travel demand patterns

Travel Demand Forecasting: Parameters and Techniques (2012)

Chapter: chapter 1 - introduction.

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1 1.1 Background In 1978, the Transportation Research Board (TRB) published NCHRP Report 187: Quick-Response Urban Travel Estimation Techniques and Transferable Parameters (Sosslau et al., 1978). This report described default parameters, factors, and manual techniques for doing 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 Nation- wide Personal Transportation Survey, an update to NCHRP Report 187 was published in the form of NCHRP Report 365: Travel Estimation Techniques for Urban Planning (Martin and McGuckin, 1998). Since NCHRP Report 365 was published, significant changes have occurred affecting the complexity, scope, and context of transportation planning. Transportation planning tools have evolved and proliferated, enabling improved and more flexible analyses to support decisions. The demands on trans- portation planning have expanded into special populations and broader issues (e.g., safety, congestion, pricing, air quality, environment, climate change, and freight). In addition, the default data and parameters in NCHRP Report 365 need to be updated to reflect the planning requirements of today and the next 10 years. The objective of this report is to revise and update NCHRP Report 365 to reflect current travel characteristics and to pro- vide guidance on travel demand forecasting procedures and their application for solving common transportation problems. It is written for “modeling practitioners,” who are the public agency and private-sector planners with responsibility for devel- oping, overseeing the development of, evaluating, validating, and implementing travel demand models. This updated report includes the optional use of default parameters and appropriate references to other more sophisticated techniques. The report is intended to allow practitioners to use travel demand fore- casting methods to address the full range of transportation planning issues (e.g., environmental, air quality, freight, multimodal, and other critical concerns). One of the features of this report is the provision of trans- ferable parameters for use when locally specific data are not available for use in model estimation. The parameters pre- sented in this report are also useful to practitioners who are modeling urban areas that have local data but wish to check the reasonableness of model parameters estimated from such data. Additionally, key travel measures, such as average travel times by trip purpose, are provided for use in checking model results. Both the transferable parameters and the travel measures come from two main sources: the 2009 National Household Travel Survey (NHTS) and a database of model documentation for 69 metropolitan planning organizations (MPOs) assembled for the development of this report. There are two primary ways in which planners can make use of this information: 1. Using transferable parameters in the development of travel model components when local data suitable for model development are insufficient or unavailable; and 2. Checking the reasonableness of model outputs. This report is written at a time of exciting change in the field of travel demand forecasting. The four-step modeling process that has been the paradigm for decades is no longer the only approach used in urban area modeling. Tour- and activity-based models have been and are being developed in several urban areas, including a sizable percentage of the largest areas in the United States. This change has the potential to significantly improve the accuracy and analytical capability of travel demand models. At the same time, the four-step process will continue to be used for many years, especially in the smaller- and medium- sized urban areas for which this report will remain a valuable resource. With that in mind, this report provides information on parameters and modeling techniques consistent with the C h a p t e r 1 Introduction

