Ridership analysis at the stop level : case study of Austin, TX

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Park, Han

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Transit ridership analysis has been advancing towards the use of disaggregate spatial and boarding data. This study attempts to improve the understanding of factors influencing transit ridership by estimating/comparing ridership models at the route, the segmented route, and the stop level in the Austin area.

Spatial and statistic analysis methods are used in this study. The dependent variable is ridership at the transit route, the segmented route, and the stop level, whereas independent variables consist of traveler characteristics, land use, transit service characteristics, and other contextual factors. Spatial analysis is conducted using Geographic Information System (GIS) to compile data within a quarter-mile buffer from each transit stop, each segregated route, and each route. Linear and semi-log models of ridership are estimated using Statistical Analysis System (SAS). Initial analysis confirms the qualitative understanding that traveler demographics such as population and employment densities, ethnic background, and income significantly affect transit ridership. Land use composition, measured by the shares of single-family homes, multi-family homes, commercial, civic uses, as well as the total area of paved parking, all influence transit use. Service qualities such as headway and transfer opportunities also matter. Sensitivity tests of these factors affecting ridership are carried out to compare model performance among the route, segmented route, and the stop level analyses.

It is expected that the study findings will help to better inform transit agencies and local communities in optimizing existing transit operations, planning for new services, and developing transit-friendly environments.

Primary data were obtained from the Capital Metropolitan Transit Authority and the Census Bureau, and secondary data was processed by GIS analysis.



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