4 Travel and the Built Environment

Chapter Overview

Travel behavior is closely related to characteristics of the built environment. Extensive empirical evidence helps us understand the relationship and how changes to the built environment might influence the choice of travel mode. This chapter introduces readers to the components of travel behavior and key elements of the built environment and extends the discussion to urban sprawl and compact development. The correlation of travel behavior and the built environment is then explored along with uncertainties in the field of multimodal planning in relation to travel and the built environment.

Chapter Topics

  1. Travel Behavior
  2. Built Environment
  3. Correlating Travel Behavior and the Built Environment
  4. Limitations of Current Research

Learning Objectives

At the completion of this chapter, readers will be able to:

  • Describe dimensions of travel behavior
  • Identify elements used to the built environment (5D’s)
  • Explain the benefits of compact development
  • Demonstrate an understanding of the relationship between travel behavior and the built environment

Travel Behavior

Travel behavior involves a complex set of decisions. For example, when making a trip, each traveler will decide what mode to use (mode choice), which route to take, when to make the trip, and which destination(s) to visit. Other issues that influence travel behavior include, but are not limited to, automobile ownership, availability of parking, ease of navigating a particular route, congestion levels throughout the day, and how frequently the trip must be made. To plan an effective multimodal transportation system, it is important therefore to understand these various dimensions of travel behavior and how different travel decisions are made.

Terms and Measurements

Trip, Tour, Activity, Trajectory

A trip represents the departure by a traveler from an origin to a specific destination and is a common unit of travel behavior analysis. Tour is a series of trips, and activity is a series of tours in a given day. Trajectory refers to the tracked path of individuals or vehicles as they move in both space and time.

Travel Distance, Time, Cost, Purpose

Travel distance simply refers to the distance between an origin and a destination. Two types of travel distance are outlined in the literature ─ Euclidean and Manhattan (Figure 4. 1). Euclidean distance relates to Euclidean geometry and is represented as a straight line between two locations. Manhattan distance (also called taxicab geometry) is a more complex representation of distance that is measured along axes at right angles. It accounts for a grid street layout such as the one found in the Borough of Manhattan in New York City.

Figure outlining the difference of Euclidean distance and Manhattan distance.

Figure 4. 1. Euclidean distance (left) and Manhattan distance (right)
Source: Zgonc et al., 2019, CC BY 4.0

Travel time is usually a specified period spent traveling from an origin to a destination. Research suggests that on average, individuals typically travel about one hour per day during weekdays – a phenomenon known as a stable travel time expenditure – with greater variability on weekends (Stopher et al., 2017). Nonetheless, a variety of issues impact the time an individual will spend in travel, such as socio-demographic factors, income and employment status, trip purpose, travel mode options, and characteristics of the built environment at origins and destinations (e.g., density, land use mix, street connectivity) (Mokhtarian & Chen, 2004).

In transportation economics, the generalized cost of travel is considered a key determinant of the relative attractiveness of travel alternatives. Both monetary costs, such as transit fares or automobile value, and non-monetary costs, such as schedule delay, are factored into the generalized cost. The underlying assumption of mode choice modeling is that individuals minimize the generalized cost of travel in choosing travel modes. However, recent research has challenged this assumption, suggesting that generalized journey time must also be considered for an accurate assessment (Wardman & Toner, 2020).

Finally, people travel for different purposes and those purposes influence travel demand. Trip purposes are generally classified as commute or school trips, work-related trips, shopping trips, social-recreational trips, personal errand trips, exercise trips, and other. Scholars refer to the demand for travel that would not exist without a given purpose, such as travel to work or shopping, to be a “derived” demand. However, research suggests that travel is sometimes “desired for its own sake” and that this penchant for travel may reduce the influence of certain types of policies aimed at reducing vehicle miles of travel (Mokhtarian & Salomon, 2001).

Mode Choice

Mode choice refers to the process by which individuals decide which travel mode to use to access their destinations. A travel mode may include driving alone, carpooling, riding transit, walking, bicycling, riding an electric scooter, or some other means. Mode split refers to the estimated percentage of people or trips that will be accommodated by a given mode.