2four-step process and Chapter 4, which contains the key information on parameters and techniques, is organized con- sistent with the four-step approach. Chapter 6 of this report presents information relevant to advanced modeling practices, including activity-based models and traffic simulation. This report is organized as follows: • Chapter 1—Introduction; • Chapter 2—Planning Applications Context; • Chapter 3—Data Needed for Modeling; • Chapter 4—Model Components: – Vehicle Availability, – Trip Generation, – Trip Distribution, – External Travel, – Mode Choice, – Automobile Occupancy, – Time-of-Day, – Freight/Truck Modeling, – Highway Assignment, and – Transit Assignment; • Chapter 5—Model Validation and Reasonableness Checking; • Chapter 6—Emerging Modeling Practices; and • Chapter 7—Case Studies. This report is not intended to be a comprehensive primer for persons developing a travel model. For more complete information on model development, readers may wish to consult the following sources: • “Introduction to Urban Travel Demand Forecasting” (Federal Highway Administration, 2008); • “Introduction to Travel Demand Forecasting Self- Instructional CD-ROM” (Federal Highway Administra- tion, 2002); • NCHRP Report 365: Travel Estimation Techniques for Urban Planning (Martin and McGuckin, 1998); • An Introduction to Urban Travel Demand Forecasting— A Self-Instructional Text (Federal Highway Administration and Urban Mass Transit Administration, 1977); • FSUTMS Comprehensive Modeling Online Training Workshop (http://www.fsutmsonline.net/online_training/ index.html#w1l3e3); and • Modeling Transport (Ortuzar and Willumsen, 2001). 1.2 Travel Demand Forecasting: Trends and Issues While there are other methods used to estimate travel demand in urban areas, travel demand forecasting and mod- eling remain important tools in the analysis of transportation plans, projects, and policies. Modeling results are useful to those making transportation decisions (and analysts assisting in the decision-making process) in system and facility design and operations and to those developing transportation policy. NCHRP Report 365 (Martin and McGuckin, 1998) pro- vides a brief history of travel demand forecasting through its publication year of 1998; notably, the evolution of the use of models from the evaluation of long-range plans and major transportation investments to a variety of ongoing, every- day transportation planning analyses. Since the publication of NCHRP Report 365, several areas have experienced rapid advances in travel modeling: • The four-step modeling process has seen a number of enhancements. These include the more widespread incor- poration of time-of-day modeling into what had been a process for modeling entire average weekdays; common use of supplementary model steps, such as vehicle availability models; the inclusion of nonmotorized travel in models; and enhancements to procedures for the four main model components (e.g., the use of logit destination choice models for trip distribution). • Data collection techniques have advanced, particularly in the use of new technology such as global positioning systems (GPS) as well as improvements to procedures for performing household travel and transit rider surveys and traffic counts. • A new generation of travel demand modeling software has been developed, which not only takes advantage of modern computing environments but also includes, to various degrees, integration with geographic information systems (GIS). • There has been an increased use of integrated land use- transportation models, in contrast to the use of static land use allocation models. • Tour- and activity-based modeling has been introduced and implemented. • Increasingly, travel demand models have been more directly integrated with traffic simulation models. Most travel demand modeling software vendors have developed traffic simulation packages. At the same time, new transportation planning require- ments have contributed to a number of new uses for models, including: • The analysis of a variety of road pricing options, including toll roads, high-occupancy toll (HOT) lanes, cordon pricing, and congestion pricing that varies by time of day; • The Federal Transit Administration’s (FTA’s) user benefits measure for the Section 5309 New Starts program of transit projects, which has led to an increased awareness of model properties that can inadvertently affect ridership forecasts;

3 • The evaluation of alternative land use patterns and their effects on travel demand; and • The need to evaluate (1) the impacts of climate change on transportation supply and demand, (2) the effects of travel on climate and the environment, and (3) energy and air quality impacts. These types of analyses are in addition to several traditional types of analyses for which travel models are still regularly used: • Development of long-range transportation plans; • Highway and transit project evaluation; • Air quality conformity (recently including greenhouse gas emissions analysis); and • Site impact studies for developments. 1.3 Overview of the Four-Step Travel Modeling Process The methods presented in this report follow the conven- tional sequential process for estimating transportation demand that is often called the “four-step” process: • Step 1—Trip Generation (discussed in Section 4.4), • Step 2—Trip Distribution (discussed in Section 4.5), • Step 3—Mode Choice (discussed in Section 4.7), and • Step 4—Assignment (discussed in Sections 4.11 and 4.12). There are other components commonly included in the four-step process, as shown in Figure 1.1 and described in the following paragraphs. The serial nature of the process is not meant to imply that the decisions made by travelers are actually made sequentially rather than simultaneously, nor that the decisions are made in exactly the order implied by the four-step process. For example, the decision of the destination for the trip may follow or be made simultaneously with the choice of mode. Nor is the four-step process meant to imply that the decisions for each trip are made independently of the decisions for other trips. For example, the choice of a mode for a given trip may depend on the choice of mode in the preceding trip. In four-step travel models, the unit of travel is the “trip,” defined as a person or vehicle traveling from an origin to a destination with no intermediate stops. Since people traveling for different reasons behave differently, four-step models segment trips by trip purpose. The number and definition of trip purposes in a model depend on the types of information the model needs to provide for planning analyses, the char- acteristics of the region being modeled, and the availability of data with which to obtain model parameters and the inputs to the model. The minimum number of trip purposes in most models is three: home-based work, home-based nonwork, and nonhome based. In this report, these three trip purposes are referred to as the “classic three” purposes. The purpose of trip generation is to estimate the num- ber of trips of each type that begin or end in each location, based on the amount of activity in an analysis area. In most models, trips are aggregated to a specific unit of geography (e.g., a traffic analysis zone). The estimated number of daily trips will be in the flow unit that is used by the model, which is usually one of the following: vehicle trips; person trips in motorized modes (auto and transit); or person trips by all modes, including both motorized and nonmotorized (walking, bicycling) modes. Trip generation models require some explanatory variables that are related to trip-making behavior and some functions that estimate the number of trips based on these explanatory variables. Typical variables include the number of households classified by characteristics such as number of persons, number of workers, vehicle availability, income level, and employment by type. The output of trip generation is trip productions and attractions by traffic analysis zone and by purpose. Trip distribution addresses the question of how many trips travel between units of geography (e.g., traffic analysis zones). In effect, it links the trip productions and attractions from the trip generation step. Trip distribution requires explanatory variables that are related to the cost (including time) of travel between zones, as well as the amount of trip-making activity in both the origin zone and the destination zone. The outputs of trip distribution are production-attraction zonal trip tables by purpose. Models of external travel estimate the trips that originate or are destined outside the model’s geographic region (the model area). These models include elements of trip generation and distribution, and so the outputs are trip tables represent- ing external travel. Mode choice is the third step in the four-step process. In this step, the trips in the tables output by the trip distri- bution step are split into trips by travel mode. The mode definitions vary depending on the types of transportation options offered in the model’s geographic region and the types of planning analyses required, but they can be generally grouped into auto mobile, transit, and nonmotorized modes. Transit modes may be defined by access mode (walk, auto) and/or by service type (local bus, express bus, heavy rail, light rail, commuter rail, etc.). Nonmotorized modes, which are not yet included in some models, especially in smaller urban areas, include walking and bicycling. Auto modes are often defined by occupancy levels (drive alone, shared ride with two occupants, etc.). When auto modes are not modeled separately, automobile occupancy factors are used to convert the auto person trips to vehicle trips prior to assignment. The outputs of the mode choice process include person trip tables by mode and purpose and auto vehicle trip tables.

