM’s abilities in reflecting behavioral realism are still limited. Possible solutions to address this issue include increasing the inaccuracy of the primary data, improved integrity of ABMs across days of the week, and tackling the uncertainty via integrating demand and supply. Opportunities exist to test, the feasibility of spatial transferability of ABMs to new geographical contexts along with expanding the applicability of ABMs in transportation policy-making.

• activity-based models
• travel demand forecasting
• transportation planning
• transferability of transport demand models

Author Information

Atousa tajaddini *.

• Institute of Transport Studies, Monash University, Australia

Geoffrey Rose

Kara m. kockelman.

• The University of Texas, The United States of America

Hai L. Vu *

*Address all correspondence to: [email protected] and [email protected]

1. Introduction

In recent years, behaviorally oriented activity-based travel demand models (ABMs) have received much attention, and the significance of these models in the analysis of travel demand is well documented in the literature [ 1 , 2 ]. These models are found to be consistent and realistic in several fundamental aspects. They possess some significant advantages over the simple aggregated trip-based travel demand models [ 3 ]. To achieve this, ABMs consider the linkage among activities and travel for an individual as well as different people within the same household and place more attention to the constraints of time and space. In other words, these models are capable of integrating both the activity, time, and spatial dimensions. The comprehensive advantages of activity-based models in comparison to the trip-based models have been discussed in previous papers [ 4 , 5 , 6 , 7 , 8 ]. Activity-based models are suitable for a wider variety of transportation policies involving individual decisions such as congestion pricing and ridesharing. More especially, enabling the relationship between activity and behavioral pattern of trip making is one of the main reasons for the shift from the aggregate-level in trip based models to disaggregate-level provided by ABMs [ 9 ].

Activity-based travel demand models (ABMs) can be classified into two main groups: Utility maximization-based econometric models and rule-based computational process models (CPM). Utility maximization-based econometric models apply different econometric structures such as logit, probit, hazard-based, and ordered response models. While the logit models rely on different assumptions about the distribution of the error terms in the utility functions, hazard-based models use the duration of activity based on end-of-duration occurrence to generate activity schedules [ 10 ]. Rule-based computational process models apply different sets of condition-action rules and focus on the implementation of daily travel and ordering activities to mimic individuals’ behavior when constructing schedules. In addition to the aforementioned models, other approaches can be employed either in combination with these models or separately to develop activity-based models. Examples include agent-based and time-space prism approaches. While an agent-based approach allows agents to learn, modify, and improve their interactions with other agents as well as their dynamic environment, time-space prisms are utilized to capture spatial and temporal constraints under which individuals construct the patterns of their activities and trips. Figure 1 exhibits critical elements of ABM such as activity generation, activity scheduling, and mobility choices. It also provides a comparison among the notable existing travel demand models regarding their different elements. The development of activity-based travel demand models has been reviewed comprehensively in previous studies [ 10 , 11 ]. Table 1 provides a summary of the literature on the evolution of these models over time by introducing the notable existing developed models and highlighting their limitations.

Components of activity-based travel demand models.

ABM evolution over time.

Despite the existence of many models as listed in Table 1 , ABM’s abilities in reflecting behavioral realism are still limited [ 40 ]. The capability of ABM models in predicting individual travel movements can be evaluated from two perspectives of input (data) and output (applicability). Activity schedules are an essential input into the ABM model. From an input point of view, the necessity of deriving activity schedules from dynamic resources together with their challenges will be reviewed. From the applicability perspective, the application of ABM output in integration with dynamic traffic assignment (DTA) models, transferring to a new geographical context, and why and how it is applied in transport planning management will also be discussed. To this end, the first part of this paper will review the new real-time data resources revealing the pattern and traces of traveler’s mobility at a large scale and over an extended period of time. The big data enables new ABM models to reflect mobility behavior on an unprecedented level of detail while collecting data over a longer period (e.g., more than one typical day) would improve the behavioral realism in trip making [ 41 ]. The second part of this paper looks into the applicability of ABM models. This part includes (i) gap investigation in enriching ABMs by integrating time-dependent OD matrices produced by ABMs with dynamic traffic assignment; (ii) investigation of ABMs’ applicability in transferring from one region to another; and (iii) enriching the capability of ABMs by moving beyond the transportation domain to other such as environment and management strategies.

The remainder of the paper is organized as follows. Section 2 introduces new data sources such as mobile phone call data records, transit smart cards, and GPS data where the influence of new data sources on the planning of activities, formation, and analysis of the travel behavior of individuals will be investigated. This section also introduces activity-based travel demand models, which generates activity-travel schedules longer than a typical day. Section 3 describes the existing experiences in transferring utility-based and CPM activity-based travel demand models from one geographical area to another. This section also reviews the integration of ABM models with dynamic traffic assignment and other models such as air quality models. The possibility of using activity-based models in travel demand management strategies with a focus on car-sharing and telecommuting are considered as examples. The last section concludes the paper and identifies remaining challenges in the area of activity-based travel demand modeling.

2. ABMs and the emerging of big data

This section provides an overview of the role of big data in replacing the traditional data sources, and the changes in activity-based travel demand models given these newly available data.

2.1 Improvements in activity-based travel demand modeling

It is more than half a century that transportation planners try to understand how individuals schedule their activities and travel to improve urban mobility and accessibility. The evolution of travel demand modeling from trip-based to activity-based highlighted the need for high-resolution databases including sociodemographic and economic attributes of individuals and travel characteristics. Today, with the rapid advancements in computation, technology, and applications, the intelligent transportation systems (ITS) have revolutionized the analysis of travel behavior by having more accurate data, removing human errors, and making use of the vast amount of available data [ 42 ]. Tools such as GPS devices, smartphones, smart card data, and social networking sites all have the potential to track the movements and activities of individuals by recording and retaining the relevant data continuously over time. Most of the traditional travel survey data are rich in detail. However, it can result in biased travel demand models because of incomplete self-reports and inaccurate scheduling patterns. Therefore, in this section, the common tools used in collecting big data are introduced and the progress made in the area of extracting big data sources is discussed.

2.1.1 Cell phone data

A call detail record (CDR) is a data record produced by a telephone exchange and consists of spatiotemporal information on the recent system usage [ 40 ], which can track people’s movements. This CDR data can be processed and applied in activity-based travel demand modelings to better understand human mobility and obtain more accurate origin-destination (OD) tables [ 43 ]. The first attempt using CDR data was a study of Caceres et al. [ 44 ], who applied mobile phone data to generate OD matrices. Their concept was then formalized by Wang et al. [ 45 ] to obtain transient OD matrices by counting trips for each pair of the following calls from two different telephone (cell) towers at the same hour. Afterward, using the shortest path algorithm, OD trips are assigned to the road network. In the area of urban activity recognition, Farrahi et al. [ 46 ] applied two probabilistic methods (i.e., Latent Dirichlet Allocation (LDA) and Author Topic Models, ATM) to cluster CDR trajectories according to their temporal aspects to discover the home and work activities. Considering the spatial aspect of CDR data, Phithakkitnukoon et al. [ 47 ] applied auxiliary land use data and geographical information database to find possible activities around a certain cell tower. And considering both the temporal-spatial aspect of CDR, Widhalm et al. [ 48 ] used an undirected relational Markov network to infer urban activities. They extracted activity patterns for Boston and Vienna by analyzing cell phone data (activity time, duration, and land use). Their results show that trip sequence patterns and activity scheduling observed from datasets were compatible with city surveys as well as the stability of generated activity clusters across time. In a more recent study, [ 49 ] an unsupervised generative state-space model is applied to extract user activity patterns from CDR data. Furthermore, it has been shown that the method of CDR sampling is as significant as survey sampling. For example, in one study [ 50 ], CDR and survey data is used during a period of six months to investigate the daily mobility for Paris and Chicago. The result shows that 90% of travel patterns observed in both surveys are compatible with phone data. In another similar study [ 51 ], a probabilistic induction was proposed using motifs (daily mobility network), time of day activity sequence, and land use classification to produce activity types. CDR data of Singapore was used by Jiang et al. [ 52 ] to produce activity-based human mobility patterns.

In the context of activity-based transport modeling, Zilske et al. [ 53 ] replaced travel diaries with CDRs as input data for agent-based traffic simulation. They first generated the synthetic CDR data, then the MATSim simulation software was used to identify every observed person as an agent to convert call information into activity. They fused the CDR data set with traffic counts in their next paper [ 54 ], to reduce the Spatio-temporal uncertainty.

In summary, the findings reported from different studies indicated the major implications of mobile phone records on the estimation of travel demand variables including travel time, mode and route choice as well as OD demand and traffic flow estimation; however; in practice, the information generated from CDR data are yet to be used widely in simulation models. This is mainly because of the conflict between either level of resolution or format and completeness of model and data [ 55 ].

2.1.2 Smart card data

Smart card systems with on- and off-boarding information gained much popularity in large public transport systems all over the world, and have become a new source of data to understand and identify the Spatio-temporal travel patterns of the individual passengers. The smart card data are investigated in various studies such as activity identification, scheduling, agent-based transport models, and simulation [ 56 ]. Besides, in other studies [ 57 , 58 , 59 ] smart card data was used as an analysis tool in investigating the passenger movements, city structure, and city area functions. Similarly, in the recent study [ 60 ], a visual analysis system called PeopleVis was introduced to examine the smart card data (SCD) and predict the travel behavior of each passenger. They used one-week SCD in the city of Beijing and found a group of “familiar strangers” who did not know each other but had lots of similarities in their trip choices. Zhao et al. [ 61 ] also investigated the group behavior of metro passengers in Zhechen by applying the data mining procedure. After extracting patterns from smart card transaction data, statistical-based and clustering-based methods were applied to detect the passengers’ travel patterns. The results show that a temporally regular passenger is very probable to be a spatially regular passenger. The disaggregated nature of smart card data represents suitable input to multi-agent simulation frameworks. For example, the smart card data is used to generate activity plans and implement an agent-based microsimulation of public transport in two cities of Amsterdam and Rotterdam [ 62 ]. An agent-based transport simulation is developed for Singapore’s public transport using MATSim environment [ 63 ]. Unlike Bouman’s study, they considered the interaction of public transport with private vehicles. The study of Fourie et al. [ 64 ] was another research work to present the possibility of integrating big data algorithms with agent-based transport models. Zhu [ 65 ] compared one-week transaction data of smart cards in Shanghai and Singapore. They found feasibility in generating continuous transit use profiles for different types of cardholders. However, to have a better understanding of the patterns and activity behaviors, in addition to collecting the data from smart cards, one should integrate them with other data set.

2.1.3 GPS data

In travel demand modeling, it is important to have accurate and complete travel survey data including trip purpose, length, and companions, travel demand, origin and destination, and time of the day. Since the 1990s, the global positioning system (GPS) became popular for civil engineering applications, especially in the field of transportation as it provides a means of tracking some of the above variables. In the literature, methods of processing the GPS data and identifying activities can be classified according to different approaches such as rule-based and Bayesian model [ 66 ]; fuzzy logic [ 67 ]; multilayer perceptron [ 68 ]; and support vector machine learning [ 69 ]. Nevertheless, the disadvantages of using GPS data include the cost, sample size limitation, and the need to retrieve and distribute GPS devices to participate. Since smartphones are becoming one of the human accessories while equipped with a GPS module, they can be considered as a replacement of the GPS device to gather travel data. In this regard, CDR from smartphones is used [ 70 ] to estimate origin-destination matrices, or a smartphone-based application is used [ 71 ] to map the semiformal minibus services in Kampala (Uganda) and to count passenger boarding and alighting [ 72 ]. In the Netherlands, the Mobidot application is developed for analyzing the mobility patterns of individuals. To deduce travel directions and modes, this application uses the real-time data gathered by sensors of smartphones including GPS, accelerometer, and gyroscope sensors to compare them with existing databases [ 73 ].

