Real-Time Info Research

Real-time information tells transit riders when the next bus or next train will be arriving.  Here are some of my recent research projects that evaluate the impacts of real-time information on transit rider behavior and perceptions.

1. The Impact of Real-Time Information on Bus Ridership in New York City (2015).  Authors: Brakewood, Macfarlane and Watkins.
Summary: 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. 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. For more information, read the paper available here.

2. An Analysis of Commuter Rail Real-Time Information in Boston (2015). Authors: Brakewood, Rojas, Zegras, Watkins and Robin.
Summary: Prior studies have assessed the impacts of real-time information (RTI) provided to bus and heavy rail riders but not commuter rail passengers. The objective of this research is to investigate the benefits of providing commuter rail RTI. For more information, read the paper here.

iphone 13.  An Experiment Evaluating the Impacts of Real-Time Transit Information on Bus Riders in Tampa, Florida (2014). Authors: Brakewood, Barbeau and Watkins. 
Summary: The objective of this research is to quantify the benefits of real-time information (RTI) provided to bus riders. The method used is a behavioral experiment with a before-after control group design in which RTI is only provided to the experimental group. Web-based surveys are used to measure behavior, feeling, and satisfaction changes of bus riders in Tampa, Florida over a study period of approximately three months. For more details, read the paper here.

4. Quantifying the Impact of Real-Time Information on Transit Ridership (2014). Author: Brakewood. 
Summary: 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. For more information, read the dissertation here.