4Time-of-day modeling is used to divide the daily trips into trips for various time periods, such as morning and afternoon peak periods, mid-day, and evening. This division may occur at any point between trip generation and trip assignment. Most four-step models that include the time-of-day step use fixed factors applied to daily trips by purpose, although more sophisticated time-of-day choice models are sometimes used. While the four-step process focuses on personal travel, commercial vehicle/freight travel is a significant component of travel in most urban areas and must also be considered in the model. While simple factoring methods applied to per- sonal travel trip tables are sometimes used, a better approach is to model such travel separately, creating truck/commercial vehicle trip tables. The final step in the four-step process is trip assignment. This step consists of separate highway and transit assignment processes. The highway assignment process routes vehicle trips from the origin-destination trip tables onto paths along Forecast Year Highway Network Forecast Year Transit Network Forecast Year Socioeconomic DataTrip Generation Model Internal Productions and Attractions by Purpose Trip Distribution Model Mode Choice Model Person and Vehicle Trip Tables by Purpose/Time Period Time of Day Model Person and Vehicle Trip Tables by Mode/Purpose/Time Period Highway Assignment CHECK: Input and output times consistent? Transit Assignment Highway Volumes/ Times by Time Period Transit Volumes/ Times by Time Period Input Data Model Output Model Component Decision Feedback Loop Yes No Truck Trip Generation and Distribution Models Production/Attraction Person Trip Tables by Purpose Truck Vehicle Trip Tables by Purpose Truck Time of Day Model Truck Vehicle Trip Tables by Time Period External Trip Generation and Distribution Models External Vehicle Trip Tables by Time Period Figure 1.1. Four-step modeling process.