Applying smartphones as a replacement of GPS however, holds several restrictions including the draining of smartphone battery and it is not possible to record travel mode and purpose.

2.1.4 Social media data

Today transport modelers, planners, and managers have started to benefit from the popularity of social networking data. There are different kinds of social media data such as Twitter, Instagram, and LinkedIn data, which consist of normal text, hash-tag (#), and check-in data. As hash-tag and check-in data are related to an activity, location or event, they can be used as meaningful resources in analysis of destination/origin of the activity [ 74 ]. According to the literature, social media has a great influence on different aspects of travel demand modeling [ 75 ]. Using social media instead of traditional data collection methods was investigated in different studies [ 76 ]. The way of processing these data to extract useful information is challenging as investigated in different studies [ 77 , 78 ]. Various studies [ 79 , 80 , 81 , 82 ] also examined social media data to understand the mobility behavior of a large group of people. Testing the possibility of evaluating the origin-destination matrix based on location-based social data was researched [ 83 ] or in another similar studies [ 84 , 85 ] where Twitter data was used to estimate OD matrices. The comparison between this new OD with the traditional values produced by the 4-step model proved the great potential of using social media data in modeling aggregate travel behavior. Social media data can be used in other areas such as destination choice modeling [ 86 ], recognizing activity [ 87 ], understanding the patterns of choosing activity [ 80 , 88 , 89 ], and interpreting life-style behaviors via studying activity-location choice patterns [ 90 ].

2.2 Dynamic ABM using a multi-day travel data set

Most existing travel demand modelers have applied the household survey data during the period of one day to construct activity schedules. However, longer periods such as one week or one month gained substantial importance during recent years. For simulating everyday travel behavior and generating schedules, a one-week period provides more comprehensive coverage because it includes weekdays and weekends and represents the weekly routines of individuals in making trips. Periods longer than one week can further provide detail on personal behavior as well as various usage of modes in different ways. So far only a few travel demand models covered a typical week as a studied period. For example rhythm in activity-travel behavior based on the capacity of one week was presented by applying a Kuhn-Tucker method [ 41 ]. Few works have been concentrating on the generation of multiple-day travel dataset. For example, by using large data and surveys, Medina developed two discrete choice models for generating multi-day travel activity types based on the likeliness of the activity [ 91 ]. a sampling method based on activity-travel pattern type clustering [ 92 ] was proposed to extract multi-day activity-travel data according to single-day household travel data. The results show similarities in distributions of intrapersonal variability in multi-day and single-day. MATSim is a popular agent-based simulation for ABM research [ 93 , 94 ], however, it is not appropriate for modeling the multi-day scenarios because MATSim uses the co-evolutionary algorithm to reach the user equilibrium which is a time consuming particularly for multi-day plans. To solve these problems, Ordonez [ 95 ] proposed a differentiation between fixed and flexible activities. Based on different time scales, Lee examined three levels of travel behavior dynamics, namely micro-dynamics (24 hours), macro-dynamics (lifelong travel behavior), meso-dynamics (weekly/monthly/yearly basis) by applying different statistical models [ 96 ]. A learning day-by-day module in another agent-based simulation software SimMobility is proposed [ 97 ]. Furthermore, ADAPTS is one of the few activity-based travel demand models which depends on activity planning horizon data for a longer period than one day, for example, one week or one month [ 33 ].

As highlighted by the above literature review, applying one-day observation data in travel demand modeling provides an inadequate basis of understanding of complex travel behavior to predict the impact of travel demand management strategies. So multi-day data are needed to refine this process. Previously, it was not easy to collect multi-day data, however, today thanks to advantages to technology it is possible to extract data from GPS, smartphones, smart cards, etc. with no burden for the respondent. Models built based on GPS data have been found to be more accurate and precise due to having fewer measurement errors. Collecting call detail records from mobile phones provide modelers with large trip samples and origin-destination matrices, while smart card data are more useful in terms of validation.

3. ABM transferability

We now turn to the recent advances and ongoing research in ABM focused on testing and enhancing geographical transferability and capacity to predict a broader range of impacts than flows and performance of the transport network.

3.1 ABM transferability from one geographical context to another

The spatial transferability of a travel demand model happens when the information or theory of a developed model of one region is applied to a new context [ 98 ]. Transferability can be used not only as a beneficial validation test for the models but also to save the cost and time required to develop a new model. Validation of a model by testing spatial transferability beside other various methods such as base-year and future-year data set is a test of validity which represents the capability of activity-based models in predicting travel behavior in a different context [ 99 ]. The exact theoretical basis and behavioral realism of activity-based travel demand model make them more appropriate for geographic transferability in comparison to traditional trip-based models [ 100 ]. Testing the transferability of ABM was first investigated by Arentze et al. [ 101 ]. They examined the possibility of transferring the ALBATROSS model at both individual and aggregate levels for two municipalities (Voorhout and Apeldoorn) in the Netherlands by simulating activity patterns. The results were satisfactory except for the transportation mode choice. In the United States, the CT-RAMP activity-based model which was developed for the MORPC region then transferred to Lake Tahoe [ 102 ]. In another study, one component of the ADAPTS model showed the potential for having good transferability properties [ 31 ]. The transferability of the DaySim model system developed for Sacramento to four regions in California and two other regions in Florida was investigated in [ 103 ]. The results show that the activity generation and scheduling models can be transferred better than mode and location choice models. The CEMDAP model developed for Dallas Fort Worth (DFW) region was transferred to the southern California region [ 104 ]. Outside the U.S., the TASHA model system developed for Toronto was transferred to London [ 105 ], and also in another study [ 106 ] the transferability of TASHA to the context of the Island of Montreal was assessed. Activity generation, activity location choice, and activity scheduling were three components of TASHA that transferred from Toronto to Montreal. In general, TASHA provided acceptable results at (macro and meso-level) for work and school activities even in some cases better results for Montreal in comparison to Toronto area. The possibility of developing a local area activity-based transport demand model for Berlin by transferring an activity generation model from another geographical area (Los Angeles) and applying the traffic counts of Berlin was investigated [ 107 ]. In their research, the CEMDAP model was applied to achieve a set of possible activity-travel plans, and the MATSim simulation was then used to generate a representative travel demand for the new region. The results were quite encouraging, however, the study indicated a need for further evaluation. In one recent study [ 108 ], an empirical method was used to check the transferability of ABMs between regions. According to their investigations, the most difficult problems with transferability caused by parameters of travel time, travel cost, land use, and logsum accessibilities. They suggested that in the transferability of the ABM from another region, agencies should be aware of finding a region within the same state or with similar urban density, or preferably both in order to improve the results. The possibility of transferring the FEATHERS model to Ho Chi Minh in Vietnam is investigated [ 109 ]. FEATHERS initially is developed for Flanders in Belgium. After calibration of FEATHERs sub-models, testing results using different indicators confirmed the success of transferring the FEATHER’s structure to the new context.

At the theoretical level, a perfect transferable model contributes to the transferability of its underlying behavioral theory, model structure, variable specification and coefficient to the new context. However, perfect transferability is not easy to achieve due to different policy and planning needs as well as the size of the regions, and the availability of data and other resources. Although the results of several transferred ABM model systems seem to have worked reasonably, it is equally important to assess how much accuracy is important in transferring models and how best and where to transfer models from.

3.2 ABM transferability to other non-transport domain

One of the advantages of the activity-based travel demand models over trip-based models is its capability to generate various performance indicators such as emission, health-related indicators, social exclusion, well-being, and quality of life indicators. Application of disaggregate models for the area of emission and air quality analysis was introduced by Shiftan [ 110 ] who investigated the Portland activity-based model in comparison to trip-based models. In another study [ 111 ], the same author integrated the Portland activity-based model with MOBILE5 emission model to study the effects of travel demand techniques on air quality. Regarding the integration of ABM with the emission model, the Albatross ABM model was coupled with MIMOSA (macroscopic emission model) [ 112 ] considering the usage of fuel and the amount of produced emission as a function of travel speed. A study in [ 113 ] added one dispersion model (AUROTA) to the previous integration of Albatross and MIMOSA to predict the hourly ambient pollutant. Albatross linked with a probabilistic air quality system was employed [ 114 ] in air quality assessment study. TASHA was another activity-based model, which has been extensively employed in air quality studies. For example, this model was integrated [ 29 , 115 ] with MOBILE6.2 to quantify vehicle emissions in Toronto. In their study, EMME/2 was used in the traffic assignment part. The previous research was improved [ 116 ] by replacing EMME/2 with MATSim as an agent-based DTA model. This TASHA-MATSim chain was used in the research [ 117 ] with the integration of MOBILE6.2C (emission model) and CALPUFF (dispersion model). OpenAMOS linked with MOVES emission model [ 118 ], and ADAPTS linked with MOVES [ 119 ] together with Sacramento ABM model [ 120 ] are among recent studies which represented the application of activity-based models in analyzing the impacts of vehicular emissions.

Human well-being and personal satisfaction play an important role in social progression [ 121 ]. To understand the theory behind human happiness, transport policies concentrated on the concept of utility as a tool to increase activity, goods, and services [ 122 , 123 ]. The issue of well-being as a policy objective is addressed in the literature and measured through various indicators, which show personal satisfaction and growth. For example, in the study by Hensher and Metz [ 124 , 125 ], saving time which leads to engagement in more activities was introduced as one of the benefits of measuring transport performance. Spatial accessibility was another benefit of travel that provides a range of activities that can be reasonably reached by individuals [ 126 ]. A dynamic ordinal logit model was developed [ 127 ] based on the collected data on happiness for a single activity in Melbourne. The authors found different activity types, which have different influences on the happiness that each individual experienced. Well-being can be integrated into activity-based models based on random utility theory. In terms of modeling, a framework was introduced [ 122 ] considering well-being data to improve activity-based travel demand models. According to their hypothesis, well-being is the final aim of activity patterns. They applied a random utility framework and considered well-being measures as indicators of the utility of activity patterns, and planned to test their framework empirically by adding well-being measurement equations to the DRCOG’s activity-based model.

The above literature review showed the importance of applying traffic models to generate input data to other models such as the air quality model. The accuracy of emission models is highly dependent on the level of detail in transport demand model inputs. Activity-based and agent-based models are supposed to describe reality more accurately by providing more detailed traffic data. Beyond measurement of air quality, well-being and health have drawn increasing attention. The health impact of changes in travel behavior, health inequalities, and social justice can be assessed within the activity-based platform [ 128 ]. With the help of geospatial data acquisition technologies like GPS, behavioral information with health data can be integrated into the development of an activity-based model to provide policies that affect the balance of transport and well-being.

