Understanding Factors Associated with Commuter Rail Ridership: A Demand Elasticity Study of the GO Transit Rail Network

Abstract

Planners have theorized that transitioning commuter rail systems to regional rail networks will increase ridership, balance mode share, and reduce automobile use in North American cities. This process is currently underway in Ontario, Canada, as service is being expanded throughout the GO Transit commuter rail network. Calculating elasticities is a common approach used to identify factors that, if adapted, may significantly influence transit demand. However, few studies have focused on identifying demand elasticities specific to the current case of upgrading commuter rail systems in the North American context. The purpose of this study is to fill this gap. Station-level ridership data were compiled for the GO system from January 2016 to December 2019. Smartcard data were used to estimate station catchment areas for which land use, socioeconomic, and demographic datasets were developed. Data about additional factors related to station access, service quantity, fare price, and availability of substitute transport modes were also compiled. Controlling for trip type (e.g., a.m. peak and evening off-peak), demand models were estimated using a random effect panel data estimator. This study finds that service quantity, population density, fuel price, and unemployment rate were significantly associated with commuter rail ridership, regardless of trip type examined. Employment density and seasonal variation were also significant, although different signs were shown between models. The results suggest that plans for this kind of transition should include other considerations in addition to service quantity improvements. Those directed toward the implementation of transit-oriented developments, transport pricing schemes, and competitive fare price strategies are outlined.