5 the highway network, resulting in traffic volumes on network links by time of day and, perhaps, vehicle type. Speed and travel time estimates, which reflect the levels of congestion indicated by link volumes, are also output. The transit assignment process routes trips from the transit trip tables onto individual transit routes and links, resulting in transit line volumes and station/ stop boardings and alightings. Because of the simplification associated with and the resul- tant error introduced by the sequential process, there is some- times “feedback” introduced into the process, as indicated by the upward arrows in Figure 1.1 (Travel Model Improvement Program, 2009). Feedback of travel times is often required, particularly in congested areas (usually these are larger urban areas), where the levels of congestion, especially for forecast scenarios, may be unknown at the beginning of the process. An iterative process using output travel times is used to rerun the input steps until a convergence is reached between input and output times. Because simple iteration (using travel time outputs from one iteration directly as inputs into the next iteration) may not converge quickly (or at all), averaging of results among iterations is often employed. Alternative approaches include the method of successive averages, constant weights applied to each iteration, and the Evans algorithm (Evans, 1976). Although there are a few different methods for implement- ing the iterative feedback process, they do not employ param- eters that are transferable, and so feedback methods are not discussed in this report. However, analysts should be aware that many of the analysis procedures discussed in the report that use travel times as inputs (for example, trip distribution and mode choice) are affected by changes in travel times that may result from the use of feedback methods. 1.4 Summary of Techniques and Parameters Chapter 4 presents information on (1) the analytical tech- niques used in the various components of conventional travel demand models and (2) parameters for these mod- els obtained from typical models around the United States and from the 2009 NHTS. These parameters can be used by analysts for urban areas without sufficient local data to use in estimating model parameters and for areas that have already developed model parameters for reasonableness checking. While it is preferable to use model parameters that are based on local data, this may be impossible due to data or other resource limitations. In such cases, it is common practice to transfer parameters from other applicable models or data sets. Chapter 4 presents parameters that may be used in these cases, along with information about how these parameters can be used, and their limitations. 1.5 Model Validation and Reasonableness Checking Another important use of the information in this report will be for model validation and reasonableness checking. There are other recent sources for information on how the general process of model validation can be done. Chapter 5 provides basic guidance on model validation and reasonable- ness checking, with a specific focus on how to use the informa- tion in the report, particularly the information in Chapter 4. It is not intended to duplicate other reference material on validation but, rather, provide an overview on validation consistent with the other sources. 1.6 Advanced Travel Analysis Procedures The techniques and parameters discussed in this report focus on conventional modeling procedures (the four-step process). However, there have been many recent advances in travel modeling methods, and some urban areas, especially larger areas, have started to use more advanced approaches to modeling. Chapter 6 introduces concepts of advanced model- ing procedures, such as activity-based models, dynamic traffic assignment models, and traffic simulation models. It is not intended to provide comprehensive documentation of these advanced models but rather to describe how they work and how they differ from the conventional models discussed in the rest of the report. 1.7 Case Study Applications One of the valuable features in NCHRP Report 365 was the inclusion of a case study to illustrate the application of the parameters and techniques contained in it. In this report, two case studies are presented to illustrate the use of the information in two contexts: one for a smaller urban area and one for a larger urban area with a multimodal travel model. These case studies are presented in Chapter 7. 1.8 Glossary of Terms Used in This Report MPO—Metropolitan Planning Organization, the federally designated entity for transportation planning in an urban area. In most areas, the MPO is responsible for maintaining and running the travel model, although in some places, other agencies, such as the state department of transportation, may have that responsibility. In this report, the term “MPO” is sometimes used to refer to the agency responsible for the model, although it is recognized that, in some areas, this agency is not officially the MPO.

6Model area—The area covered by the travel demand model being referred to. Often, but not always, this is the area under the jurisdiction of the MPO. The boundary of the model area is referred to as the cordon. Trips that cross the cordon are called external trips; modeling of external trips is discussed in Section 4.6. Person trip—A one-way trip made by a person by any mode from an origin to a destination, usually assumed to be without stops. In many models, person trips are the units used in all model steps through mode choice. Person trips are the usual units in transit assignment, but person trips are converted to vehicle trips for highway assignment. Trip attraction—In four-step models, the trip end of a home-based trip that occurs at the nonhome location, or the destination end of a nonhome-based trip. Trip production—In four-step models, the trip end of a home-based trip that occurs at the home, or the origin end of a nonhome-based trip. Vehicle trip—A trip made by a motorized vehicle from an origin to a destination, usually assumed to be without stops. It may be associated with a more-than-one-person trip (for example, in a carpool). Vehicle trips are the usual units in highway assignment, sometimes categorized by the number of passengers per vehicle. In some models, vehicle trips are used as the units of travel throughout the modeling process. Motorized and nonmotorized trips—Motorized trips are the subset of person trips that are made by auto or transit, as opposed to walking or bicycling trips, which are referred to as nonmotorized trips. In-vehicle time—The total time on a person trip that is spent in a vehicle. For auto trips, this is the time spent in the auto and does not include walk access/egress time. For transit trips, this is the time spent in the transit vehicle and does not include walk access/egress time, wait time, or time spent transferring between vehicles. Usually, transit auto access/ egress time is considered in-vehicle time. Out-of-vehicle time—The total time on a person trip that is not spent in a vehicle. For auto trips, this is usually the walk access/egress time. For transit trips, this is the walk access/ egress time, wait time, and time spent transferring between vehicles. In some models, components of out-of-vehicle time are considered separately, while in others, a single out-of- vehicle time variable is used.

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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What is the future of travel?

All aboard! After the pandemic upended life and leisure as we know it, travel is roaring back. The industry is set to make a full recovery by the end of 2024, after losing 75 percent of its value in 2020. Much of this has been so-called “revenge travel,” or people embarking on international or bucket list trips that were delayed by the pandemic. But domestic travel is recovering quickly too and is set to represent 70 percent of travel spending by 2030.