3.3 ABM integration with dynamic traffic assignment

In parallel with the travel demand modeling, on the supply side, the conventional supply models used to be static, which import constant origin-destination flows as an input and produce static congestion patterns as an output. Consequently, these models were unable to represent the flow dynamics in a clear and detailed manner. Dynamic traffic assignment (DTA) models have emerged to address this issue and are capable of capturing the variability of traffic conditions throughout the day. It is evident that the shift of analysis from trips to activities in the demand modeling, as well as, the substitution of the static traffic assignment with dynamic traffic assignment in the supply side, can provide more realistic results in the planning process. Furthermore, the combination of ABM and DTA can better represent the interactions between human activity, their scheduling decision, and the underlying congested networks. Nevertheless, according to the study of [ 11 ], the integration of ABM with DTA received little attention and still requires further theoretical development. There are different approaches to the integration of ABM and DTA, which started with a sequential integration. In this type of integration, exchanging data between two major model components (ABM and DTA) happens at the end of the full iteration, to generate daily activity patterns for all synthetic population in an area of study, the activity-based model is run for the whole period of a complete day. The outputs of the ABM model which are lists of activities and plans are then fed into the DTA model. The DTA model generates a new set of time-dependent skim matrices as inputs to ABM for the next iteration. This process is continued until the convergence will be reached in the OD matrices output. Model systems applying the sequential integration paradigm can be found in most of the studies in the literature. For example, Castiglione [ 129 ] integrated DaySim which is an activity-based travel demand model developed for Sacramento with a disaggregate dynamic network traffic assignment tool TRANSIMS router. Bekhor [ 130 ] investigated the possibility of coupling the Tel Aviv activity-based model with MATSim as an agent-based dynamic assignment framework. Hao [ 116 ] integrated the TASHA model with MATSim. Ziemke [ 107 ] integrated CEMDAP, which is an activity-based model with MATSim to check the possibility of transferring an activity-based model from one geographic region to another. Lin [ 131 ] introduced the fixed-point formulation of integrated CEMDAP as an activity-based model with an Interactive System for Transport Algorithms (VISTA). Based on the mathematical algorithm of household activity pattern problem (HAPP), ABM and DTA were integrated [ 132 ] by presenting the dynamic activity-travel assignment model (DATA) which is an integrated formulation in the multi-state super network framework.

In the sequential integration, the ABM and DTA models run separately until they reach convergence. At the end of an iteration, these models perform data exchange before iterate again. Therefore, this kind of integrated framework cannot react quickly and positively to network dynamics and is unable to adapt to real-time information available to each traveler. In addressing this limitation, integrated models that adopt a much tighter integration framework have been developed recently. This approach is quite similar to the sequential approach, however; the resolution of time for ABM simulation is one minute rather than 24 hours (complete day). Relating to this level of dynamic integration, Pendyala [ 133 ] investigated the possibility of integrating OpenAMOS which is an activity-travel demand model with DTA tool name MALTA (Multiresolution Assignment and loading of traffic activities) with appropriate feedback to the land-use model system. For increasing the level of dynamic integration of ABM and DTA models, dynamic integration having pre-trip enroute information with full activity-travel choice adjustments has been introduced. In this level of ABM & DTA integration, it is assumed that pre-trip information is available for travelers about the condition of the network. It means that travelers are capable of adjusting activity-travel choices since they have access to pre-trip and Enroute travel information. Another tightly integrated modeling framework was proposed in [ 134 ] to integrate ABM (openAMOS) and DTA (DTALite) to capture activity-travel demand and traffic dynamics in an on-line environment. This model is capable of providing an estimation of traffic management strategies and real-time traveler information provision. Zockaie et al. [ 135 ] presented a simulation framework to integrate the relevant elements of an activity-based model with a dynamic traffic assignment to predict the operational impacts related to congestion pricing policies. Auld et al. [ 38 ] developed an agent-based modeling framework (POLARIS) which integrates dynamic simulation of travel demand, network supply, and network operations to solve the difficulty of integrating dynamic traffic assignment, and disaggregate demand models. A summary of the current literature on ABM and DTA integration is presented in Table 2 .

A summary of the empirical literature on ABM and DTA integration.

The above discussion illustrates that most of the model integration platforms between ABM + DTA work based on sequential integration. This loose coupling platform is the most straightforward and popular approach albeit is not responsive to network short-term dynamics and real-time information. Efforts to develop a comprehensive simulation model that can account for all components of dynamic mobility and management strategies continue. Further developments will have to deal with the implementation of an integrated ABM + DTA platform on a large network to support decision-makers, focus on the integration between activity-based demand models and multimodal assignment [ 143 ] as well as reducing computational efforts via better data exchange procedure and improving model communication efficiency. Defining practical convergence criteria is another issue which needs further investigations. Fully realistic convergence is normally never happened in sequential integration due to applying a pre-defined number of feedback loops in order to save model runtime.

3.4 ABM and travel demand management applications

Travel demand management (TDM) strategies are implemented to increase the efficiency of the transportation system and reduce traffic-related emissions. Some examples include mode shift strategies (encouraging people to use public transport) [ 144 ], time shift (to ride in off-peak hours, congestion pricing), and travel demand reduction [ 145 ] (using shared mobility service or teleworking). Shared transport services including car sharing, bike sharing, and ridesharing have been implemented in most of the transport planning systems across the world. Applying activity-based travel demand models to study the optimal fleet size can be found in different studies in the literature [ 146 , 147 ]. Parking price policies and their impacts on car sharing were investigated using MATSim in [ 148 ]. Results show shared vehicles use more efficient parking spaces in comparison to private vehicles. In the first attempt to model car sharing on more than one typical day [ 149 ] the agent-based simulation (mobitopp) was extended with a car-sharing option to study the travel behavior of the population in the city of Stuttgart in one week. In the recent study of [ 150 ], car sharing was integrated into an activity-based dynamic user equilibrium model to show the interaction between the demand and supply of car sharing. Among all the TDM strategies, telecommuting can be implemented in a shorter time [ 151 , 152 , 153 ]. The results of these studies present a reduction in vehicle-kilometers-traveled (VKT) during peak hours mainly because telecommuters change their trip timetable during these times. This plan rescheduling is also investigated and addressed in different studies [ 154 ] based on the statistical analysis of worker’s decisions about choice and frequency of telecommuting. While the plan rescheduling leads to reducing commute travel, the overall impacts of telecommuting on the formation of worker’s daily activity-travel behavior is challenging. For example, this policy reduced total distance traveled by 75% on telecommuting days while telecommuting could reduce the total commute distance up to 0.8% and 0.7% respectively [ 151 , 155 ]. Based on the adoption and frequency of telecommuting, a joint discrete choice model of home-based commuting was developed for New York city using the revealed preference (RP) survey [ 156 ]. Their results show a powerful relationship among individuals’ attributes, households’ demographics, and work-related factors, and telecommuting adoption and frequency decisions. A similar study [ 157 ] estimated the telecommuting choice and frequency by using a binary choice model and ordered-response model respectively. In terms of using activity-based modeling, [ 158 ] POLARIS activity-based framework was applied to research telecommuting adoption behavior and apply MOVES emission simulator model to assess the consequences of implementing this policy on air quality. Their results show that considering 50% of workers in Chicago with flexible working time hours in comparison to the base case with 12% flexible time hour workers, telecommuting can reduce Vehicle Mile Traveled (VMT) and Vehicle Hour Traveled (VHT) by 0.69% and 2.09% respectively. This policy reduces greenhouse gas by up to 0.71% as well. Pirdavani et al. [ 159 ] investigated the impact of two TDM scenarios (increasing fuel price and considering teleworking) on traffic safety. In this work, FEATHERS model, which is an activity-based model, was applied to produce exposure matrices to have a more reliable assessment. The results show the positive impacts of two scenarios on safety ( Figure 2 ).

Travel demand management policies within the activity-based platform.

The above section explores the relationship between transport demand management policies and travel behavior in the ABM context. The use of an activity-based travel demand model provides flexibility to employ a range of policy scenarios, and at the same time, the results are as detailed as possible to obtain the impact of policies on a disaggregated level. The finding highlights the importance of implementing different transportation policies management together to reach the most appropriate effect in terms of improving sustainability and the environment. The discussion emphasizes the need for considering more comprehensive transportation and environmental policies concerning sustainability to tackle travel planning in light of the increasingly diverse and complex travel patterns.

4. Summary and research directions

The use of activity-based models to capture complex underlying human’s travel behavior is growing. In this paper, we began by introducing the components of activity-based models and the evolution of the existing developed ABM models. In the first part of this paper, the new resources of data for travel demand analysis were introduced. In the new era of travel demand modeling, we need to deal with a dynamic, large sample, time-series data provided from new devices, and as a result manage observation covering days, weeks, and even months. The outcome of the recent works revealed that since activity-based models originated from the concept of individual travel patterns rather than aggregate flows, they highly suited to these new big data sources. These big datasets, which document human movements, include the information about mobility traces and activities carried out. Based on the in-depth and critical review of the literature, it is clear that while these big datasets provide detailed insight into travel behavior, challenges remain in extracting the right information and appropriately integrating them into the travel demand models. In particular, extracting personal characteristics and trip information like trip purpose and mode of transport are still open problems as these big data resources which provide space-time traces of trip-maker behaviors. Research works along these lines have been started as it was reviewed in the first part; however, further researches should be conducted to handle the uncertainty of big data mobility traces in the modeling process. Also, new methods should be investigated to validate the results for each step of the data analysis and mining. The possibility of fusing data from different available datasets needs further investigation. For instance, to understand the mode inference both data from the smart card and CDRs can be analyzed simultaneously. Another challenging issue regarding the application of this rich new data in transport modeling is that the need for methodologies to extract useful information needed regarding the traveler’s in-home and out-of-home activity patterns, which highlights the combination of data science, soft computing-based approaches, and transport research methods. It requires new Different algorithms such as statistical, genetic, evolutionary, and fuzzy as well as different techniques including advanced text and data mining, natural language processing, and machine learning.

The spatial transferability of activity-based travel demand models remains an important issue. Generally, it is found that the transferability of these models is more feasible than trip-based models, especially between two different regions with similar density or even between two areas in the same state. To date, most of the transferability research in activity-based travel demand modeling is motivated by a desire to save time, and very few studies that applied spatial transferability of activity-based models have undertaken rigorous validation of the results. While literature showed successful model transferability in terms of transferring activity/tour generation, time-of-day choice components, more studies are required on the model transferability regarding mode and location choice models as well as the validation test of activity-based models in different levels, i.e., micro, meso, and macro models.

As part of the second section of this study, this paper reviewed the progress made in the integration of activity-based models with dynamic traffic assignment.

Based on the literature, although evolution has occurred in DTA models, the loose coupling (sequential method) between ABM and DATA models still dominate the field. Two main challenges remain, namely poor convergence quality and excessively long run time. Replacing MATSim as a dynamic traffic assignment tool with other route assignment algorithms in recent years was a technical solution to loose coupling, which considered route choice as another facet of a multi-dimensional choice problem. MATSim provides not only an integration between the demand and supply side, but it can also act as a stand-alone agent-based modeling framework. However; MATSim potential drawbacks include being based on unrealistic assumptions of utility maximization and perfect information. To remove these unrealistic rational behavioral assumptions, applying other approaches such as a new innovative method of behavioral user equilibrium (BUE) is needed. This method helps trip-makers to reach certain utility-level rather than maximize the utility of their trip making [ 160 ]. Work along this approach has started (e.g., [ 161 ]).

The capability of activity-based models in generating other kinds of performance indicators in addition to OD matrices was also reviewed. Literature proved activity-based models generate more detailed results as inputs to air quality models, however; error rises from the accuracy of the information has a relevant impact on the process of integration. So it is necessary to do a comprehensive analysis of the uncertainties in traffic data. Literature proved that despite of the improvements in such disaggregate frameworks and the capability of these models in replicating policy sensitive simulation environment; there is yet to develop the best and perfect traffic-emission-air quality model. While the issue of health has drawn extensive attention from many fields, activity-based travel demand models have proved to have the potential to be used in estimating health-related indicators such as well-being. However, very few studies have been found to investigate the theories required to extend the random utility model based on happiness. While it is proved that mobility and environment have direct impacts on transport-related health [ 162 ], investigations on how travel mode preferences and air pollution exposure are related in this context are needed. Another area of research within ABM platform which is yet to be studied is the relationship between individual exposure to air pollution and mobility, especially in space, and time.

