Improving Ridership Projections of Proposed Bus and Rail Transit Projects to Evaluate Congestion Reduction Effects

About the report:

Transit ridership data comprise one of the performance metrics examined when allocating funding to transportation projects, especially for those designed to reduce traffic congestion.  The better the quality of the data, the more efficient the project prioritization process. The purpose of this study was to obtain better ridership data by answering three questions using Virginia-based data: How is transit ridership affected by changes to infrastructure and transit service such as the addition of real-time information systems, shelters, and lighting or increases in service frequency?  What percentage of transit ridership occurs during peak hours of congestion?  How does crowdsourced transit activity data compare to ridership data from Virginia transit agencies? 

Study methods included conducting extensive literature reviews to determine previous findings related to ridership effects of stop improvements and then conducting a before-after study in Virginia using ridership data from one Virginia transit agency.  Ridership data were also collected on an hourly basis for year 2019 from six Virginia transit agencies to determine the percentage of ridership during peak travel hours.  Generally, ridership data are challenging to obtain directly from transit agencies because there is not a standardized process for data collection, storage, and sharing.  Crowdsourced big data platforms such as StreetLight promise easily accessible ridership-related data in standard formats.  To explore the value of such data, this study also examined the accuracy of StreetLight transit activity data by comparing them with ridership data from Virginia transit agencies and then calculating the root mean square error. 

The results for one Virginia transit agency documented in this study showed statistically significant increases (177%) in ridership where bus stop infrastructure was improved compared to statistically insignificant increases of 27% where bus stops were unchanged, but it is likely that improvements in bus frequency at some treated stops contributed to some portion of this increase. Literature searches found stop-level bus ridership increases ranging from 1.5% to 140% and route-level ridership increases of 2% when basic stop infrastructure was improved or added. The hourly ridership data from transit agencies showed that the peak hourly percentage of daily transit ridership for fixed-route services varied from 10% to 11% of daily ridership for buses and 14% to 26% for heavy rail transit.  For commuter rail services, this percentage was much higher, ranging from 37% to 56%.  Directly using transit activity data from StreetLight’s current algorithm was deemed to be inappropriate without verifying them with agency data, especially for agencies in small- to medium-sized cities such as those in most of Virginia. 

The study’s first two recommendations are for the Virginia Department of Rail and Public Transportation to consider the findings of this study if updating (1) the peak-hour ridership percentage used when scoring proposed fixed-route bus projects or (2) the percentage of ridership increase used when scoring proposed bus stop improvements in the form of shelters and benches.  Implementing both of these recommendations by adjusting parameters used in project scoring should result in improved project prioritization.  The third recommendation is for the Virginia Department of Rail and Public Transportation to consider the use of StreetLight transit activity data using the detailed instructions provided in this report.  This would ensure efficiency in the use of this data source and knowledge of the expected level of accuracy in its results.

Authors