Get to know and directly engage with senior McKinsey experts on travel and tourism

Margaux Constantin is a partner in McKinsey’s Dubai office, Matteo Pacca is a senior partner in the Paris office, and Vik Krishnan is a senior partner in the Bay Area office.

We’ve done a deep dive into the latest travel trends and how industry players can adjust accordingly in The state of travel and hospitality 2024 report. Check out the highlights below, as well as McKinsey’s insights on AI in travel, mass tourism, and much more.

Learn more about McKinsey’s Travel, Logistics, and Infrastructure Practice .

Who are today’s travelers, and what do they want?

In February and March 2024, McKinsey surveyed  more than 5,000 people in China, Germany, the United Arab Emirates (UAE), the United Kingdom, and the United States who had taken at least one leisure trip in the past two years. Here are six highlights from the results of that survey:

• Travel is a top priority, especially for younger generations. Sixty-six percent of travelers we surveyed said they are more interested in travel now than before the COVID-19 pandemic. And millennials and Gen Zers  are traveling more and spending a higher share of their income on travel than their older counterparts.
• Younger travelers are keen to travel abroad. Gen Zers and millennials who responded to our survey are planning nearly an equal number of international and domestic trips in 2024. Older generations are planning to take twice as many domestic trips.
• Baby boomers are willing to spend if they see value. Baby boomers still account for 20 percent of overall travel spending. They are willing to spend on comforts such as nonstop flights. On the other hand, they are more willing to forego experiences to save money while traveling, unlike Gen Zers who will cut all other expense categories before they trim experiences.
• Travel is a collective story, with destinations as the backdrop. Travelers both want to hear other travelers’ stories and share their own. Ninety-two percent of younger travelers were inspired by social media in some shape or form for their last trip.
• What travelers want depends on where they’re from. Sixty-nine percent of Chinese respondents said they plan to visit a famous sight on their next trip, versus the 20 percent of European and North American travelers who said the same. Respondents living in the UAE also favor iconic destinations, as well as shopping and outdoor activities.

Learn more about McKinsey’s  Travel, Logistics, and Infrastructure Practice .

What are the top three travel industry trends today?

Travel is back, but traveler flows are shifting. McKinsey has isolated three major themes for industry stakeholders to consider as they look ahead.

• The bulk of travel spending is close to home. Seventy-five percent of travel spend is domestic. The United States is currently the world’s largest domestic travel market, but China is set to overtake it in the coming years. Stakeholders should make sure they capture the full potential of domestic travelers before turning their attention abroad.
• New markets such as India, Southeast Asia, and Eastern Europe are growing sources of outbound tourism. Indians’ travel spending is expected to grow 9 percent per year between now and 2030; annual growth projections for Southeast Asians and Eastern Europeans are both around 7 percent.
• Unexpected destinations are finding new ways to lure travelers and establish themselves alongside enduring favorites. Rwanda, for example, has capitalized on sustainable tourism by limiting gorilla trekking permits and directing revenue toward conservation.

Looking for direct answers to other complex questions?

For a more in-depth look at these trends, check out McKinsey’s State of travel and hospitality 2024   report .

How will AI change how people travel?

In the 1950s, the introduction of the jet engine dramatically reduced travel times, changing the way people traveled forever. Now AI is upending the industry  in a similarly fundamental way. Industry players down to individual travelers are using advances in generative AI (gen AI) , machine learning , and deep learning  to reimagine what it means to plan, book, and experience travel. “It’s quite clear,” says McKinsey partner Vik Krishnan , “that gen AI significantly eases  the process of travel discovery.”

For travel companies, the task now is to rethink how they interact with customers, develop products and services, and manage operations in the age of AI. According to estimates by McKinsey Digital, companies that holistically address digital and analytics opportunities have the potential to see an earnings improvement of up to 25 percent .

McKinsey and Skift Research interviewed executives from 17 companies across five types of travel business. Here are three key findings on how travel companies can reckon with emerging technologies, drawn from the resulting report The promise of travel in the age of AI :

• Segmentation. Companies can use AI to create hyperspecific customer segments to guide how they interact with and serve customers. Segmentation can be based on a single macro characteristic (such as business versus leisure), or it can be so specific as to relate to just one customer.
• Surprise and delight. In the travel context, gen AI could take the form of digital assistants that interact with customers throughout their journeys, providing personalized trip itineraries and tailored recommendations and helping to resolve unexpected disruptions.
• Equipping workers better. AI tools can free up frontline workers’ time, allowing them to focus more on personal customer interactions. These tools can also shorten the training time for new hires and quickly upskill  the existing workforce.