Experience has shown that providing alternatives to the automobile is not enough to entice people to use them. It is important to consider what influences an individual’s choice of mode. Variables found to have a significant influence on mode choice include, but are not limited to, car ownership, gender, distance, income, population wants and needs, and features of the built environment (Cao et al, 2007; Cervero, 2002; De Vos et al., 2016; Ko et al., 2019). Limtanakool et al. (2006) found that when controlling for socioeconomic characteristics, travel time and characteristics of the built environment explained much of the variation in mode choice for mid- to long-distance travel.

Public transportation providers are becoming more aware of the importance of considering the needs and desires of the community in attracting new users. For example, Honolulu Rail Transit in Hawaii includes areas to store surf boards in their commuter monorail system, much like other cities have space for bicycles. What may seem to be a small addition can entice more people to use public transportation systems for everyday activities.

Route Choice

Route choice refers to the process of selecting routes between origins and destinations. How best to assign trips to a particular route is an important consideration in roadway travel demand modelling (Yang & Bell, 1998). Using traditional four-step travel demand models, transportation planners attempt to estimate the number of vehicles expected to use each link of a network at different times of the day in relation to the capacity of that link based on the estimated mode split. The results of this process help in determining where congestion will occur, as well as costs and benefits of various modal options for accommodating the excess trips, such as adding new lanes versus increasing transit service.

Departure Time Choice

Another important component of the travel decision-making process is when trips will be made and especially workday departures. Empirical observations and experience indicate that congestion will occur during peak commuting periods; therefore, alternative work schedules, such as flexible work hours and compressed work weeks, are considered effective strategies to mitigate peak hour congestion. Recent studies further suggest that drivers rely on expected time savings to adjust travel plans and that monetary incentives can further influence when they choose to travel (Li et al., 2021).

Destination/Location Choice

Destination choice is a traveler’s decision of which destination to choose from multiple alternatives. Although destination choice may not apply to commuting trips, it does apply to other trip purposes, especially tourism and shopping. Tourism is a large part of transportation demand in popular destinations, such as Orlando, Tokyo, London, and New York City. Transportation systems can be designed to move tourists to popular destinations and integrate tourism and shopping into the local economy. For example, transit stations in tourist areas can be located to provide access to as many tourist attractions and related activities as possible. Locating stations near major shopping centers, popular restaurant rows, natural spaces, nightlife hot spots, and other multi-modal transportation stations can lead not only to more tourists, but also to more residents, choosing public transportation options.

Built Environment

As discussed in Chapter 1, land use and the built environment can increase or decrease automobile dependence. Effective multimodal planning and policy making addresses the land use characteristics needed to support public transportation, walking, and bicycling, making land use strategies integral to the multimodal plan. Built environment characteristics that support public transportation, walking, and bicycling have been characterized in the literature as the “five Ds” of development and are discussed in this section.

The 5Ds Framework

Exploration of the implications of the built environment on travel behavior led to a popular framework of measures that begin with the letter “D” (Ewing & Cervero, 2001). These measures now dominate much of the literature on this topic and are briefly summarized below (Ewing & Cervero, 2010):

  1. Density: population and employment by geographic unit (e.g., per unit of area).
  2. Diversity: number of land uses in an area and their relative representation in an area.
  3. Design: neighborhood layout and street characteristics, such as degree of connectivity, presence of sidewalks, and other design features (e.g., shade, scenery, pedestrian crossings) that enhance the pedestrian or bicycle environment.


  4. Destination accessibility: ease or convenience of trip destinations from origins, either regional or local, and often measured in terms of distance from the central business district or other major centers or in local terms such as distance to the nearest store.
  5. Distance to transit: average distance to transit station or stop in an area from home, work or other using other measures (e.g., distance between transit stops, number of stations per area, etc.).


Density in urban planning refers to the number of people living and working in a given urbanized area and the intensity of development to accommodate that human activity. Density is frequently measured using ratios of people or jobs to a given land area (e.g., dwelling units per acre, jobs per acre) or ratios of building intensity (e.g., floor area ratios). Adequate densities are particularly important to the efficiency of public transportation, as discussed in Chapter 7. Traditional zoning bylaws, developed in response to overcrowding in early industrial cities, have sought to control density and separate residential and non-residential land uses through maximum density and use restrictions. The widespread adoption of these zoning requirements in the U.S. facilitated population dispersion and low-density urban sprawl throughout the U.S., fueled by the advent of the automobile and supported by federal highway investment and housing programs.  Recent zoning practices experiment with minimum density thresholds in urban districts to curb urban sprawl and reinforce transit use.

