Quantifying the impact of real-time information on transit ridership

Abstract

Public transit agencies often struggle with service reliability issues; when a bus or train does not arrive on time, passengers become frustrated and may be less likely to choose transit for future trips. To address reliability problems, transit authorities increasingly provide real-time vehicle location and arrival information to riders via web-enabled and mobile devices. Although prior studies have found several benefits of offering this information to passengers, researchers have had difficulty determining if real-time information affects ridership levels. Therefore, the objective of this dissertation is to quantify the impact of real-time information on public transit ridership. Statistical and econometric methods were used to analyze passenger behavior in three American cities that share a common real-time information platform: New York City, Tampa, and Atlanta. New York City was the setting for a natural experiment in which real-time bus information was gradually launched on a borough-by-borough basis over a three year period. Panel regression techniques were used to evaluate route-level bus ridership while controlling for changes in transit service, fares, local socioeconomic conditions, weather, and other factors. In Tampa, a behavioral experiment was performed with a before-after control group design in which access to real-time bus information was the treatment variable and web-based surveys measured behavior changes over a three month period. In Atlanta, a methodology to combine smart card fare collection data with web-based survey responses was developed to quantify changes in transit travel of individual riders in a before-after study. In summary, each study utilized different data sources and quantitative methods to assess changes in transit ridership. The results varied between cities and suggest that the impact of real-time information on transit travel is greatest in locations that have high levels of transit service. These findings have immediate implications for decision-makers at transit agencies, who often face pressure to increase ridership with limited resources.