AI is important, yes. But, according to Ella Alkalay Schreiber, general manager (GM) of fintech at Hopper, “The actual challenge is to understand the data, ask the right questions, read prediction versus actual, and do this in a timely manner. The actual challenge is the human thinking, the common sense .”

How is mass tourism changing travel?

More people are traveling than ever before. The most visited destinations are experiencing more concentrated flows of tourists ; 80 percent of travelers visit just 10 percent of the world’s tourist destinations. Mass tourism can encumber infrastructure, frustrate locals, and even harm the attractions that visitors came to see in the first place.

Tourism stakeholders can collectively look for better ways to handle visitor flows before they become overwhelming. Destinations should remain alert to early warning signs about high tourism concentration and work to maximize the benefits of tourism, while minimizing its negative impacts.

Destinations should remain alert to early warning signs about high tourism concentration and work to maximize the benefits of tourism, while minimizing its negative impacts.

For one thing, destinations should understand their carrying capacity of tourists—that means the specific number of visitors a destination can accommodate before harm is caused to its physical, economic, or sociocultural environment. Shutting down tourism once the carrying capacity is reached isn’t always possible—or advisable. Rather, destinations should focus on increasing carrying capacity to enable more growth.

Next, destinations should assess their readiness to handle mass tourism and choose funding sources and mechanisms that can address its impacts. Implementing permitting systems for individual attractions can help manage capacity and mitigate harm. Proceeds from tourism can be reinvested into local communities to ensure that residents are not solely responsible for repairing the wear and tear caused by visitors.

After risks and funding sources have been identified, destinations can prepare for growing tourist volumes in the following ways:

• Build and equip a tourism-ready workforce to deliver positive tourism experiences.
• Use data (gathered from governments, businesses, social media platforms, and other sources) to manage visitor flows.
• Be deliberate about which tourist segments to attract (business travelers, sports fans, party groups, et cetera), and tailor offerings and communications accordingly.
• Distribute visitor footfall across different areas, nudging tourists to visit less-trafficked locations, and during different times, promoting off-season travel.
• Be prepared for sudden, unexpected fluctuations triggered by viral social media and cultural trends.
• Preserve cultural and natural heritage. Engage locals, especially indigenous people, to find the balance between preservation and tourism.

How can the travel sector accelerate the net-zero transition?

Global warming is getting worse, and the travel sector contributes up to 11 percent of total carbon emissions. Many consumers are aware that travel is part of the problem, but they’re reticent to give up their trips: travel activity is expected to soar by 85 percent  from 2016 to 2030. Instead, they’re increasing pressure on companies in the travel sector to achieve net zero . It’s a tall order: the range of decarbonization technologies in the market is limited, and what’s available is expensive.

But decarbonization doesn’t have to be a loss-leading proposition. Here are four steps  travel companies can take toward decarbonization that can potentially create value:

• Identify and sequence decarbonization initiatives. Awareness of decarbonization levers is one thing; implementation is quite another. One useful tool to help develop an implementation plan is the marginal abatement cost curve pathway framework, which provides a cost-benefit analysis of individual decarbonization levers and phasing plans.
• Partner to accelerate decarbonization of business travel. Many organizations will reduce their business travel, which accounts for 30 percent of all travel spend. This represents an opportunity for travel companies to partner with corporate clients on decarbonization. Travel companies can support their partners in achieving their decarbonization goals by nudging corporate users to make more sustainable choices, while making reservations and providing data to help partners track their emissions.
• Close the ‘say–do’ gap among leisure travelers. One McKinsey survey indicates that 40 percent of travelers globally say they are willing to pay at least 2 percent more for carbon-neutral flights. But Skift’s latest consumer survey reveals that only 14 percent  of travelers said they actually paid more for sustainable travel options. Travel companies can help close this gap by making sustainable options more visible during booking and using behavioral science to encourage travelers to make sustainable purchases.
• Build new sustainable travel options for the future. The travel sector can proactively pioneer sustainable new products and services. Green business building will require companies to create special initiatives, led by teams empowered to experiment without the pressure of being immediately profitable.

What’s the future of air travel?

Air travel is becoming more seasonal, as leisure travel’s increasing share of the market creates more pronounced summer peaks. Airlines have responded by shifting their schedules to operate more routes at greater frequency during peak periods. But airlines have run into turbulence when adjusting to the new reality. Meeting summer demand means buying more aircraft and hiring more crew; come winter, these resources go unutilized, which lowers productivity . But when airlines don’t run more flights in the summer, they leave a lot of money on the table.