Diversity (Land Use Mix)

Diversity relates to the mix of land uses in a given area with two or more major land use types (typically including residential, commercial, office, institutional, and recreational). The entropy index is commonly used in studies to evaluate land use mix and the relative balance of land uses in a given area (Ewing & Cervero, 2010; Kockelman, 1997).

Entropy\ ={\left(\left[\sum_{j=1}^{k}{P^j\ln{P^j}}\right]\right)}/{\ln{k}}
Where pj is the percentage of each land use type (j) in the area, k is the total number of land use types, and pj is calculated in terms of area.

Other commonly used indicators of land use diversity are jobs-housing ratios (total employment divided by total occupied housing units) and the dissimilarity index (measure of segregation between groups). Promoting compact, mixed-use, small-block, and infill developments can be effective in encouraging the use of sustainable transportation modes (walking, biking, and transit), increasing the frequency of intra-zonal travel, and reducing vehicle miles traveled (VMT) per person (L. Zhang et al., 2012). Land use strategies appear to influence travel more effectively, however, when complemented by pricing policies (W. Zhang, 2017).

Design (Street Connectivity)

Design as referenced in the 5 Ds framework largely relates to street connectivity ─ the relative number of connections or intersections among streets in an area. The density of intersections in a given area affects the distance one must travel to destinations in that area and therefore it’s overall accessibility. A traditional grid network, for example, is considered well-connected, whereas the loops and cul-de-sacs of conventional suburban developments are examples of disconnected networks. Various methods may be used to evaluate street connectivity, including a connectivity index where the number of street segments (links) in a given area is divided by the number of intersections or cul-de-sacs (nodes). An index of 1.4 or higher is a common threshold for multimodal planning (Williams & Seggerman, 2014). Areas with a dense street network, more intersections, and fewer dead-ends or cul-de-sacs offer better destination accessibility, which provides the basis for an efficient and convenient walking and biking environment and reduces vehicle travel (Litman, 2022a).

Distance to Transit (Transit Access)

Access to transit plays an important role for commuting in large cities. When people consider the commute implications of their potential move, they look at the access to different travel options that they would gain or lose by moving. Of all the options considered, access to transit is most often cited as important to location decisions. Proximity to rail stations or bus stops increases the likelihood that transit will be used as a primary travel mode. New technologies and services are expanding the reach of a person’s distance to transit. Micromobility systems, such as e-scooters and bikes, allow individuals to conveniently travel longer distances to transit stations, allowing transit systems to attract more users. Likewise, first mile and last mile systems are being employed in the private and public sector which provide car or bus trips for residents within a mile or so of rail transit stations.

Destination Accessibility (Regional Accessibility)

As discussed in Chapters 1 and 8, accessibility is an area-wide measure of the overall ease of travel between locations within a defined geographic area. It relates to both land use proximity and system connectivity. Accessibility impacts the time and money that people and businesses must devote to transportation and is influenced by factors, such as (Litman, 2022a):

  1. Mobility: Physical movement and quality of travel by different modes.
  2. Geographic proximity: The distance between destinations, as also influenced by the mix and density of land uses.
  3. System Connectivity: The density of modal networks and quality of connections between modes.

Urban Sprawl and Compact Development

Urban sprawl is perhaps best characterized by Ewing et al., (2003) as having the following dimensions:

…a population that is widely dispersed in low density development; rigidly separated homes, shops, and workplaces; a network of roads marked by huge blocks and poor access; and a lack of well-defined, thriving activity centers, such as downtowns and town centers. (p. 3)

These areas tend to have few travel choices other than the automobile and have resulted in land consumption far beyond what can be explained by population growth. A variety of factors contribute to urban sprawl in the U.S., such as traditional zoning that promotes low-density, single use areas and larger minimum lot sizes, residential segregation, and minimum parking requirements.

The opposite of urban sprawl would be compact development. Compact development can occur at a variety of scales and in different urban, suburban or rural contexts. The Urban Land Institute (2010) defines successful compact development as having the following characteristics:

  • Concentrations of population and/or employment
  • Medium to high densities appropriate to context
  • A mix of uses
  • Interconnected streets
  • Innovative and flexible approaches to parking
  • Pedestrian-, bicycle-, and transit-friendly design
  • Access and proximity to transit

Research has demonstrated that compact development with these characteristics do reduce vehicle miles of travel, although debates continue over the magnitude of that effect. In a commentary on the topic, Nelson (2017) uses a sprawl and infill-redevelopment scenario for Tucson to demonstrate how changes to the mean of the “D” variables based on urban planning and design strategies could significantly reduce overall vehicle miles of travel (VMT), despite large population increases.