In the last part of this paper, the capability of activity-based models in the analysis of traffic demand management was investigated. Generally, the influence of telecommuting on both travel demand and network operation is still incomplete. Very few studies were found in which activity-based framework is used to simulate the potential impacts of telecommuting on traffic congestion and network operation where the real power of activity-based models lie.

In conclusion, while there are still open problems in activity-based travel demand models, there has been a lot of progress being made which is evidenced by the various recent and on-going researches reviewed in this paper. The review showed that by applying different methodologies in the modeling of different aspects of activity-based models, these models are becoming more developed, robust, and practical and become an inevitable tool for transport practitioners, city planners, and policy decision-makers alike.

Acknowledgments

The research work presented in this paper was supported by the Australian Government-Department of Education under Research Training Program (RTP Stipend) award.

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Activity-Based Travel Demand Models: A Primer

This publication is a guide for practitioners that describes activity-based travel demand model concepts and the practical considerations associated with implementing them. Activity-based travel demand models portray how people plan and schedule their daily travel. This type of model more closely replicates actual traveler decisions than traditional travel demand models and thus may provide better forecasts of future travel patterns. The guide is composed of two parts. Part 1 is intended to help managers, planners, and hands-on practitioners and modelers make informed decisions about activity-based model development and application. Part 2 examines the practical issues that transportation agencies face in migrating from traditional to “advanced” travel demand models, in which activity-based models are linked with regional-scale dynamic network assignments.

• Record URL: http://www.trb.org/Main/Blurbs/170963.aspx
• Find a library where document is available. Order URL: http://www.trb.org/Main/Blurbs/170963.aspx
• Castiglione, Joe
• Bradley, Mark
• Gliebe, John
• Publication Date: 2015
• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: 181p
• SHRP 2 Report
• Issue Number: S2-C46-RR-1
• Publisher: Transportation Research Board

Subject/Index Terms

• TRT Terms: Activity choices ; Dynamic traffic assignment ; Forecasting ; Travel demand ; Travel patterns
• Identifier Terms: Strategic Highway Research Program 2
• Uncontrolled Terms: Activity based modeling
• Subject Areas: Highways; Pedestrians and Bicyclists; Planning and Forecasting; Public Transportation; I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01530554
• Record Type: Publication
• ISBN: 9780309273992
• Report/Paper Numbers: SHRP 2 Report S2-C46-RR-1
• Files: TRIS, TRB, ATRI
• Created Date: Jul 18 2014 2:48PM

Rock Creek Analytics

Prediction is difficult, especially about the future!

Travel Demand Modeling

This note provides an overview of travel demand modeling.

The overall methodology for application of a modern transport model typically consists of:

• Establishing a basis for categorizing (dividing) travel demand by passenger and cargo travel and by trip purpose and cargo type;
• Recognizing the key underlying socio-economic variables that are the causes (generators) of travel demand;
• Identifying the types and sources of data that will be needed for systematic analysis of the existing travel demand and for projection of future travel demand.
• Collecting, processing and tabulating such data;
• Analyzing the relationships of travel demand to its underlying causes;
• Developing and calibrating traffic projection methods and models; and
• Completing traffic projections for alternative scenarios and transport network expansion and improvement concepts.

Using information obtained from existing sources together with the information collected from surveys, the traffic/socio-economic data is analyzed to determine how to predict the essential characteristics of travel demand and travel patterns in the study area.  With respect to the investigation of travel demand, these analyses seek to determine the essential factors and relationships affecting the major components of travel behavior.

Travel demand modeling entails a number of steps and processes.  The most important of these are described briefly in the following sections.  Details of these and other additional areas can be found in any standard transportation planning book such as one by J. de D. Ortúzar and L.G. Willumsen ( Modelling Transport , John Wiley and Sons, Second Edition 1995.)

Trip purposes

Because travel behavior depends on trip purpose (travel to work, education, shopping or other trip purpose site), an initial step in design of a travel demand modeling process will be the selection of the number and types of trip purposes to be separately modeled.  Typically, the trip purposes of personal travel which are separately modeled are: home based – work trips, home based – education trips, home based – other purpose trips, and non-home based trips.

Another consideration in the analysis of travel demand is that the total trips made within the study area can include:

• Trips with both trip ends (trip origin and trip destination) within the limits of the study area and trips having one or both trip ends located outside the limits of the study area; and
• Trips made by study area residents and trips made by visitors to the study area.

Trip generation analyses

For each trip purpose, the trip generation analyses will be to determine the best method for predicting the production and attraction of trips for given zonal land-use and population characteristics.  The methods for determining trip generation rates to be selected as the best methods may be obtained from either: (a) regression analysis or (b) cross-classification techniques.  Trip generation rates could be developed from regression analysis of the correlation of trip productions within zones to the zones’ average household population and socio-economic characteristics.  Another set of regressions are done to correlate the trip attractions to zones’ total number of jobs by type or other zonal variables that are logically and statistically related to trip attractions.

The cross-classification technique, which provides a preferable (disaggregated) method for predicting trip generation, involves the stratification of all households within zones into household groups having similar characteristics and developing trip generation rates for each household group.  For the (non-home) attraction end of personal trips, the consultant will investigate and, if practical and appropriate, develop and apply trip attraction rates related to specific types and intensities of land use.  For example, trip attraction rates could be related to such land-use measures as square meters of land-use, square meters of floor space, number of employees, or other measures of land-use that can be logically and statistically related to trip generation.

Trip distribution analysis

Two basic types of trip distribution models are usually applied to predict the future distribution (origins and destinations) of trips: (a) a gravity model or (b) an average growth factor method.  The gravity model is applicable in cases where data from a home interview survey is available for calibration of such a model.  In the absence of home interview survey data, an average growth factor method (such as the Fratar model) can be used to project an existing base year trip table to a future year by applying traffic growth factors (for each traffic analysis zone) for the production and attraction of trips.

Modal choice analysis

Mode choice analysis concerns with the relative usage of the available alternative modes of travel by the person making trips for the different trip purposes.  Alternative modes of travel that can be included in the modal choice analysis are: private passenger car driver, motorcycle driver, riding with someone else in passenger car or motorcycle, metro/LRT, public bus passenger by type of bus (large, mini-bus), private bus passenger, taxi passenger, walking, and other modes of travel.

The Consultant should explain and discuss the pros and cons for addressing modal choice for the different trip purposes and at alternative stages within the overall modeling process.  Mode choice procedures could be applied: (1) within the trip generation model, (2) before or after trip distribution, or (3) possibly within the traffic assignment process.

The mode choice models used in practice are generally based on the logit formulations.  Logit models have gained widespread acceptability in modeling travel choices as faced by travelers in the region.  Such models takes as inputs the level of service characteristics (such as travel time, travel cost etc.) and calculates the respective shares of each of the competing modes (i.e., auto, bus, light rail, etc.)

The simplest form of the logit model can be expressed as:

Share k = exp(U k ) / ∑ i exp(U i )

• U k is the utility of option k , computed as ∑ j b j X jk
• b j is the estimated parameter for the explanatory variable j
• X jk is the value of explanatory variable j for option k .

More advanced forms of logit models include the nested logit formulations.  The nested logit models address some of the shortcomings of the simple multinomial logit models and are theoretically superior.  The nested logit is a generalized logit model where the groups of modes ( nests ) with similar characteristics perceive the competition differently than the modes within the same group.  Nested logit models allow grouping of similar options in hierarchies or nests.  With these models it is possible to represent the intermodal competition much better and the groupings of alternatives indicate the cross-elasticity among the alternatives.  Alternatives in a common nest show the same degree of increased sensitivity compared to alternatives not in the nest.

Traffic assignment

Essential features for the traffic assignment process  include:

• Capabilities for network link assignments for Annual Average Daily Traffic (AADT) volumes as well as peak hour and by direction of travel,
• Assignment of person trips as well as vehicle and equivalent Passenger Car Unit (PCU) trips,
• Intersection turning movements, delays, queue lengths, etc.,
• Alternative assignments techniques including all-or-nothing, capacity restrained assignments, equilibrium, multi-path assignments, etc.; and
• Transit assignments.

The determination of zone-to-zone network travel impedance (usually travel time) is an important feature of any of the available traffic assignment techniques.  Within such traffic assignment models, the determination of zone-to-zone travel times requires the ability to simulate the operation of all network links and their junctions under the possible range of traffic loading to which they can be subjected.  Within traffic assignment model, this is accomplished by the use of speed-volume curves (one for each unique link or intersection design class) which predict the operating speed associated with traffic loading expressed in volume-capacity ratio.  For this reason, the appropriate design class for each link and junction in the highway network must be determined and, in the road network inventories, speed-volume curves defined for each of the link and junction classes.  This work will involve sophisticated use of the road inventory information and analysis of the travel time and delay survey results.

Calibration of the traffic assignment model will involve assignment of the base year trip tables to network describing the existing study area transport network.  Then a comparison is made to determine how closely the assignment results match the actual traffic at pre-determined screen and/or cordon lines.  The hierarchical priority for checking the model will be to compare the model’s predictions for total vehicle-kilometers of network travel; vehicles per day crossing screen line and cordon locations; vehicle-kilometers of travel in major corridors; and finally, individual network links.

Model Calibration and Validation

The transport modeling system will be calibrated so that the measures of travel demand and traffic flow (i.e., AADT, seasonal variation, daily variation, morning and evening flows) are accurately predicted for the base year situation with respect to land use, transport network characteristics and travel demand.

After completing the analyses and calibrating each significant component of the transport model, all components will be integrated into an overall operational model.  This overall model will then be run through all stages of modeling process in order to check how well the model is capable of replicating the existing network travel and operating characteristics.  This process of model calibration includes:

• Making consistency checks of principal components of the model to ensure that they are capable of producing accurate estimates of the current traffic levels and patterns and network performance measures for the study area (accomplished by comparing the traffic predictions of the model with the actual observed base year traffic usage of the highway/public transit network);
• Applying forecasts of socio-economic and land-use patterns to the model in order to have the model produce forecasts of future network travel; and
• Using the completed and calibrated model to make assessments of traffic and transportation impacts resulting from the range of possible future development scenarios for the study area.

After assuring that all elements of the model are properly calibrated, the model will then be applied to the task of predicting how the transport network will perform (using these same measures of transport network operation) for the base year.  This exercise, known as model validation , involves comparing the model output with observed data at screen-lines and cordon-lines; comparison of traffic volumes; and statistical analysis of assigned versus counted link volumes by volume group.

Land-use and Socio-economic Forecasts

The most important inputs to the models for forecasting traffic demand are the future land use and socio-economic patterns in the study area.  It is therefore essential that the land-use and socio-economic forecasts be done using standard and reliable econometric methodologies.  The land-use and socio-economic variables that are required for trip-generation models include:

• Type of activity (residential, commercial, industrial, etc.)
• Density of activity (residential or commercial space per square km, number of employees per sq. m. of built space, etc.)
• Employment by type
• Household size
• Automobile ownership

The data required for such forecasts are generally gathered from different government agencies (such as the Census Bureau, housing and urban development agencies, etc.).

Traffic Forecasts

Traffic forecasts are prepared using the traffic modeling system and the forecasts of socio-economic variables along with the likely conditions on the transport network of the study area.