How can airlines respond to seasonality? Here are three approaches :

• Mitigate winter weakness by employing conventional pricing and revenue management techniques, as well as creative pricing approaches (including, for example, monitoring and quickly seizing on sudden travel demand spikes, such as those created by a period of unexpectedly sunny weather).
• Adapt to seasonality by moving crew training sessions to off-peak periods, encouraging employee holiday taking during trough months, and offering workers seasonal contracts. Airlines can also explore outsourcing of crew, aircraft, maintenance, and even insurance.
• Leverage summer strengths, ensuring that commercial contracts reflect summer’s higher margins.

How is the luxury travel space evolving?

Quickly. Luxury travelers are not who you might expect: many are under the age of 60 and not necessarily from Europe or the United States. Perhaps even more surprisingly, they are not all millionaires: 35 percent of luxury-travel spending is by travelers with net worths between $100,000 and $1 million. Members of this group are known as aspirational luxury travelers, and they have their own set of preferences. They might be willing to spend big on one aspect of their trip—a special meal or a single flight upgrade—but not on every travel component. They prefer visibly branded luxury and pay close attention to loyalty program points and benefits .

The luxury-hospitality space is projected to grow faster than any other segment, at 6 percent per year  through 2025. And competition for luxury hotels is intensifying too: customers now have the option of renting luxurious villas with staff, or booking nonluxury hotels with luxury accoutrements such as rainfall showerheads and mattress toppers.

Another critical evolution is that the modern consumer, in the luxury space and elsewhere, values experiences over tangible things (exhibit).

Luxury properties may see more return from investing in a culture of excellence—powered by staff who anticipate customer needs, exceed expectations, create cherished memories, and make it all feel seamless—than in marble floors and gold-plated bath fixtures. Here are a few ways luxury properties can foster a culture of excellence :

• Leaders should assume the role of chief culture officer. GMs of luxury properties should lead by example to help nurture a healthy and happy staff culture and listen and respond to staff concerns.
• Hire for personalities, not resumes. “You can teach someone how to set a table,” said one GM we interviewed, “but you can’t teach a positive disposition.”
• Celebrate and reward employees. Best-in-class service is about treating customers with generosity and care. Leaders in the service sector can model this behavior by treating employees similarly.
• Create a truly distinctive customer experience . McKinsey research has shown that the top factor influencing customer loyalty in the lodging sector is “an experience worth paying more for”—not the product. Train staff to focus on tiny details as well as major needs to deliver true personalization.

What’s the latest in travel loyalty programs?

Loyalty programs are big business . They’ve evolved past being simply ways to boost sales or strengthen customer relationships; now, for many travel companies, they are profit centers in their own right. One major development was that travel companies realized they could sell loyalty points in bulk to corporate partners, who in turn offered the points to their customers as rewards. In 2019, United’s MileagePlus loyalty program sold $3.8 billion worth of miles to third parties, which accounted for 12 percent of the airline’s total revenue for that year. In 2022, American Airlines’ loyalty program brought in $3.1 billion in revenue, and Marriott’s brought in $2.7 billion.

But as this transition has happened, travel players have shifted focus away from the original purpose of these programs. Travel companies are seeing these loyalty programs primarily as revenue generators, rather than ways to improve customer experiences . As a result, loyalty program members have become increasingly disloyal. Recent loyalty surveys conducted by McKinsey revealed a steep decline in the likelihood that a customer would recommend airline, hotel, and cruise line loyalty programs to a friend. The same surveys also found that airline loyalty programs are driving fewer customer behavior changes than they used to.

So how can travel brands win customers’ loyalty back? Here are three steps to consider:

• Put experience at the core of loyalty programs. According to our 2023 McKinsey Travel Loyalty Survey , American respondents said they feel more loyal to Amazon than to the top six travel players combined, despite the absence of any traditional loyalty program. One of the reasons for Amazon’s success may be the frictionless experience it provides customers. Companies should strive to design loyalty programs around experiential benefits that make travelers feel special and seamlessly integrate customer experiences between desktop, mobile, and physical locations.
• Use data to offer personalization  to members. Travel brands have had access to customer data for a long time. But many have yet to deploy it for maximum value. Companies can use personalization to tailor both experiences and offers for loyalty members; our research has shown that 78 percent  of consumers are more likely to make a repeat purchase when offered a personalized experience.
• Rethink partnerships. Traditionally, travel companies have partnered with banks to offer cobranded credit cards. But many credit card brands now offer their own, self-branded travel rewards ecosystems. These types of partnerships may have diminishing returns in the future. When rethinking partnerships, travel brands should seek to build richer connections with customers, while boosting engagement. Uber’s partnership with Marriott, for example, gives users the option to link the brands’ loyalty programs, tapping into two large customer bases and providing more convenient travel experiences.

