2016/11/12 by Laura L. Tupper, Tupper, Laura L., David S. Matteson +3
Mathematics · Social Sciences · #Applications (stat.AP) #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Transportation Planning and Optimization #Urban Transport and Accessibility #stat.AP
paper · pdf · doi:10.48550/arxiv.1611.04026
arxiv created 2016/11/12 · openalex publication_date 2016/11/12 · arxiv updated 2016/11/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
An analysis of the characteristics and behavior of individual bus stops can reveal clusters of similar stops, which can be of use in making routing and scheduling decisions, as well as determining what facilities to provide at each stop. This paper provides an exploratory analysis, including several possible clustering results, of a dataset provided by the Regional Transit Service of Rochester, NY. The dataset describes ridership on public buses, recording the time, location, and number of entering and exiting passengers each time a bus stops. A description of the overall behavior of bus ridership is followed by a stop-level analysis. We compare multiple measures of stop similarity, based on location, route information, and ridership volume over time.