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Title: long-term forecasts of statewide travel demand patterns using large-scale mobile phone gps data: a case study of indiana.

Abstract: The growth in availability of large-scale GPS mobility data from mobile devices has the potential to aid traditional travel demand models (TDMs) such as the four-step planning model, but those processing methods are not commonly used in practice. In this study, we show the application of trip generation and trip distribution modeling using GPS data from smartphones in the state of Indiana. This involves extracting trip segments from the data and inferring the phone users' home locations, adjusting for data representativeness, and using a data-driven travel time-based cost function for the trip distribution model. The trip generation and interchange patterns in the state are modeled for 2025, 2035, and 2045. Employment sectors like industry and retail are observed to influence trip making behavior more than other sectors. The travel growth is predicted to be mostly concentrated in the suburban regions, with a small decline in the urban cores. Further, although the majority of the growth in trip flows over the years is expected to come from the corridors between the major urban centers of the state, relative interzonal trip flow growth will likely be uniformly spread throughout the state. We also validate our results with the forecasts of two travel demand models, finding a difference of 5-15% in overall trip counts. Our GPS data-based demand model will contribute towards augmenting the conventional statewide travel demand model developed by the state and regional planning agencies.

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Agent-based models in urban transportation: review, challenges, and opportunities

• Faza Fawzan Bastarianto   ORCID: orcid.org/0000-0002-1004-6980 1 , 2 ,
• Thomas O. Hancock 1 ,
• Charisma Farheen Choudhury 1 &
• Ed Manley 3  

European Transport Research Review volume  15 , Article number:  19 ( 2023 ) Cite this article

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This paper aims to provide a comprehensive overview of the current state of agent-based models, focusing specifically on their application in urban transportation research. It identifies research gaps and challenges while outlining the key potential directions for future research.

Methodology

To conduct this study, a bibliometric analysis has been performed on 309 documents obtained from the Scopus database. The resulting clustering analysis has been further supplemented with content analysis.

The analysis reveals the existence of nine distinct clusters representing a wide range of research methods and problem interpretations in the field. In-depth examination of selected publications within each cluster has helped to identify key challenges faced by agent-based modelling approaches. These challenges include enhancing computing efficiency, developing unified calibration and validation methods, ensuring reproducibility of work, and incorporating various modules or frameworks into models to accurately replicate the complexities of the transport system and travel behaviour within specific application contexts.

1 Introduction

The modern transportation system is composed of complex large-scale interactions that are generated as the travellers engage with their dynamic environment to go from one location to another. The dynamic environment of modern transport system comprises of transport infrastructures, modes, services, and technologies. The decisions travellers make regarding their activities and trips in this dynamic environment are also governed by their spatial, societal and economical characteristics. The multi-dimensionality of the travel decisions and the underlying heterogeneity makes travel behaviour difficult to predict when there are changes in the transport system.

Researchers, engineers, and planners in transport rely on transportation forecasting models to predict the performance of the transport system in alternate future scenarios and evaluate the potential effectiveness of new plans and policies. Over the last few decades, two distinct approaches for travel demand modelling have emerged: trip-based and activity-based approaches.

The trip-based approach, frequently referred to the traditional four-step travel demand model, considers aggregate travel choices in four steps: trip generation, trip distribution, modal split, and route assignment [ 1 ]. Even though this model considers interactions between the stages in the simulation stage, in the majority of the cases, the models used in each step are estimated as stand-alone models (e.g. separate models for trip generation, attraction, mode choice and traffic assignment). Hence, they often struggle to predict certain situations such as derived travel or demand [ 2 ]. In addition, the conventional sequential travel demand modelling approach used in the four step approach may not adequately capture individual decision-making processes as it concentrates on aggregate travel behaviours. To solve these shortcomings, a coherent framework that can simulate four stages simultaneously at the disaggregate level is required. This motivated the development and increased use of activity-based models [ 3 ].

Activity-based models predict activities and associated travel choices by taking into account time and space constraints as well as individual characteristics. Using a sequence of activities and corresponding trips to connect those activities, individuals are assumed to maximise their activity utility by choosing the maximum utility among trips [ 4 ]. Even though activity-based models have the ability to be an alternative to four-step models, these models also have several problems and issues [ 5 ]. The current activity-based models simulate typical activity-travel patterns in a day. It is possible that certain activities cannot be completed within a simulation run, for example, where working hours exceed the end of the run. As a result, those activities may be removed from the schedule. Further, the integration of demand generation and its traffic assignment to the network still needs a robust solution. This approach is incapable of making changes in departure time or more significant activity rescheduling decisions. A more fundamental approach in simulating frequent responses between traffic assignment algorithm and the activity-based model of travel demand may be a technical solution to resolve this incompatibility.

On a parallel stream, in the last twenty years, agent-based modelling has emerged as a method to replicate the complexity in social systems. Agent-based models (ABM) represent individuals autonomous agents with independent characteristics and behavioural rules guiding their decisions and actions. Agents generally ‘act’ within a dynamic environment, allowing for the analysis of their interactions both with other individuals (in relation to proximity or connectivity) and with the environment in which they are placed [ 6 ]. Agents can learn, adapt, and hold different perceptions of an environment. The flexibility of the ABM framework means applications are broad, as agents can represent any sort of entity (e.g. person, car, road, city). Within transportation domains, ABM enables the creation of complex, dynamic, and stochastic transport systems, typically consisting of individual traveller or vehicular agents that have heterogeneous characteristics and behaviours (e.g. perceptions, needs, capabilities) and is adaptive to changes in circumstances and or the environment [ 7 , 8 ]. The ABM approach is often conflated with microscopic traffic simulation and population microsimulation, and while it shares some characteristics with these approaches, its notions of agent learning, adaptation and behavioural heterogeneity sets it apart. ABM furthermore places few constraints on how an agent or environment is represented, and as such, there is little to no dependence on specific processes or software packages for its implementation, allowing its application in a very broad set of contexts.

Agent-based models originated in the area of computing, where agents represent software entities that run independently and interact with other agents in an environment [ 9 ]. Alongside ABM, Cellular Automata (CA) models emerged, a simplified grid-based simulation approach, similar to ABM. CA models were introduced to transportation research as a novel method for modelling traffic flow in the 1990s [ 10 ], representing the first exploration of autonomous agents in the transportation domain. Expansions on this approach followed, and the first mentions of agent-based modelling in mobility research occurred in the 2000s [ 11 , 12 ]. The expansions include the development of intelligent traffic control systems [ 13 ] and the construction of decision support systems (DSS) which allow for the provision of recommendations of efficient route allocation across time and space for the travellers or other agents in the domain of road traffic management [ 14 , 15 ].

ABMs have since been applied to a diverse range of applications in transportation systems, including the micro, meso, and macro levels, which represent the interactions between agents, groupings of agents with similar attributes, and large-scale structures of agents in transport systems, respectively. In the case of the microscopic scale, they have been used to simulate the behavioural aspects of pedestrian movement [ 16 ] and their crossing behaviour in front of an automated vehicle (AV) [ 17 ]. Agents who share common properties (such as location or destination) can be aggregated to generate a higher level called mesoscopic. In this level, ABM has been developed to simulate the behaviour of drivers in a spatially explicit environment and is capable of capturing the characteristics of a large group of parking agents [ 18 ]. Meanwhile, ABM has been used to simulate the entire city or region at the macroscale, such as in Paris [ 19 ] and Singapore [ 20 ]. According to these studies, ABM was employed due to its capability of dealing with the uncertainty of a dynamic environment in which there is a complex interaction of modern transportation system that composes of innovative transport technologies.

As the modelling of a transportation system is well suited to ABM approach [ 21 ], extensive ABM tools have been developed within the past decade to overcome lots of complexities in modern transportation systems. Most ABM frameworks consist of several modules that can be integrated or used stand-alone, for instance SimMobility [ 22 ], TRANSIMS [ 23 ], and AnyLogic [ 24 ] There are also free and open-source frameworks which enable users to develop or replace any module by custom implementations to test particular aspects of their project such as MATSim [ 25 ], GAMA [ 26 ], and NetLogo [ 27 ]. Furthermore, technological advancements in ICT (information and communication technology) have resulted in the development of agent-based transport modelling and analysis using open and publicly available data [ 19 ].

Based on the wide-ranging growing literatures above, there has been changes in the way agent-based model are applied in the field of transportation in recent years. The introduction of new techniques due to the development of computational capabilities, as well as the emergence of new transport modes as a result of technological innovations, pose numerous challenges that must be addressed. Though some studies indicated that ABM have also been utilised at the national level, this has only occurred in countries with prominent Activity-travel Diary Survey data that can be scaled up to the national level, such as Singapore and Switzerland. Because of the lack of comprehensive research undertaken at the national level, the scope of this study is limited to a review of ABM studies conducted at the urban scale, where this model is frequently employed. Furthermore, it can also be implied that transportation in urban areas is extremely complicated due to the variety of modes of transportation used, the number of origins and destinations, and the variety and volume of traffic. Yet, to date, very few studies have reviewed the contribution of agent-based models within urban transportation fields in a wide-ranging literature approach. Previous research examining the application of agent-based modelling in transportation were limited to overviews of ABMs for autonomous vehicles in urban mobility and logistics [ 28 ], transport simulation and analysis [ 29 ], and the simulation of e-scooter sharing services [ 30 ].

This study, however, will contribute to the body of literatures by examining how researchers employed agent-based models in the field of urban transport research by using a bibliometric technique. By examining all of the publications related to a given topic or field, bibliometric analysis offers a promising approach for identifying the most important research or authors, as well as their relationships with one another [ 31 , 32 ]. This enables the researchers to investigate the current development of agent-based models while also shedding light on the emerging areas in the urban transport domain. Further, a detailed examination of content analysis using keyword clustering has been performed to identify the key trends and potential research gaps in existing literatures. This is expected to be useful for transport researchers and serve as the first step in developing ideas for new research, especially to those who are tackling important issues in urban transport research.

The remainder of this paper is structured as follows. Section  2 describes the methodology used in this study which are bibliometric and content analysis techniques. Section  3 presents the results and discussions of the application of agent-based models in the urban transport domain. Following this, challenges faced in agent-based model are presented in Sect.  4 . This section also provides some perspective for future research of agent-based model in the context of urban transport field. Finally, Sect.  5 summarises the findings and recommendations from this study.

The three key methodological steps used in this study are data collection, data analysis and visualisation. Each procedure contains several steps to be undertaken. Data was collected from Scopus indexed database and then refined by removing irrelevant sources using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines [ 33 ]. Bibliometric approaches were then used in the refined data to determine the distribution of the publications across years, types of documents, and perform co-word occurrence analysis. Co-word occurrence analysis allows for the exploration of past and present research trends as well as helping the analyst to uncover the research gaps. Results from bibliometric analysis were analysed further in content analysis where each selected paper’s content was studied in depth to uncover the challenges and opportunities of agent-based models in urban transport research.

2.1 Selection criteria

The literature data used in this research were downloaded from the Scopus indexed database. Scopus is used because it is an academic database that provides a broader coverage of scientific resource collections and has better metrics than other academic databases [ 34 ]. In the initial keyword search in the academic database, the time span was outlined as “all years” and the type of documents was expressed as “all types”.