In a changing travel ecosystem, travel brands will need to ask themselves some hard questions if they want to earn back their customers’ loyalty.

Learn more about McKinsey’s Travel, Logistics, and Infrastructure Practice . And check out travel-related job opportunities if you’re interested in working at McKinsey.

Articles referenced include:

• “ Updating perceptions about today’s luxury traveler ,” May 29, 2024, Caroline Tufft , Margaux Constantin , Matteo Pacca , and Ryan Mann
• “ The way we travel now ,” May 29, 2024, Caroline Tufft , Margaux Constantin , Matteo Pacca , and Ryan Mann
• “ Destination readiness: Preparing for the tourist flows of tomorrow ,” May 29, 2024, Caroline Tufft , Margaux Constantin , Matteo Pacca , and Ryan Mann
• “ How the world’s best hotels deliver exceptional customer experience ,” March 18, 2024, Ryan Mann , Ellen Scully, Matthew Straus, and Jillian Tellez Holub
• “ How airlines can handle busier summers—and comparatively quiet winters ,” January 8, 2024, Jaap Bouwer, Ludwig Hausmann , Nina Lind , Christophe Verstreken, and Stavros Xanthopoulos
• “ Travel invented loyalty as we know it. Now it’s time for reinvention. ,” November 15, 2023, Lidiya Chapple, Clay Cowan, Ellen Scully, and Jillian Tellez Holub
• “ What AI means for travel—now and in the future ,” November 2, 2023, Alex Cosmas  and Vik Krishnan
• “ The promise of travel in the age of AI ,” September 27, 2023, Susann Almasi, Alex Cosmas , Sam Cowan, and Ben Ellencweig
• “ The future of tourism: Bridging the labor gap enhancing customer experience ,” August 1, 2023, Urs Binggeli, Zi Chen, Steffen Köpke, and Jackey Yu
• “ Hotels in the 2030s: Perspectives from Accor’s C-suite ,” July 27, 2023, Aurélia Bettati
• “ Tourism in the metaverse: Can travel go virtual? ,” May 4, 2023, Margaux Constantin , Giuseppe Genovese, Kashiff Munawar, and Rebecca Stone
• “ Three innovations to solve hotel staffing shortages ,” April 3, 2023, Ryan Mann , Esteban Ramirez, and Matthew Straus
• “ Accelerating the transition to net-zero travel ,” September 20, 2022, Danielle Bozarth , Olivier Cheret, Vik Krishnan , Mackenzie Murphy, and Jules Seeley
• “ The six secrets of profitable airlines ,” June 28, 2022, Jaap Bouwer, Alex Dichter , Vik Krishnan , and Steve Saxon
• “ How to ‘ACE’ hospitality recruitment ,” June 23, 2022, Margaux Constantin , Steffen Köpke, and Joost Krämer
• “ Opportunities for industry leaders as new travelers take to the skies ,” April 5, 2022, Mishal Ahmad, Frederik Franz, Tomas Nauclér, and Daniel Riefer
• “ Rebooting customer experience to bring back the magic of travel ,” September 21, 2021, Vik Krishnan , Kevin Neher, Maurice Obeid , Ellen Scully, and Jules Seeley

Want to know more about the future of travel?

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

• Published: February 2000
• Volume 27 , pages 25–51, ( 2000 )

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• Ryuichi Kitamura 1 ,
• Cynthia Chen 2 ,
• Ram M. Pendyala 3 &
• Ravi Narayanan 4  

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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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Department of Civil Engineering Systems, Kyoto University, Sakyo-ku, Kyoto, 606-01, Japan

Ryuichi Kitamura

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Cynthia Chen

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Ram M. Pendyala

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Ravi Narayanan

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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.

• Complext networks
• Network science
• Travel demand

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• Civil and Structural Engineering
• Development
• Transportation

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• Graph Analysis Keyphrases 100%
• Complex Networks Keyphrases 100%
• Travel Demand Keyphrases 100%
• Urban Travel Demand Keyphrases 100%
• Network Perspective Keyphrases 100%
• Demand Patterns Keyphrases 100%
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T1 - A complex network perspective for characterizing urban travel demand patterns

T2 - graph theoretical analysis of large-scale origin–destination demand networks

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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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