Correlating Travel Behavior and the Built Environment

Travel behavior research examines how people travel and the ways travel behavior influences society. The way people access and experience the built environment and use the transportation system has a significant impact on quality of life. Due to the interrelatedness of these elements, research on the built environment has been integrated into multimodal transportation planning. This section examines areas of agreement in the literature, explores findings relative to compact development, and considers limitations of current research.

Many studies have correlated travel behavior and the built environment, and the “D” variables discussed above are frequently addressed in this literature. Two landmark studies in the field are a 2001 synthesis and a meta-analysis published in 2010 (Ewing & Cervero, 2001, 2010). The research found that (Ewing & Cervero, 2010):

  • Destination accessibility was the most statistically significant variable for reducing vehicle miles of travel (VMT). [Alternatively, poor accessibility and sprawling, single land use areas and/or strip development are defining characteristics of urban sprawl that contribute to increased VMT (Ewing et al., 2003)].
  • Distance to downtown was strongly correlated with VMT (i.e., the greater distance, the more VMT), as downtown cores offer a dense land use mix and better access to a variety of destinations.
  • Proximity of diverse land uses (especially jobs-housing balance and distance to stores) combined with intersection density were factors found to support walking.
  • Intersection density and street connectivity both have strong correlations with VMT.
  • Distance to a bus stop was found to be a key determinant of riding transit, followed by land use mix.

Despite the popularity of the “D” measures in multimodal analysis, Handy (2018) suggests that accessibility is the best measure for both research and practice. Planners increasingly agree as they shift from the traditional focus on regional mobility, to accessibility as the goal with a focus on access to services and activities (Litman, 2022a). The change in focus has altered how analysts evaluate transportation performance, reinforcing smart growth strategies such as compact urban development served by a variety of modes and services. As summarized by Manaugh and El-Geneidy (2012), ”[b]y favouring shorter travel distances and active modes of transportation, and by influencing household location choices, accessibility can also be used as a sustainability indicator and as a goal in land-use planning” (p. 15).

Denser and more connected street networks help reduce the walking and biking distance in the local environment, and hence provide more route options and better street accessibility to bus riders, and improve their first-mile and last-mile traveling experience. As more people ride transit, the frequency of service can be improved. Land use mix at transit origins and destinations makes it possible to efficiently meet one’s needs when using transit, as does linking transit with alternative modes when accessing bus stops.

Limitations of Current Research

Most studies of travel behavior span relatively short periods and therefore fail to capture how it may change over the course of a lifetime due to changes in employment, income, marital status, the birth of children, and retirement. These events cannot be easily captured using typical methods such as travel surveys, and research on long-term travel behavior changes throughout the life course is rare. Cross-sectional or before-after studies often incorporate life-course events through modeling, which fails to accommodate the influence of decision making on life events.

Self-selection bias is another consideration. Individuals may choose travel options based on their personal preferences rather than anything presented in the built environment. Ignoring self-selection may result in overestimating the effect of built environments in impacting travel choices. (Cao, 2014; Ettema & Nieuwenhuis, 2017; Guan & Wang, 2019).

Causality is also a concern in evaluating the relationship between the built environment (cause) and travel behavior (effect). For example, does travel behavior change because of the built environment or do travel preferences cause people to choose certain types of living environments? Do people use transit on a daily basis because the city is walkable and downtown parking is not affordable, or because they are weary of driving and enjoy walking and riding transit? As the decision-making process is not observed, the causality is difficult to determine using cross-sectional data. In a study of this topic, Handy, Cao and Mokhtarian (2005) found that although multivariate analysis of cross section data indicates that travel behavior relates more to attitudes, a quasi-longitudinal analysis showed clear evidence that travel behavior responds to changes in the built environment, despite differing attitudes toward travel among residents of suburban and traditional neighborhoods.


Key Takeaways

The built environment has an enormous impact on transportation and travel. It is up to the transportation system to connect what we build in our cities. Integrating the built environment with the transportation system helps to improve the city and make it a more desirable place to live, work and play. The effects of the built environment can be summarized into the 5D’s framework including density, distance to transit, design, destination accessibility, and diversity. These categories help explain the effect that the built environment has on transportation, what areas need special planning strategies, and how to better integrate a transportation network into the surrounding city.