It should be noted that whilst the terms agent-based and multi-agent are often used interchangeably, Railsback and Grimm [ 35 ] claimed that the second term is a branch of the first term that originated from computer science. This implies that the term agent-based should capture an article’s content that is also covered by the term multi-agent. Therefore, the search for relevant literature of agent-based model in urban transport involved sorting through a large number of articles using the main topic keyword: agent-based and “agent based” followed by the predetermined 52 keywords shown in Table 1 . The chosen keywords were selected to capture extensive previous literatures on agent-based model and matched three criteria: (1) emerging mobility services in urban areas (2) simulation framework and policy related to urban transport, and (3) agent-based model software toolkits for the purpose of both general and transport analysis. It may be noted that these criteria were determined based on general main topics used in previous extensive studies on agent-based model in urban transport research. Moreover, criteria related to agent-based modelling platforms for both general and transport purpose were considered to capture the implementation of various agent-based toolkits in this particular field. Afterward, PRISMA guidelines [ 33 ] were used to identify, select, assess, and refine the final data set of literatures in this study.

There were 1144 documents identified from the Scopus database through keyword searches. The refinement consisted of eliminating unrelated keywords and filtering only English papers (769 exclusions), and reading the abstract of each article (66 exclusions). After refinement, there were 309 documents included in the final dataset of literatures. The bibliometric analysis and content analysis were then employed to analyse the challenges and opportunities of agent-based model in urban transportation research.

2.2 Bibliometric analysis

Bibliometric analysis is a comprehensive method for examining and analysing large amounts of scientific data [ 36 ]. This method is widely used to read and understand the developmental key points of a particular field while also shedding light on its emerging areas. This technique can be used to perform the quantitative and qualitative analyses which depends on the scope and volume of the dataset.

There are two categories of bibliometric analysis: performance analysis and science mapping [ 37 ]. Performance analysis is the process of evaluating the contributions of research constituents to a particular field, while science mapping is the process of examining the relationships that exist between research components. Science mapping is used to visually observe the research distribution and direction of research trend as well as development. The techniques used in science mapping include citation analysis, co-citation analysis, co-word analysis, co-authorship analysis, and bibliographic coupling. In this study, co-word analysis or keyword co-occurrence networks were applied to build the science map of agent-based modelling in urban transport studies. The number of keywords and its frequency of occurrence in the network can express the theme of literatures. A clustering analysis of these frequently occurring keywords can shed light on the knowledge structure of the research field as well as highlight significant areas of interest.

Bibliometric tools including VOSviewer and bibliometrix are used in this study. VOSviewer is a bibliometric tool developed by van Eck and Waltman [ 38 ] used to visualise the science mapping which displays cluster analysis results. In the network generated by VOSviewer, items are expressed as nodes and links. The nodes, such as authors, keywords, countries, and affiliations, are proportional to the weight of appearance. Links indicate the association between the nodes, suggesting that nodes that are close to one another tend to appear together, whereas nodes that are distant from one another do not or nearly never occur together. Another tool, bibliometrix, is an open-source tool for executing a comprehensive bibliometric analysis [ 39 ]. This tool has been applied for the extraction, analysis, and visualisation of bibliographical information in various display metrics, such as three-field plot, word cloud, and treemap.

2.3 Content analysis

Content analysis is the process of detailed examination of the content of selected literatures. Literatures were grouped based on research clusters within agent-based model in urban transport field. Visualisation outputs from VOSviewer were used to investigate the research clusters within this particular field. Literatures in each research cluster were explored in detail with content analysis to deepen the analysis.

3 Results and discussion

In this section, the results of this study are presented and discussed. First, the historical trend of publications and modelling tools of agent-based models are analysed. Next, the distribution of geographical locations of the case studies used within agent-based modelling studies in urban transport is illustrated. Then, the results of keyword co-occurrence analysis to explore the trend and current state-of-the-art of agent-based model in urban transport are identified. Finally, the content analysis based on research clusters are demonstrated, which reveal the knowledge domain within research clusters.

3.1 Overview of the article outputs and modelling tools used

The final data set (consisting of 309 documents), represents the largest dataset for this type of analysis on agent-based models. There are four document types found in 309 publications within the time span of 2006–2022. Even though there may be several agent-based model-related publications prior to 2006, these documents do not appear in the search results. This is due to these publications not containing the keyword combinations used in their metadata, i.e., title, abstract, or keyword.

The most frequent document type is conference paper, accounting for 151 publications (49%), followed by journal articles, accounting for 147 outputs (48%). Book chapters account for 8 publications (3%) and review articles account for 3 documents (1%). The yearly output of articles is presented in Fig.  1 .

Dynamic of the top 10 agent-based modelling tools used and yearly output of articles within the period of study

Based on Fig.  1 , there were no significant increase in the number of publications in the first few years from 2006. In this period, there were limited agent-based simulation frameworks that had the sufficient performance required to do real-time simulations in terms of simulating the complete time horizon of the decision makers or agents. After this period, a steady rise can be observed from 2010 to 2014. Then, the growth of the publications is exponential, starting from 2015 to date. This is partially explained by the fact that numerous modelling tools in agent-based model were developed alongside significant increases in computing performance during the past decade, meaning that a much more realistic simulation could be made that runs in real time. Additionally, most of these simulation platform toolkits are fully open-source, allowing for researchers to more easily create extensions to improve the key features and solve particular problems. The use of the ten most frequently used agent-based modelling tools in the 309 documents is shown in Fig.  1 below.

Each line in Fig.  1 represents the cumulative occurrences of the top ten agent-based models used in the context of urban transport simulation. ABM frameworks that have specific names (e.g., SimMobility, MATSim, TAPAS) have been identified separately with their programming language used. The results from this illustration reveal that MATSim [ 25 ] is the most popular agent-based simulation framework used in this context. The ability of MATSim (Multi-Agent Transport Simulation) to simulate a large-scale agent-based model framework on stochastic and co-evolutionary algorithm in which each agent continuously searches for better travel plans until reaching its maximum utility, enables the user to develop and create various urban mobility scenarios. These are for instance measuring the presence of emerging modes of transport: autonomous mobility-on-demand in Zurich [ 40 ]; shared mobility in San Francisco [ 41 ]; electric vehicle in Berlin [ 42 ]; demand responsive transport in Michigan [ 43 ]. Furthermore, the MATSim simulation framework also provides the flexibility to be integrated with other platforms or frameworks in order to enhance or overcome some limitations (e.g. microscopic land-use model, multiple mode choice specifications, the modelling of sequences of activities and choices of location) of the existing MATSim framework, such as SILO [ 44 ], FEATHERS [ 45 ], EQASIM [ 46 ], BEAM [ 47 ], and mobiTopp [ 48 ].

The second most frequently used agent-based tool is NetLogo, a free open-source software based on an agent-based programming Scala and Java language [ 27 ]. NetLogo has been widely used for general and transport purpose studies due to its flexibility, which allows users to build and modify the model, perform multilevel modelling through connecting other models together, import both raster and vector data, and explore the elements interface. Previous research showed that NetLogo was employed to explore the impact of behavioural parameters on modal shift in public transport in Paris [ 49 ] and measure the effect of demand responsive shared transport on taxi service in Ragusa, Italy [ 50 ]. Other frequently applied agent-based frameworks are SimMobility [ 22 ] and AnyLogic [ 24 ]. SimMobility is a fully modular activity-based simulation platform that allows the user to utilise distinct modules by timeframe i.e. short-term (traffic simulation), mid-term (travel demand), and long-term (land use). A study measuring the effects of Automated Mobility-on-demand on accessibility and residential relocation was conducted in Singapore [ 51 ]. AnyLogic is a multimethod simulation modelling tool capable of simulating three major simulation modelling methodologies in place today: system dynamics, discrete-event, and agent-based modelling. This platform features various visual modelling languages and an industry-specific toolkit. Application of this modelling tool includes investigating the use of Automated Last-Mile Transport (ALMT) of train trips in Delft, Netherlands [ 52 ].

Previous studies have also proposed frameworks to analyse agent-based models in urban transport contexts. This includes an application of unspecific or uncommercial agent-based tool. Aschwanden et al. [ 53 ] used Esri City Engine for generating a 3D environment city model, then agents were created by using Massive Prime. This approach allows the user to analyse, predict, and quantify traffic fluctuations over time, as well as defining the number of individual traffic, public transport, and pedestrians in each area and link or street of a city. Another example is a study by Hyland and Mahmassani [ 54 ], which utilised an agent-based simulation framework in Python to model the dynamic system of autonomous vehicles (AV) and compared assignment strategies for a shared-use AV mobility service (SAMS). Additionally, Matlab and C++ are widely used for agent-based modelling in the context of urban transportation due to their adaptability, which allows users to construct computational codes utilising a large database of built-in algorithms.

These findings above show that many studies have simulated agent-based models in various platforms and frameworks to achieve numerous research purposes and solve complex problems. These approaches have different programming languages, primary application domains, scalability, strengths and shortcomings. Some platforms, such as SUMO [ 55 ] and AIMSUN [ 56 ], are specifically designed for modelling microscopic traffic flow dynamic, while others, such as TRANSIMS [ 23 ], SimMobility [ 22 ], and POLARIS [ 57 ], are designed for mesoscopic or large-scale simulation which lead to longer running times. The list of agent-based modelling tools and its additional frameworks including their detailed specifications such as aim, language used, and key features can be seen in the Appendix .

3.2 Geographical distributions

This research took into consideration the country where the case study has been undertaken, which may be different from the country of authors’ affiliations (Fig.  2 ).

Case studies of agent-based models in urban transport by country

The geographical distribution of papers—based on case studies of agent-based model in urban transport—is concentrated in developed countries such as Germany, US, Switzerland, and Singapore. In contrast, models are rarely applied in the context of the Global South, where countries have different characteristics of travel behaviour and may require different approaches.

Germany is the most considered country in terms of where the case studies have been conducted. This is in line with the finding from cumulative occurrences of agent-based modelling tools analysis where it is revealed that MATSim, a framework developed continuously in TU Berlin, is the most frequently used modelling tool for agent-based transportation modelling. This relationship can be seen on the diagram in Fig.  3 as follows.

The relationship among agent-based framework, case studies, and authors

The left-most column represents the agent-based modelling tools, the middle column displays the countries where the case studies were completed, and the right-most column shows the top authors in the field. The height of the box indicates the number of publications, and thicker line connections imply that a greater volume of work or information is produced.

3.3 Co-occurrence analysis and key research clusters

Further, analysis of the distribution of keywords co-occurrence network map was undertaken to effectively reflect on the research hotspots. The words in the co-occurrence network map were derived from a textual field (e.g., title, abstract, and author’s keywords) in a bibliographic collection [ 38 ]. Additionally, the co-occurrence analysis assumes that words that frequently occur together reflect a thematic relationship represented in the same colour which is then formed clustering [ 37 ]. The co-occurrence network map was created by the VOSviewer software as shown in Fig.  4 .

Keyword co-occurrence network visualisation

Co-occurrence analysis, which identifies the major categories and their interrelationships, was used to detect the disciplinary distribution of agent-based modelling in urban transport research. The size of the nodes and words in Fig.  4 corresponds to the weights of the nodes in that particular graph. The weight increases in proportion to the size of the node and word. Nodes are separated by a distance, which indicates how strong their relationship is between each other. A closer distance is usually indicative of a stronger relationship. The line drawn between two keywords indicates that they have appeared together in the same document. The greater the thickness of the line, the greater the likelihood of their co-occurrence.

