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• 1. [Article] Understanding Travel Modes to Non-work Destinations: Analysis of an Establishment Survey in Portland, Oregon

  During the past three decades, research in travel behavior has generally proceeded from broad-level, aggregate analysis of mode share--the proportions of walking, bicycling, transit, and vehicle travel ...

  Citation × Citation Citation Abstract Copy Title: Understanding Travel Modes to Non-work Destinations: Analysis of an Establishment Survey in Portland, Oregon Author: Muhs, Christopher D. Year: 2013 During the past three decades, research in travel behavior has generally proceeded from broad-level, aggregate analysis of mode share--the proportions of walking, bicycling, transit, and vehicle travel occurring in traffic analysis zones, census tracts, neighborhood, or other geographical units--to fine-grained, disaggregate analysis of mode choices and other trip-making attributes at the individual level. One potential issue is whether there are differences in the types of conclusions drawn from results of analyses performed at these different levels, as these results directly inform transportation planning and policy. This thesis aims in part to confirm whether the types of conclusions drawn from different levels of analysis are different, and to what extent. We also examine the relationships between the built environment and non-work travel choices from a unique analysis perspective. To do this, we use data from a 2011 travel intercept survey in the Portland, Oregon metropolitan region that was administered at convenience store, bar, and restaurant establishments. We estimate, for each of the travel modes--walk, bicycle, and automobile--two analysis models: one binary logistic regression model for mode choice of the individual traveler going to the establishment and one multiple linear regression model for mode share of shoppers at the establishment. Both models control for socio-demographics, trip characteristics, and built environment measures of travelers. For the binary logistic regression models, the data are disaggregate and particular to the individual traveler. These models also controlled for attitudes and preference towards travel modes. For the multiple regression models, data are aggregated to the establishment. The built environment data in each model represent characteristics of urban form surrounding the establishment. The data being oriented to the destination-end of the trip, as well as providing controls on land use make this analysis unique in the literature, as most non-work travel studies use residential-based data. Results suggest that analyses performed at the two different levels provide policy-relevant but somewhat different conclusions. In general, characteristics of the individual and the trip have stronger associations with mode choices of individuals than when aggregated to the establishment and analyzed against the mode share patterns of shoppers. Instead, mode shares have stronger relationships with characteristics of the built environment. The built environment surrounding the destination has a much more pronounced association with mode shares at the establishment than with mode choices of individuals. The results highlight the usefulness of simple aggregate analysis, when appropriate. We also find large differences between modes in which characteristics are important for mode choice and mode share. Walking and automobile models behave somewhat similarly but in opposite directions, while bicycling behaves quite differently. These differences suggest on their own a move away from non-motorized travel to be considered as equivalent or assessed as one item in research and in practice. Title: Understanding Travel Modes to Non-work Destinations: Analysis of an Establishment Survey in Portland, Oregon Author: Muhs, Christopher D. Year: 2013 During the past three decades, research in travel behavior has generally proceeded from broad-level, aggregate analysis of mode share--the proportions of walking, bicycling, transit, and vehicle travel occurring in traffic analysis zones, census tracts, neighborhood, or other geographical units--to fine-grained, disaggregate analysis of mode choices and other trip-making attributes at the individual level. One potential issue is whether there are differences in the types of conclusions drawn from results of analyses performed at these different levels, as these results directly inform transportation planning and policy. This thesis aims in part to confirm whether the types of conclusions drawn from different levels of analysis are different, and to what extent. We also examine the relationships between the built environment and non-work travel choices from a unique analysis perspective. To do this, we use data from a 2011 travel intercept survey in the Portland, Oregon metropolitan region that was administered at convenience store, bar, and restaurant establishments. We estimate, for each of the travel modes--walk, bicycle, and automobile--two analysis models: one binary logistic regression model for mode choice of the individual traveler going to the establishment and one multiple linear regression model for mode share of shoppers at the establishment. Both models control for socio-demographics, trip characteristics, and built environment measures of travelers. For the binary logistic regression models, the data are disaggregate and particular to the individual traveler. These models also controlled for attitudes and preference towards travel modes. For the multiple regression models, data are aggregated to the establishment. The built environment data in each model represent characteristics of urban form surrounding the establishment. The data being oriented to the destination-end of the trip, as well as providing controls on land use make this analysis unique in the literature, as most non-work travel studies use residential-based data. Results suggest that analyses performed at the two different levels provide policy-relevant but somewhat different conclusions. In general, characteristics of the individual and the trip have stronger associations with mode choices of individuals than when aggregated to the establishment and analyzed against the mode share patterns of shoppers. Instead, mode shares have stronger relationships with characteristics of the built environment. The built environment surrounding the destination has a much more pronounced association with mode shares at the establishment than with mode choices of individuals. The results highlight the usefulness of simple aggregate analysis, when appropriate. We also find large differences between modes in which characteristics are important for mode choice and mode share. Walking and automobile models behave somewhat similarly but in opposite directions, while bicycling behaves quite differently. These differences suggest on their own a move away from non-motorized travel to be considered as equivalent or assessed as one item in research and in practice. Close