Research and experience demonstrate that the built environment has an enormous impact on transportation and travel behavior. The transportation system must connect what we build in our cities. Integrating the built environment with the transportation system through multimodal planning helps to improve the city and make it a more desirable place to live, work, and play. The effects of the built environment can be summarized into the 5D’s framework including density, distance to transit, design, destination accessibility, and diversity. These categories help explain the effect that the built environment has on transportation, what areas need special planning strategies, and how to better integrate a transportation network into the surrounding city.

Key takeaways from this chapter are:

  • The built environment has an immense impact on transportation. These effects are explained in part by the 5D’s of the built environment.
  • Accessibility is an effective measure of the performance of multimodal transportation systems.
  • The 5D’s help planners and researchers correctly define and understand the aspects of the built environment that can potentially impact a developing transportation system. They influence decisions on how to better construct a transportation system in a given area.
  • Land use planning and zoning are tools planners can use to increase density and maximize transportation system ridership, creating less car dependent cities. These tools allow planners to influence diversity in land use, the distance people must travel to transit stations, the accessibility of destinations, and the design of the overall built environment.
  • Urban sprawl refers to the expansion of urban development as generally characterized by low-density housing, single-use zoning, and increased reliance on the private automobile for transportation. Increasing density combats sprawl.

NOTE: For further information on the topic of this chapter see “Land use impacts on transport: How land use factors affect travel behavior (Litman 2022b) available online at https://www.vtpi.org/landtravel.pdf.


Self Test



Activity: A series of tours in a given day.

Density: The number of people inhabiting a given urbanized area.

Destination choice: A traveler’s decision on which destination to choose from multiple alternatives.

Diversity: Relates to the mix of land uses in a given area with two or more major types of uses.

Mode choice: The process where the means of traveling (or travel mode) is determined.

Peak spreading: The tendency for travelers to change the time they make their trip as traffic conditions deteriorate, which often results in a wider and flatter peak period profile.

Route assignment: The selection of routes between origins and destinations in transportation networks (also referred to as route choice or traffic assignment).

Street connectivity: The directness of links and density of connections in street networks.

Tour: A series of trips.

Trajectory: The tracked path of individuals or vehicles moving in space and time.

Trip: The representation of a traveler departing from an origin to accessing a specific destination.

Urban sprawl: The rapid expansion of the geography of cities and towns, often characterized by low-density residential housing, single-use zoning, and increased reliance on the private automobile for transportation.



Cao, X. (2014). Residential self-selection in the relationships between the built environment and travel behavior: Introduction to the special issue. Journal of Transport & Land Use, 7(3), 1-3.

Cao, X., Mokhtarian, P. L., & Handy, S. L. (2007). Cross-sectional and quasi-panel explorations of the connection between the built environment and auto ownership. Environment and Planning A, 39(4), 830-847.

Cervero, R. (2002). Built environments and mode choice: toward a normative framework. Transportation Research Part D: Transport and Environment, 7(4), 265-284.

De Vos, J., Mokhtarian, P. L., Schwanen, T., Van Acker, V., & Witlox, F. (2016). Travel mode choice and travel satisfaction: bridging the gap between decision utility and experienced utility. Transportation, 43(5), 771-796.

Ettema, D., & Nieuwenhuis, R. (2017). Residential self-selection and travel behaviour: What are the effects of attitudes, reasons for location choice and the built environment? Journal of Transport Geography, 59, 146-155. doi:https://doi.org/10.1016/j.jtrangeo.2017.01.009

Ewing, R., & Cervero, R. (2001). Travel and the built environment: A synthesis. Transportation research record, 1780(1), 87-114. doi:10.3141/1780-10

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

Ewing, R., Pendall, R., & Chen, D. (2003). Measuring Sprawl and Its Transportation Impacts. Transportation Research Record, 1831(1), 175-183. doi:10.3141/1831-20

Guan, X., & Wang, D. (2019). Residential self-selection in the built environment-travel behavior connection: Whose self-selection? Transportation Research Part D: Transport and Environment, 67, 16-32. doi:https://doi.org/10.1016/j.trd.2018.10.015

Handy, S. (2018). Enough with the “D’s” already—Let’s get back to “A.” Transfers. https://transfersmagazine.org/enough-with-the-ds-already-lets-get-back-to-a/

Handy, S., Cao, X., Mokhtarian, P. (2005). Correlation or causality between the built environment and travel behavior? Evidence from Northern California,Transportation Research Part D: Transport and Environment, 10(6), 427-444.https://doi.org/10.1016/j.trd.2005.05.002.

