This study investigates the factors affecting transit ridership at the stop level, including the effect of business establishment attributes. Using a large-scale passenger boarding and alighting data set, a mixed-effects hierarchical regression modeling technique is employed to capture the heterogeneity of various attributes considered in the weekday and weekend ridership analysis. Key findings reveal that population density significantly affects ridership, with higher values increasing ridership. Higher-income individuals are more likely to use personal vehicles, and the model shows a negative effect on ridership. Single-detached households, larger household sizes, and higher employment rates also have a negative effect on ridership, especially during the evening hours. Proximity to educational institutions and healthcare facilities is associated with lower ridership, and locations such as commercial banks, insurance agencies, parks, studios, consulting services, engineering services, and law offices show higher ridership. Proximity to highways, downtown, and more bus routes increases ridership, and longer wait times and parking facilities decrease it. This study also finds that professional and technical services, as well as dining out and weekend partying at restaurants, positively affect ridership. Retail and wholesale services negatively affect ridership, suggesting a preference for personal vehicles and active transportation modes. In addition, larger employee sizes contribute to increased ridership. Insights from this study, such as enhancing transit access in densely populated and low-income areas, fostering business development, providing more frequent and varied routes, improving infrastructure, reducing wait times, and increasing bus routes, can help policymakers substantially increase transit ridership. This study model can also predict future ridership based on business changes, which is important as e-commerce and remote work evolve in the post-pandemic era.
Nokshi et al. (Mon,) studied this question.