Panel regression modeling of bus ridership over a three-year period.
New York City, New York, United States
The impact of real-time information on bus ridership in New York City
Summary Information
Mobile applications have made it possible for public transit passengers to find routes and/or learn about the expected arrival time of their transit vehicles. Though these services are widely used, their impact on overall transit ridership remains unclear. The objective of this research is to assess the effect of real-time information provided via web-enabled and mobile devices on public transit ridership.
Methodology
An empirical evaluation is conducted for New York City, which is the setting of a natural experiment in which a real-time bus tracking system was gradually launched on a borough-by-borough basis beginning in 2011. Panel regression techniques are used to evaluate bus ridership over a three-year period, while controlling for changes in transit service, fares, local socioeconomic conditions, weather, and other factors.
Findings
A refined fixed effects model reveals the largest quartile of routes, defined by revenue miles of service, realized approximately 340 additional trips per route per weekday, a median increase of 2.3 percent per route.