Ko, J., Lee, S., & Byun, M. (2019). Exploring factors associated with commute mode choice: An application of city-level general social survey data. Transport Policy, 75, 36-46.

Kockelman, K. (1997). Travel Behavior as Function of Accessibility, Land Use Mixing, and Land Use Balance: Evidence from San Francisco Bay Area. Transportation Research Record, 1607(1), 116-125. doi:10.3141/1607-16

Li, T., Chen, P., & Tian, Y. (2021). Personalized incentive-based peak avoidance and drivers’ travel time-savings. Transport Policy, 100, 68-80.

Limtanakool, N., Dijst, M., & Schwanen, T. (2006). The influence of socioeconomic characteristics, land use and travel time considerations on mode choice for medium- and longer-distance trips. Journal of Transport Geography, 14(5), 327-341. https://doi.org/10.1016/j.jtrangeo.2005.06.004

Litman, T. (2022a). Evaluating accessibility for transport planning: Victoria Transport Policy Institute. https://www.vtpi.org/access.pdf

Litman, T. (2022b) Land use impacts on transport: How land use factors affect travel behavior. Victoria Transport Policy Institute. https://www.vtpi.org/landtravel.pdf

Manaugh, K. and El-Geniedy, M. (2012). What makes travel ‘local’: Defining and understanding local travel behavior. Journal of Transport and Land Use, 5(3), 15. doi:10.5198/jtlu.v5i3.300]

Mokhtarian, P. L., & Chen, C. (2004). TTB or not TTB, that is the question: a review and analysis of the empirical literature on travel time (and money) budgets. Transportation Research Part A: Policy and Practice, 38(9), 643-675. https://doi.org/10.1016/j.tra.2003.12.004

Mokhtarian P. L, and Salomon, I. (2001) How derived is the demand for travel? Some conceptual and measurement considerations, Transportation Research Part A: Policy and Practice, 35(8),  695-719. https://doi.org/10.1016/S0965-8564(00)00013-6.

Nelson, A.  (2017) Compact Development Reduces VMT: Evidence and Application for Planners—Comment on “Does Compact Development Make People Drive Less?”, Journal of the American Planning Association, 83(1), 36-41, DOI: 10.1080/01944363.2016.1246378

Stopher, P. R., Ahmed, A., & Liu, W. (2017). Travel time budgets: new evidence from multi-year, multi-day data. Transportation, 44(5), 1069-1082.

Urban Land Institute. (2010). Land Use and Driving The Role Compact Development Can Plan in Reducing Greenhouse Gas Emissions. Urban Land Institute. Washinton D.C.  https://uli.org/wp-content/uploads/ULI-Documents/Land-Use-and-Driving-Low-Res.pdf

Wardman, M., & Toner, J. (2020). Is generalised cost justified in travel demand analysis? Transportation, 47(1), 75-108. doi:10.1007/s11116-017-9850-7

Williams, K., & Seggerman, K. (2014). Multimodal transportation best practices and model element. https://www.nctr.usf.edu/wp-content/uploads/2015/08/77954.pdf

Yang, H., & Bell, M. G. (1998). Models and algorithms for road network design: a review and some new developments. Transport Reviews, 18(3), 257-278. doi:10.1080/01441649808717016

Zgonc, B., Tekavčič, M., & Jakšič, M. (2019). The impact of distance on mode choice in freight transport. European Transport Research Review, 11(1), 10. doi:10.1186/s12544-019-0346-8

Zhang, L., Hong, J., Nasri, A., & Shen, Q. (2012). How built environment affects travel behavior: A comparative analysis of the connections between land use and vehicle miles traveled in US cities. Journal of Transport & Land Use, 5(3), 40-52.

Zhang, W. & Zhang, M. (2017). Incorporating land use and pricing policies for reducing car dependence: Analytical framework and empirical evidence. Urban Studies. 55(13). https://doi.org/10.1177/0042098017720150




Icon for the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License

Multimodal Transportation Planning Copyright © 2024 by Peng Chen; Tia Boyd; and Kristine Williams is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, except where otherwise noted.

Share This Book