IPOD Abstract for presentation (Poster or Podium)
Active Transportation (Bike/Ped)
Ruijie Bian, PhD (she/her/hers)
Research Assistant Professor
Louisiana Transportation Research Center, Louisiana State University
Baton Rouge, LA, United States
Ruijie Bian, PhD (she/her/hers)
Research Assistant Professor
Louisiana Transportation Research Center, Louisiana State University
Baton Rouge, LA, United States
Existing walking/biking data are often insufficient to support active transportation planning activities. This study used an emerging large-scale human mobility dataset to identify places where there are a higher number of short-distance trips to non-residential locations, which are more likely to be potentially served by active modes, given adequate infrastructure and network connectivity. Demographic variables were integrated into the mobility index design to prioritize access for more people and address equity concerns. In addition, a safety index (reflecting injurious crashes involving pedestrians/bicyclists) and a connectivity index (reflecting the density of existing active transportation infrastructure) were also calculated and considered together with the mobility index in generating an active transportation investment potential score. The scores are statewide standardized values, for which a higher score reflects greater safety improvement needs (i.e., more injuries/fatalities), greater mobility needs (i.e., more short-distance trips), and lower network density (i.e., inhibiting current demand). All the indices/scores were generated at both hexagon (in 0.1 km^2) and segment (in 0.1-mile) level, then mapped and published on an online dashboard for public access. Over 100 equity and contextual indicators were included in the same dashboard to assist various needs. The developed dashboard is expected to support statewide active transportation planning and project selection/prioritization decision-making activities. The proposed methodology is based on data sources that are available to public agencies and should be replicable in any U.S. states that have few active transportation facilities and where pedestrian/bicyclist count data are not sufficient to directly measure or model demand.