The determinants of travel demand between rail stations: A direct transit demand model using multilevel analysis for the Washington D.C. Metrorail system
place - north america, place - urban, mode - subway/metro, mode - bus, mode - car, planning - travel demand management, planning - service level, planning - methods, land use - impacts, land use - transit oriented development, technology - passenger information, ridership - demand
Metrorail transit, Direct transit demand model, Origin-destination ridership, Multilevel (hierarchical) modeling, Transit oriented development, Fare-card data
Transit demand models have become indispensable tools for transit planners and mangers in the 21st century. By quantifying the relationship between transit ridership, the cost of travel, the character of the built environment, and the socio-economic characteristics of riders, such models enable transit planners and managers to make more informed decisions regarding transit routes, levels of service, transit fares, transit oriented development (TOD) and other transit supply parameters. Direct ridership models (DRMs) are now able to address transit ridership at each station directly with higher sensitivity to built environmental characteristics in well-defined station areas. More recently Origin-Destination DRMs have begun to use data on ridership between each origin and destination pair to facilitate more precise estimation of transit demand by origin-destination pair.
In this study, we developed a time-of-day Origin-Destination Direct Transit Demand Model (OD-DTDM) that uses fare-card data from the Washington DC Metrorail system, applying a multilevel (or hierarchical) model to address the statistical problem due to the presence of groups or clusters of observations. We examine the research questions: (1) what are the determinants of transit demand between the origin and destination stations in the DC Metrorail system by time of day? and (2) what are the magnitudes of impacts that land use factors, as well as factors of fares and travel time of other modes, have on transit demand vary by time of day? To address statistical complexities introduced by the fact that each station represents both an origin and a destination, we applied multilevel (or hierarchical) modeling techniques. Using these techniques, we found that the number of households and the number of jobs within a walkshed serve as trip generating and attracting factors, respectively, in the AM peak period, but with higher positive coefficients for jobs; these two factors reverse their roles in the PM peak period. Other variables with substantial effects on ridership include transit fares per mile, travel time between OD-stations by car and by bus, parking capacity, the level of feeder bus service, and train service levels. While these findings are not surprising, the time-of-day OD-DTDM provides more detailed information regarding the determinants of transit demand with temporal variation, and enables transit planners and managers to adopt policies and plans, such as transit oriented development, fare structure, and service levels, more fine-tuned for each origin and destination pair and by time of day.
Permission to publish the abstract has been given by Elsevier, copyright remains with them.
Iseki, H., Liu, C., & Knaap, G. (2018). The determinants of travel demand between rail stations: A direct transit demand model using multilevel analysis for the Washington D.C. Metrorail system. Transportation Research Part A: Policy and Practice, Vol. 116, pp. 635-649.