Open AccessFactors associated with emerging multimodal transportation behavior in the San Francisco Bay Area
Author(s) -
Emily Wells,
Mitchell J. Small,
C. Anna Spurlock,
Gabrielle WongParodi
Publication year - 2021
Publication title -
environmental research: infrastructure and sustainability
Language(s) - English
Resource type - Journals
ISSN - 2634-4505
DOI - 10.1088/2634-4505/ac392f
Subject(s) - multimodality , sustainable transport , travel behavior , transport engineering , mode choice , mode (computer interface) , preference , built environment , transportation planning , computer science , public transport , sustainability , engineering , ecology , civil engineering , economics , world wide web , biology , microeconomics , operating system
This paper identifies the influence of demographic, local transportation environment, and individual preferences for transportation attributes on multimodal transportation behavior in an urban environment with emergent transportation mode availability. Multimodality is the use of more than one mode of transportation during a given timeframe. Multimodality has been considered a key component of sustainable and efficient transportation systems, as this travel behavior can represent a shift away from personal vehicle use to more sustainable transportation modes, especially in urban environments with diverse transportation systems and emergent shared transportation alternatives (e.g., carsharing, ridehailing, bike sharing). However, it is unclear what factors contribute towards people being more likely to exhibit multimodal transportation behavior in modern urban environments. We assessed commuting behavior based on a survey administered in the San Francisco Bay Area according to whether residents commuted (i) exclusively by vehicle, (ii) by a mix of vehicle and non-vehicle modes, or (iii) exclusively by non-vehicle modes. A classification tree approach identified correlations between commuting classes and demographic variables, preferences for transportation attributes, and location-based information. The characterization of commuting styles could inform regional transportation policy and design that aims to reduce vehicle use by identifying the demographic, preference, and location-based considerations correlated with each commuting style.