By analysing the co-occurrence of frequent terms, the research hotspots of agent-based modelling in urban transport research were determined. The minimum number of co-occurrences for a keyword was set to 3. From the 685 keywords associated with agent-based model that were extracted, 47 met the criteria of having at least 3 occurrences. The keyword “agent-based modelling” appears the most frequently. Other keywords that appear frequently include “transport modelling”, “travel demand”, “demand responsive transport”, “public transport”, and “electric vehicles”. Since this study applied keywords co-occurrence analysis, the clustering results show keywords with a strong correlation to one another will appear in the same cluster. This process is thus not subject to analyst bias, though may result in some counterintuitive clustering. For example, the results indicate that “modal shift” is a part of cluster 8 “public transport”, which means that the majority of the papers in that cluster include both “modal shift” and “public transport” as keywords and may focus on how to move people to public transportation, such as paper by Rahman et al. [ 58 ] and Barber et al. [ 49 ].

Furthermore, the trend of the research in this field over time can be identified by exploring Fig.  5 . As shown in Fig.  5 , dynamic traffic assignment and transport measures related to congestion pricing in agent-based models are the dominant categories from 2016 to 2017, but they became less influential in the 2020s. Emerging transport technology trends such as ride sharing, demand responsive transport, and electrification appeared in the period 2017–2019 and received more attention from researchers in the 2020s to date.

Keyword co-occurrence network based on trend of publication date

On the basis of the network in Fig.  4 , comparable terms as shown with the same colour were clustered. The nomenclature of the clusters was defined based on the lists of the keywords for each cluster having the same colour: Cluster 1 (red): General Transport Modelling, Cluster 2 (green): Travel Behaviour, Cluster 3 (blue): Emerging Transport Modes, Cluster 4 (yellow): Transport Policy, Cluster 5 (purple): Urban Logistic, Cluster 6 (cyan): Travel Demand, Cluster 7 (orange): Parking, Cluster 8 (brown): Public Transport, and Cluster 9 (pink): Shared Autonomous Taxi. Clusters are analysed next in turn by considering prominent examples in the literatures.

3.4 Content analysis based on research cluster

Research clusters are found as representations of various studies utilising a diverse set of research approaches and problem interpretations. In this subsection, the outline of the nine clusters identified are described. Cluster 1 ( General Transport Modelling ) mainly consists of publications developing the theory and conceptual works to different spatial scales of agent-based modelling in urban transport including microscopic and macroscopic simulation. The type of application at microscopic level includes movement of pedestrians or the movement of cars on the road network. For example, Fujii et al. [ 59 ] developed a new framework for simulating mixed traffic composed of pedestrians, cars, and trams, which may be used to support considerations concerning road management, signal control, and public transportation. The pedestrian agents can walk freely, avoid collisions, stop momentarily, pass other pedestrians, and move in the same way as car and tram agents. The car agents can plan their routes and determine acceleration. The tram agents are based on the car agents, but without the functions for changing lanes nor considering the best route. Moreover, simulation at a large-scale, which involves the modelling of corridor-level and sub-area transportation operations and planning applications, fits in this cluster [ 60 ].

In cluster 2 ( Travel Behaviour ) the links between data-driven simulation, dynamic traffic assignment, transport planning, and travel behaviour were first beginning to be recognised. For example, a high-performance data-driven agent-based modelling framework has been employed to simulate the uptake of active mode choice during commuting such as walking and bicycling in New York City [ 61 ]. Through a GIS-enabled database for the City of New York, the ABM model explicitly incorporated walking and cycling network data with pedestrian and bike accident data. This study pointed out that data-driven simulation is the most proper way to leverage the ABM approach for a close analysis of mode choice as it allows for a more realistic simulation of the environment. Moreover, smart card data can be utilised as an input for analysing travel behaviour concerning transit users in a large-scale activity based public transport simulation [ 62 ]. This work demonstrated that smart card data can generate microsimulation travel demand models effectively by improving the statistical analysis and utilising advanced data mining approaches. An effort to leverage the mode decisions has been done by bridging discrete mode choice models and agent-based simulation [ 63 ], where it was found that the convergence speed of the simulation may significantly increase by implementing a discrete mode choice model in the ABM compared to the baseline model. The baseline model is in this case the existing model used by ABM platforms when selecting mode for each trip; for instance, ABM tool such as MATSim use a co-evolutionary algorithm to reach an equilibrium state of the system and allow each agent to select different modes for a trip based on utility value from the previous agent’s plan [ 25 ]. Additionally, incorporating dynamic traffic assignment with agent-based travel behaviour models can be used to provide a better result in evaluating the impact of land development on transportation infrastructure, compared to using traditional approaches such as static modelling [ 64 ]. It provides a complex yet practical method for analysing the effect of a single or series of land development projects on a driver's behaviour, as well as on travel demand pattern and time-dependent traffic conditions. Furthermore, agent-based simulation has been developed to examine the impacts of transportation development plans on modal shifts and residential location choice [ 65 ].

The research then focused more on how agent-based modelling has been adapted for emerging transport modes which also relates to the application of technologies in transport that build a complex traffic system. Clusters 3 and 9 are related as both are characterised by the performance of emerging transport modes. Cluster 3 ( Emerging Transport Modes ) contains studies about the impact of emerging transport modes in response to travel behaviour as well as travel demand. Given the nature of public transport data and the readiness of transportation infrastructure technology, the majority of publications in cluster 3 are aggregated at the city level. The key problems studied include expected capacity gains and increases in vehicle kilometres travelled for shared autonomous vehicles [ 66 ], autonomous vehicle fleet sizes [ 67 ], the travel and environmental implications [ 68 ], travellers’ behaviour or acceptance of emerging transport modes as well as their interactions with a complex transport environment [ 69 ], and competition between existing and emerging modes [ 50 ]. On the other hand, cluster 9 ( Shared Autonomous Taxi ) is becoming an important attraction in emerging transport modes. Though this falls within the broad umbrella of ‘emerging modes’, this has been assigned to a separate cluster due to the high number of papers on this. Sharing autonomous vehicles will allow people to travel without the costs and responsibility of vehicle ownership. Consequently, taxi passengers will likely be the first users of shared autonomous vehicles [ 70 ]. Problems analysed include comparing the potential benefits and drawbacks of ride sharing both traditional taxis and shared autonomous taxis [ 71 ], the impact of introduction of autonomous taxi to travel demand [ 72 ], and commuters’ departure times [ 73 ].

The focus in cluster 4 ( Transport Policy ) shifts to the analysis of transport management policy using agent-based modelling approaches. For example, it is believed that road pricing is an effective management strategy for reducing traffic congestion on transportation networks. Various road pricing schemes have been developed using agent-based simulators. A combination of macroscopic fundamental diagram and an agent-based traffic model can replicate the heterogeneity and complexities of traveller preferences in analysing the impact of a dynamic cordon pricing scheme [ 74 ]. Further, a time-dependent area-based pricing scheme for congested multimodal urban networks has been developed by also adding incentive programmes to improve public transport services and encourage modal shift [ 75 ]. The pricing scheme also can be done for the specific service area of demand responsive transit (DRT) to encourage modal shift from car to DRT [ 76 ]. Cluster 7 ( Parking ) is considered to have relationship with the transport policy. A parking choice model can be implemented into an existing agent-based traffic simulation [ 77 ]. This model can send an input to the traffic simulation, allowing the simulation to respond to spatial variations in parking demand and supply. Another implementation of agent-based simulation in parking is examining the role of ridesharing to reduce the burden of high-demand for parking in urban centres [ 78 ]. It has also been demonstrated that parking pricing policies significantly impact the probability that a traveller would send their autonomous vehicle to travel back home instead of parking at parking lot [ 79 ].

Cluster 5 ( Urban Logistics ) moves on from the private and public transport modes to look at the role of urban freight transport especially in improving the quality of the urban environment and profit margins in the supply chain [ 80 ]. The papers in this cluster mostly use agent-based frameworks for analysing urban logistics as this method can be used to assess the interaction between agents. Traditional approaches are inadequate at evaluating such relations and fail to consider heterogeneous objectives among urban logistics agents. A development of agent-based framework in city logistics also allows for the implementation of fully-disaggregated simulations of commodity contracts, operation planning of logistics and vehicle, parking decisions, and electrification of urban freight transport [ 81 ].

Cluster 6 ( Travel Demand ) tends to focus on how to generate synthetic populations of travellers and their detailed travel demand as a basis for agent-based transport simulations. Unlike conventional transport models, agent-based transport modelling requires more detail on synthetic populations as activity chains are required. However, such output is rarely reproducible because it relies on proprietary data and tools. Thus to stimulate reproducible agent-based transport simulations, a number of studies have developed a method for creating synthetic travel demand based on open data and open software, which can be replicated by any researcher. The mobiTopp is a modular agent-based travel demand modelling framework that enables the modules to be integrated with agent-based platforms [ 82 ]. Furthermore, a framework providing a continuous pipeline from raw data to a final generic synthetic travel demand was introduced [ 46 ]. This framework has been applied in various regions, namely Île-de-France [ 19 ], Switzerland [ 46 ], and Sao Paulo [ 83 ]. Also, travel demand data for agent-based simulation purposes can be acquired from human mobility based on mobile phone data [ 84 ]. Population synthesis can be produced by employing various methods, such as Iterative Proportional Fitting to create a synthetic baseline population of individuals and households for activity-based models at the microscopic level [ 85 ], Iterative Proportional Updating approach to match both individual and household attributes level in the population [ 86 ], and Markov chain Monte Carlo (MCMC) simulation-based approach for synthesising populations [ 87 ].

Finally, Cluster 8 ( Public Transport ) covers a set of studies regarding agent-based modelling in association with public transport. This includes studies employing agent-based simulation to examine the impact of public transport in modal shift [ 49 ], analysing potential demand for emerging transport mode competing or complementing public transport [ 20 , 88 ], designing public transport network [ 60 ], assessing the impact of public transport infrastructure extension on future traffic [ 89 ], and improving public transport routes [ 90 ] as well as day-to-day operation [ 91 ].

4 Challenges and future research directions

Publications in each research cluster were investigated in detail to explore the key challenges of the existing agent-based models, particularly in urban transport studies. These are discussed below along with future research directions.

4.1 Improving computing efficiency

The review confirmed that when a large number of agents are simulated (Cluster 1 and 2), the model environment becomes more complex. This is particularly the case when different components such as mode choice, route choice, scheduling, land use, ride-sharing scheme, destination choice, etc. are combined. This requires an increase in computational resources to improve model performance in capturing complex interactions at a highly granular spatial scale among agents (e.g., individuals and the transport system).

In large-scale scenarios, MATSim typically manage numerous agents at the micro level, which can require a significant amount of time to run. For instance, in 2015, a study by Waraich et al. [ 92 ] exploring a MATSim simulation run for Switzerland scenario taking 7.3 million agents in one million links on the network was reported to take 3 h and 16 min to complete a single iteration. Based on their experience, 60 iterations were required meaning a total runtime of up to 11 days. The hardware used in the experiment was a Sun Fire X4600 M2 with 16 cores in 8 dual core CPUs and 128 GB of memory. Adding more complex transport systems such as demand responsive transport will only increase computing time. A MATSim transport model of a demand-responsive transit system in Wayne County, Michigan, with a travel demand of 9 million trips, required roughly 43 h to simulate 30 iterations on a high-performance computer cluster with 12 cores and 144 GB of memory [ 93 ]. In order to alleviate the requirement of multiple days of computing time, utilising cloud-based computation services allows for the possibility of increasing simulation realism and parallel processing while also shortening the runtime. Meanwhile, some researchers have attempted to build a framework to accelerate the computing time of large-scale agent-based mobility scenarios. For example, Manley et al. developed a hybrid agent-based modelling approach that combines a descriptive representation of detailed driver behaviour with a simplified, collective model of traffic flow in an effort to strike a balance between the demands of behavioural realism and computational capacity [ 15 ]. Taking central London as a study zone, the hybrid model was run in 4 h and 51 min, while the purely agent-based approach completes in 11 h and 24 min. GEMSim, a GPU-accelerated (graphics processing unit) simulation platform, has also been developed [ 94 ]. GEMSim has been tested on simulating a large-scale scenario for Switzerland, running a full day of the 5.2 million agents’ daily plans with detailed road infrastructures and public transport schedule in less than 5 min computing time. However, such innovative approaches are relatively scarce in the common agent-based platforms used and the effort to improve the computing efficiency in large-scale agent-based models will continue to be a motivation as transport systems become more complex in the future.

