Improving Ridership Projections of Proposed Bus and Rail Transit Projects

Raida, Afrida, Civil Engineering - School of Engineering and Applied Science, University of Virginia
Chen, Tong, EN-CEE, University of Virginia

Transit ridership data is 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. This study aimed to seek better ridership data by answering the following three questions, using Virginia-based data: (1) 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 to service frequency? (2) What percentage of transit ridership occurs during peak hours? (3) How does crowdsourced transit activity data compare to ridership data from Virginia transit agencies?

Study methods included extensive literature reviews to understand previous findings related to ridership effects of stop improvements and a before-after study using ridership data from one Virginia transit agency. Ridership data was also collected on an hourly basis for the year 2019 from six Virginia transit agencies to determine the percentage of ridership during peak travel hours. Generally, ridership data is challenging to obtain directly from transit agencies due to non-standardization of data collection processes among the agencies. 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 it against ridership data from Virginia transit agencies.

The results showed statistically significant increases (177%) in ridership when bus stop infrastructure was improved, compared to statistically insignificant increases of 27% where bus stops remained unchanged. The hourly ridership data from transit agencies showed that the 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 value was much higher, ranging from 37% to 56%. Directly using transit activity data from StreetLight’s current algorithm was deemed not to be appropriate without verifying against agency data, especially for agencies in small- to medium-sized cities.

Better transit ridership estimates can contribute towards better decision-making and more efficient funding allocation by state agencies. This study has also demonstrated the level of accuracy that can be expected from crowdsourced transit activity data sources when analyzing ridership data in small to medium sized cities.

MS (Master of Science)
Bus, Rail, Ridership, Transit
All rights reserved (no additional license for public reuse)
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