While many studies evaluate travel behavior associated with specific elements of transit-oriented development (TOD) with varying conclusions, most assess built environment factors in isolation, preventing a comprehensive understanding of the interrelated nature of the fabric of transit-oriented communities. Our research begins to address this gap by accounting for the “level of integration” between transit and the built environment. We aim to identify key factors associated with integration as a first step towards developing composite measures that account for level of integration. As a means of testing the level of integration concept, we also assess the importance of various integration factors in explaining the travel behavior of station area residents. We evaluate areas served by light rail transit in the Denver Metropolitan Area. In studying Denver, we provide much-needed insights into “second-generation” light rail systems in cities characterized by auto-dependence. We develop four final models in order to identify those variables that best explain four travel outcomes: car ownership, vehicle miles traveled, and use of LRT and alternative modes. The explanatory variables included in the models represent those that are most important for consideration in future measures of integration. Three variables appear to hold the most promise: Miles of bicycle facilities, pedestrian shed (the percent of the area within ½-mile of stations that can be walked along the network in ½-mile), and access to “other” amenities. Results of our analysis also clearly indicate that socio-demographic variables and self-selection effects must be accounted for in future investigations of the effects of level of integration. While the variables used in the present analysis are helpful in exploring the viability of a “level of integration” measure, they do not represent perfect measures. Future work will develop more nuanced composite measures of integration, and will test the effects of these measures on travel behavior using more sophisticated modeling techniques. Ultimately, we expect that station areas with characteristics representative of high levels of integration between transit and the built environment are more likely to foster positive travel outcomes.
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