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• 1. [Article] Multiday GPS Travel Behavior Data for Travel Analysis: The Effect of Day-to-Day Travel Time Variability on Auto Travel Choices

  This project explored the potential of archived multi-day GPS data to expand the understanding of travel-time reliability. While reliability is often observed and considered at the system or segment level, ...

  Citation × Citation Citation Abstract Copy Title: Multiday GPS Travel Behavior Data for Travel Analysis: The Effect of Day-to-Day Travel Time Variability on Auto Travel Choices Author: Dill, Jennifer, Broach, Joseph Paul, Deutsch-Burgne, Kate, Xu, Yanzhi, Guensler, Randall, Levinson, David, Tang, Wenyun Year: 2014 This project explored the potential of archived multi-day GPS data to expand the understanding of travel-time reliability. While reliability is often observed and considered at the system or segment level, travel-time uncertainty is also experienced at the household and trip level. Any move toward incorporating reliability into regional travel models will necessitate a re-examination of travel-time variation at more disaggregate levels. The analysis uses multiday vehicle-based GPS data analyzed within the Transportation Secure Data Center (TSDC). There were three major goals for the research. The first goal was to consider the ways in which multiday GPS data could be translated into data on reliability. The second goal was to explore relationships between trip- and household-level travel-time reliability and related trip, household, network, and urban location factors. The third goal involved use of the topic of reliability as a case study to test the usefulness of the TSDC in its current form for academic research. Title: Multiday GPS Travel Behavior Data for Travel Analysis: The Effect of Day-to-Day Travel Time Variability on Auto Travel Choices Author: Dill, Jennifer, Broach, Joseph Paul, Deutsch-Burgne, Kate, Xu, Yanzhi, Guensler, Randall, Levinson, David, Tang, Wenyun Year: 2014 This project explored the potential of archived multi-day GPS data to expand the understanding of travel-time reliability. While reliability is often observed and considered at the system or segment level, travel-time uncertainty is also experienced at the household and trip level. Any move toward incorporating reliability into regional travel models will necessitate a re-examination of travel-time variation at more disaggregate levels. The analysis uses multiday vehicle-based GPS data analyzed within the Transportation Secure Data Center (TSDC). There were three major goals for the research. The first goal was to consider the ways in which multiday GPS data could be translated into data on reliability. The second goal was to explore relationships between trip- and household-level travel-time reliability and related trip, household, network, and urban location factors. The third goal involved use of the topic of reliability as a case study to test the usefulness of the TSDC in its current form for academic research. Close

• 2. [Article] Travel Mode Choice Framework Incorporating Realistic Bike and Walk Routes

  For a number of reasons--congestion, public health, greenhouse gas emissions, energy use, demographic shifts, and community livability to name a few--the importance of walking and bicycling as transportation ...

  Citation × Citation Citation Abstract Copy Title: Travel Mode Choice Framework Incorporating Realistic Bike and Walk Routes Author: Broach, Joseph Paul Year: 2016 For a number of reasons--congestion, public health, greenhouse gas emissions, energy use, demographic shifts, and community livability to name a few--the importance of walking and bicycling as transportation options will only continue to increase. Currently, policy interest and infrastructure funding for nonmotorized modes far outstrip our ability to model bike and walk travel. To ensure scarce resources are used most effectively, accurate models sensitive to key policy variables are needed to support long-range planning and project evaluation, and to continue adding to our growing understanding of key factors driving walk and bike behavior. This research attempts to synthesize and advance the state of the art in trip-based, nonmotorized mode choice modeling. Over the past fifteen years, efforts to model the decision to walk or bike on a given trip have been hampered by the lack of a comprehensive behavioral framework and inconsistency in measurement scales and model specification. This project develops a mode choice behavioral framework that acknowledges the importance of attributes along the specific walk and bike routes that travelers are likely to consider, in addition to more traditional area-based measures of travel environments. The proposed framework is applied to a revealed preference, GPS-based travel dataset collected from 2010-2013 in Portland, Oregon. Measurement of nonmotorized trip distance, built environment, tour-level variables, and attitudinal attributes as well as mode availability are explicitly addressed. Route and mode choice models are specified using discrete choice techniques, and predicted walking and bicycling routes are tested as inputs to various mode choice models. Results suggest strong potential for predicted route measures to enhance walk and bicycle mode choice modeling. Findings also support the specific notion that bicycle and pedestrian infrastructure contribute not only to route choice but also to the choice of whether to bike or walk. For decisions to bicycle, availability of low-traffic routes may be particularly important to women. Model results further indicate that land use and built environments around trip ends and a person’s home still have important effects on nonmotorized travel when controlling for route quality. Both route and area travel environment impacts are mostly robust to the inclusion of residential self-selection variables, consistent with the idea that built environment differences matter even for households that choose to live in a walkable or bikeable neighborhood. The combination of area and route-based built environment measures alongside trip context, sociodemographic, and attitudinal attributes provides a new perspective on nonmotorized travel behavior relevant to both policy and practice. Title: Travel Mode Choice Framework Incorporating Realistic Bike and Walk Routes Author: Broach, Joseph Paul Year: 2016 For a number of reasons--congestion, public health, greenhouse gas emissions, energy use, demographic shifts, and community livability to name a few--the importance of walking and bicycling as transportation options will only continue to increase. Currently, policy interest and infrastructure funding for nonmotorized modes far outstrip our ability to model bike and walk travel. To ensure scarce resources are used most effectively, accurate models sensitive to key policy variables are needed to support long-range planning and project evaluation, and to continue adding to our growing understanding of key factors driving walk and bike behavior. This research attempts to synthesize and advance the state of the art in trip-based, nonmotorized mode choice modeling. Over the past fifteen years, efforts to model the decision to walk or bike on a given trip have been hampered by the lack of a comprehensive behavioral framework and inconsistency in measurement scales and model specification. This project develops a mode choice behavioral framework that acknowledges the importance of attributes along the specific walk and bike routes that travelers are likely to consider, in addition to more traditional area-based measures of travel environments. The proposed framework is applied to a revealed preference, GPS-based travel dataset collected from 2010-2013 in Portland, Oregon. Measurement of nonmotorized trip distance, built environment, tour-level variables, and attitudinal attributes as well as mode availability are explicitly addressed. Route and mode choice models are specified using discrete choice techniques, and predicted walking and bicycling routes are tested as inputs to various mode choice models. Results suggest strong potential for predicted route measures to enhance walk and bicycle mode choice modeling. Findings also support the specific notion that bicycle and pedestrian infrastructure contribute not only to route choice but also to the choice of whether to bike or walk. For decisions to bicycle, availability of low-traffic routes may be particularly important to women. Model results further indicate that land use and built environments around trip ends and a person’s home still have important effects on nonmotorized travel when controlling for route quality. Both route and area travel environment impacts are mostly robust to the inclusion of residential self-selection variables, consistent with the idea that built environment differences matter even for households that choose to live in a walkable or bikeable neighborhood. The combination of area and route-based built environment measures alongside trip context, sociodemographic, and attitudinal attributes provides a new perspective on nonmotorized travel behavior relevant to both policy and practice. Close