4.2 Unified calibration and validation procedures

Based on cluster analysis, it can be noticed that the methods for developing agent-based models for a variety of transport system purposes are frequently addressed. Different calibration and validation methods are implemented such as comparing the distribution of population socio-demographics from simulations against real household census surveys [ 19 ], mode share comparison between simulation and real-life traffic counts [ 95 ], and matching daily activity pattern including time-of-day, intermediate stops, number of tours, and mode choice for all tours between household travel survey data and simulation results [ 96 ]. However, there is no unified conceptual framework that can be implemented properly and securely for calibration and validation process as the application of agent-based modelling in transport is diverse across different problem levels. Thus, a calibration or validation method such as comparing the findings with analytical models can be explored further to increase the credibility of the agent-based models results. It should be noted that transport is not alone in facing this challenge, with calibration and validation noted as challenges in ABM practice across different disciplines [ 97 ].

4.3 Reproducibility of work

As one of the most recently developed methods, for which applications are still growing exponentially, agent-based models in transport system require extensive exploration by many researchers. However, the researchers in this area face difficulties even to reproduce simulations since previous models are rarely replicable due to confidential data and tools. Cluster 6 ( Travel Demand ) is the most significant area that requires open and publicly available data of travel daily activity and population data as a basis to execute agent-based transport simulations.

An effort to tackle this issue has been raised by Hörl and Balac [ 19 ]. They introduced a streamlined process for producing a synthetic travel demand with specific households, persons, and their daily activity chains for Paris and the surrounding region of Île-de-France which is totally based on open data, open software and can be replicated by any researcher. The generated travel demand is made available for others to utilise as a comprehensive data source for agent-based transport simulations and as a testing ground for population and demand synthesis techniques.

More broadly, there is a movement towards open-source software and publication of code. This is particularly evident within the MATSim community, Footnote 1 and has some traction in ABM more broadly. Footnote 2 However, an improved standardisation of model design and parameterisation is needed and there are opportunities to learn from elsewhere in establishing these [ 98 ].

4.4 Embedding various modules or frameworks in models

The use of agent-based models in many applications of transportation research is growing. In general, the majority of open-source agent-based simulation platforms can be coupled with other frameworks to solve specific problems. Some of the independent modular frameworks are being integrated with existing agent-based simulation platforms to achieve efficient and accurate individual behavioural models, i.e., Eqasim framework by Hörl and Balac [ 46 ] which can be integrated with MATSim and SUMO. Embedding modular framework to existing ABM platforms can also overcome some challenges or limitations of the existing agent-based model studies. Hörl et al. [ 63 ] integrated discrete choice model with agent-based model to improve convergence speed of ABM simulation. FEATHERS [ 45 ] and mobiTopp [ 48 ] can be employed with ABM platforms to increase the capability of existing ABM components related to activity-based models and travel demand models, respectively. ABM can also have multiple mode choice specifications by embedding BEAM [ 47 ]. Furthermore, SILO framework can be integrated with open-source ABM to explore complex interactions between land use and transport models [ 44 ]. Nonetheless, the existing integrated frameworks have shortcomings that need to be addressed, for instance adding environmental analysis and considering full-day activity-travel patterns in integrated land use/transport models. Therefore, exploring as well improving a variety of modular frameworks to be embedded in agent-based toolkit is essential for working closely with real-world individual behavioural and traffic systems.

4.5 Transport complex system in affecting travel behaviour and travel demand

Based on the previous studies grouped in Cluster 3 and 9, transport systems are becoming more complex as they faces various emerging transport modes and mobility schemes such as ridesharing, ride-pooling, demand responsive transport, electric vehicles, autonomous vehicles, and urban air mobility. Transport modellers need to adjust models to these developments occurring in the transport system as it is difficult to capture the interactions in the complex transport system in conventional transport models.

Investigating the impact of these complex systems on existing transport environments is crucial. In that sense, there would be either competing [ 50 ] or complementary [ 99 ] interactions between emerging transport modes and existing modes. The results of this phenomenon might be different in various countries or regions. Emerging modes of transport also have the potential to be integrated with existing public transport or various travel demand management measures such as mobility hubs or park and ride. Moreover, measuring the impact of large transportation infrastructure developments or extensions in spatial and temporal approaches is crucial. Nevertheless, studies regarding this matter have not so far been well assessed in agent-based approaches. Further, it is also essential to investigate the use of ICT (information communication technology) for mobility substitution, including new forms of teleworking, telecommuting, and e-shopping.

Moreover, new forms of data sources as an input in the context of ABM are increasingly being utilised, including smart card data and mobile phone data. These methods allow for better coverage of public transportation trips. For example, smart card data records and stores the date and time of each entry and exit activity as well as the boarding and alighting stops/stations [ 20 ]. This data can then be used, for example, to investigate the demand characteristics for integrating autonomous vehicles into the public transport system. Meanwhile, mobile phone data is utilised to determine work, education, or any other types of locations for each of the agents in the study area’s population [ 100 ] and to generate origin–destination matrices of trips during different time slots as well as commuting behaviours of people in the population [ 84 ]. Having a better input and result on the travel behaviour and travel demand towards emerging transport modes and mobility schemes is essential for policy makers in making more adaptive plans and infrastructure that can endure despite the uncertainty caused by urban mobility transition and technological changes.

4.6 Equity concerns of study location

The studies on agent-based transport simulation in urban area are unevenly distributed across geographical scale. The case studies are mostly conducted in developed economies, for instance Germany, Switzerland, Singapore, and the US rather than developing economies. Some of the studies in developing countries can be found in China [ 101 ], Indonesia [ 95 ], and Thailand [ 89 ]. In developed economic countries, the sources of data tend to be more complete and require less preparatory work for the implementation of an agent-based simulation framework compared to data from developing countries. Hence, data availability that is compatible with the ABM framework is a significant issue that must be addressed in order to have more greater shares of ABM studies in the context of Global South. However, different contexts in developing economies and developed economies may also result in additional challenges and insights that need to be addressed for future research. These additional challenges include socio-demographic structure, travel patterns, and available transport modes that can be substantially different. There may also be substantial challenges in achieving an accurate perception of urban travel patterns and individual behaviour based on travel characteristics, transport modes, cultural and social influence, road network supply capacity, etc.

5 Conclusions

The current study provides a comprehensive review of agent-based models by employing bibliometric and content analysis. The main contributions of the study are (1) highlighting the diversity of the applications of agent-based models in urban transport research; (2) identifying the research gaps and (3) summarising the key challenges and opportunities for future research in this domain. The paper is expected to serve as a valuable resource for researchers and practitioners considering the application of agent-based models in the context of urban transport planning.

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Acknowledgements

The authors wish to thank the funders of this study.

Faza Fawzan Bastarianto is a PhD student supported by the BPPT (Center for Higher Education Funding), Ministry of Education, Culture, Research, and Technology, the Republic of Indonesia and LPDP (Indonesia Endowment Fund for Education), Ministry of Finance of the Republic of Indonesia [Grant No. 1772/J5/KM.01.00/2021]. Charisma Choudhury and Thomas Hancock’s time is supported by  the UKRI Future Leader Fellowship, UK [MR/T020423/1-NEXUS].

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1.1 List of several agent-based modelling tools for general purposes

• Source : a [ 102 ], b [ 24 ], c [ 103 ], d [ 104 ], e [ 105 ]

1.2 List of several agent-based modelling tools for transport analysis purposes

• Source : a [ 25 ], b [ 22 ], c [ 57 ], d [ 23 ], e [ 26 ]

1.3 List of several frameworks to support agent-based model analysis in the context of transport domain

• Source : a [ 82 ], b [ 106 ], c [ 46 ], d [ 47 ], e [ 44 ]

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Bastarianto, F.F., Hancock, T.O., Choudhury, C.F. et al. Agent-based models in urban transportation: review, challenges, and opportunities. Eur. Transp. Res. Rev. 15 , 19 (2023). https://doi.org/10.1186/s12544-023-00590-5

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Micro-simulation of daily activity-travel patterns for travel demand forecasting

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• Ryuichi Kitamura 1 ,
• Cynthia Chen 2 ,
• Ram M. Pendyala 3 &
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The development and initial validation results of a micro-simulator for the generation of daily activity-travel patterns are presented in this paper. The simulator assumes a sequential history and time-of-day dependent structure. Its components are developed based on a decomposition of a daily activity-travel pattern into components to which certain aspects of observed activity-travel behavior correspond, thus establishing a link between mathematical models and observational data. Each of the model components is relatively simple and is estimated using commonly adopted estimation methods and existing data sets. A computer code has been developed and daily travel patterns have been generated by Monte Carlo simulation. Study results show that individuals' daily travel patterns can be synthesized in a practical manner by micro-simulation. Results of validation analyses suggest that properly representing rigidities in daily schedules is important in simulating daily travel patterns.

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Kitamura, R., Chen, C., Pendyala, R.M. et al. Micro-simulation of daily activity-travel patterns for travel demand forecasting. Transportation 27 , 25–51 (2000). https://doi.org/10.1023/A:1005259324588

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A complex network perspective for characterizing urban travel demand patterns: graph theoretical analysis of large-scale origin–destination demand networks

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Urban travel demand, consisting of thousands or millions of origin–destination trips, can be viewed as a large-scale weighted directed graph. The paper applies a complex network-motivated approach to understand and characterize urban travel demand patterns through analysis of statistical properties of origin–destination demand networks. We compare selected network characteristics of travel demand patterns in two cities, presenting a comparative network-theoretic analysis of Chicago and Melbourne. The proposed approach develops an interdisciplinary and quantitative framework to understand mobility characteristics in urban areas. The paper explores statistical properties of the complex weighted network of urban trips of the selected cities. We show that travel demand networks exhibit similar properties despite their differences in topography and urban structure. Results provide a quantitative characterization of the network structure of origin–destination demand in cities, suggesting that the underlying dynamical processes in travel demand networks are similar and evolved by the distribution of activities and interaction between places in cities.

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AB - Urban travel demand, consisting of thousands or millions of origin–destination trips, can be viewed as a large-scale weighted directed graph. The paper applies a complex network-motivated approach to understand and characterize urban travel demand patterns through analysis of statistical properties of origin–destination demand networks. We compare selected network characteristics of travel demand patterns in two cities, presenting a comparative network-theoretic analysis of Chicago and Melbourne. The proposed approach develops an interdisciplinary and quantitative framework to understand mobility characteristics in urban areas. The paper explores statistical properties of the complex weighted network of urban trips of the selected cities. We show that travel demand networks exhibit similar properties despite their differences in topography and urban structure. Results provide a quantitative characterization of the network structure of origin–destination demand in cities, suggesting that the underlying dynamical processes in travel demand networks are similar and evolved by the distribution of activities and interaction between places in cities.

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