Integrated Urban Models for Simulation of Transit and Land-Use Policies Final Report (1998) / Chapter Skim
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Appendix A
Appendix A
Pages 199-226
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TCRP H-l ~ Final Report APPENDIX A THE TRANSPORTATIONS - LAND-USE INTERACTION EMPIRICAL FINDINGS . This Appendix was prepared with He assistance of Dr.
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Section A.! .4 then reviews simulation studies of urban form impacts on travel behavior.
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t~ 974] compared travel behavior in fifteen new communities that had mixed landuses, with fifteen "semi-planned' conuoT suburbs.
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Thus the effect of each variable is assessed without consideration of the impact of the remaining variables, or for any possible interactions among them. Clearly, the explicit role each vanable plays in explaining travel behavior can only be fully determined when all the variables and their interactions are simultaneously considered in the analytical framework.
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employs multiple regression analysis and the binary logit model to explore the association between several dimensions of urban form and travel behavior, after controlling for socioeconomic factors. Several variables describing land patterns, such as accessibility, land-use balance, mix and density are defined and tested in the mode]
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. Multiple linear regression and binary logit models were estimated to test for any association between trip-rates, mode choice and vehicle miles traveled respectively, and several factors describing the built environment.
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population density, employment density, and land-use mix are related to mode choice; population density, employment density, and land-use mix are related to mode choice when non-urban form factors are controlled for; a stronger relationship exists between mode choice and urban-form characteristics at both trip ends than at one trip end; and 4. the relationship between population density.
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to only travel-time by automobile. Thus, the level of transit service provided or network of ~alk-paths that determine Cracking distances to retail activity do not reflect in the re:,ional or local accessibility of a location.
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Rather the differences in travel characteristics are ascribed to the different sets of choices inherent in the form of the neighborhood Hat influences travel behavior. Thus urban form is evaluated in teens of the range arid nature of the choices inherent within it.
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examined how mixed land-uses and features of the built environment, like residential densities, influences the travel choices of residents from large metropolitan areas. The Gavel choices considered are the mode used for commuting, the commuting distance arid household vehicle ownership level.
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This latter study represents a good attempt at finding out what it is about people and households that results in the differences In travel behavior of residents of high- and Tow-densitv neighborhoods. However, there are concerns with some aspects of the study.
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The study fond that density explains more of the vanabilit T in transit use than land-use mix or urban design, stating that "density explained by ~ 0-20 times more, transit use for commute trips than land-use mix". In particulars residential density was found to erratic influence commuter mode choices, transit trips made per person arid rapid rail station boardings~ while station area employ ment density was fourth to influence We number of boardings at commuter rail stations.
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3. The size and scale of SEC activities appeared to influence mode choice.
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also examined relationships between the land-use - transportation system and travel behavior to determine if policies advocating land-use modifications were likely to promote travel behavior changes. Unlike several studies where residents are classified into two neighborhood types.
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Socioeconomic factors were included In the analysis in order to assess the role landuse factors have on travel behavior in an unambiguous fashion. McNally and Kulkarni found from the ANOVA results that neighborhood type was not a statistically significant factor in explaining the variation In household trip generation and mode choice.
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By using the household unit, it was possible to control for the effects of socioeconomic attributes (household income, autos per person household size. number of workers and housing Opel on travel behavior in the models developed.
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This has led to little being known about exactly what attributes of the built environment Influence travel behavior. and how important these built-environment attributes are relative to the other factors influencing travel.
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to identify the impact of urban design factors on access-mode choice after controlling for the individual characteristics of B ART users. Three binary logit models of access mode choice there developed to identify the factors influencing the choice of the walk mode.
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Parking capacity was found to have a negative impact on walk mode share, while proximity to an activity center had a positive effect on walk-share. To properly assess the effects of urban form factors on access mode choice, the aggregate station area characteristics and indix idual characteristics of respondents were combined in a single specification for mode]
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was calibrated using data from the UK National Travel Survey (NTS) , and a land-use pattern synthesized Mom UK Defacto Urban Areas population density data to fix the modal split parameter and the behavioral values of three and money for each mode and tup-pu~pose, prior to examining travel in the nine hypothetical areas.
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Although these simulation studies may yield useful insights of how different neighborhood- and street-designs may impact on travel patterns, they are based on simplifying assumptions of urban form and travel behavior, and hence their conclusions should be treated as unproven and speculative. A.~.S Problems and Some Directions for Future Work The empirical studies reviewed have all used cross-sectional data and models for their analysis.
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develops He theoretical framework for such models and their estimation, arid this could form the basis of fixture mode! development ~ the quest to better understand how- urban form impacts on travel behavior.
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A hedonic price function specified in the form of a multiple regression model, was used to estimate property values and the impact of proximity to rail stations. Two sets of data from different cities were employed in the study.
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concluded that the transport system has ~ strong influence on the attractiveness of residential locations. The authors found from their empirical study that being within walking distance of a light-rai]
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Toronto, one of the few North American cities in which transit has done relatively well, is orate of those reviewed. Significant urban development is reported by the authors to have taken place in the lands adjacent to the rapid transit corridor dunng the period of the transit systems staged development.
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Davies tI 974] examined the impact of the Yonge street subway line on population density, charges.
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TCRP H-12 Final Report A-26
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