Understanding travel behavior and its relationship with built environment is crucial for sustainable transportation and land-use policy-making. This study provides additional insights into the linkage between the built environment and travel mode choice by looking at the built environment characteristics at both the trip origin and destination in the context of transit-oriented development (TOD). The objective of this research is to provide a better understanding of how travel mode choice is influenced by the built environment surrounding both trip end locations. Specifically, it investigates the effect of transit-oriented development policy and the way it affects people’s mode choice decisions. This is accomplished by developing discrete choice models and consideration of urban form characteristics at both trip ends. Our findings not only confirmed the important role the built environment plays in influencing mode choice, but also highlighted the influence of policies, such as TOD, at both trip end locations. Results suggest that the probability of choosing transit and non-motorized modes is higher for trips originating and ending in TOD areas. However, the magnitude of this TOD effect is larger at trip origin compared to destination. Higher residential and employment densities at both trips ends are also associated with lower probability of auto and higher probability of transit and non-motorized mode choices.

Bikeshare programs in their current form have been in place for several years in many cities across the United States. Encouraging people to use bikeshare for their daily routine travel has numerous social, economic, environmental, and health benefits. Therefore, it is important to understand factors influencing bikeshare use in different urban areas to improve the system and encourage more use. This paper investigates how the built environment at both local and regional scales influences bikeshare use in seven large metropolitan areas in the U.S. The study areas include Boston, Chicago, Philadelphia, Minneapolis, San Francisco, San Jose, and Washington, D.C., and the data consists of about 12 million bike trips from approximately 2,000 stations over a one-year period. In addition to linear regression models built for each individual city for comparison purposes, a multi-level mixed effect regression model is built to predict the number of trips originated from each station with respect to the local and regional built environment pattern. The results are consistent with previous research on the effect of land use at the local level on bikeshare demand and show that residential density, regional diversity, pedestrian-oriented road network density, and job accessibility via transit all have a significant positive effect on bikeshare demand. At the regional level, results suggest that the overall level of mixed-use development and overall bike-friendliness in the region (i.e., exclusive bike routes, right-of-way, and bike facilities) and higher congestion level in the region are significant factors influencing bikeshare activities and demand. Models developed in this study could be applied to other communities that are seeking to improve and/or expand their bikeshare systems, as well as cities planning to launch new bikeshare programs.

For many years, attempts to measure the urban structure and physical form of metropolitan areas have been focused on a limited set of attributes, mostly density and density gradients. However, the complex nature of the urban form requires the consideration of many other dimensions to provide a comprehensive measure that includes all aspects of the urban structure and growth pattern at different hierarchical levels. In this paper, a multi-dimensional method of measuring urban form and development patterns in urban areas of the United States is presented. The methodology presented here develops several variables and indices that contribute to the characterization and quantification of the overall physical form of urban areas at various hierarchical levels. Cluster analysis is performed to group metropolitan areas based on their urban form and land-use pattern. This allows for a better utilization of land-use transportation planning and policy analyses used by planners and researchers. This clustering of urban areas could eventually help policymakers and decision makers in the decision-making process to evaluate land-use transportation policies, identify similar patterns, and understand how similar policies implemented in urban areas with similar urban form structure would result in more efficient and successful planning in the future.