Spatially-oriented data, methods, and models to plan transit for reverse commuters
ridership - commuting, ridership - demand, place - north america, planning - methods
reverse commuting, transit planning, demand
The reverse commuting population, those who travel from residences in the city to workplaces in the suburbs, are largely underserved by existing transit systems. One contributing factor to this limited service are issues in existing transit planning methods; serving the reverse commute with transit requires new methods and data sources that complement decision making based on ridership and revealed demand. We utilize administrative origin–destination data from the Philadelphia area that measures commuter flows across all travel modes, rather than just those who already use transit, to capture the potential demand for reverse commuting transit service. We develop an index to highlight target areas for new services, describe geographies for intervention, and utilize negative binomial regression models to analyze key covariates of the reverse commute. Our approach advances a new methodological framework that employs spatially-oriented analyses and open data and tools to generate actionable insights to better serve reverse commuters.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Davidson, J.H., Feiglin, I., & Ryerson, M.S. (2021). Spatially-oriented data, methods, and models to plan transit for reverse commuters. Transportation Research Part D: Transport and Environment, Vol. 100, 103051.