Mixed Data and Classification of Transit Stops

TL;DR

This paper explores clustering of bus stops using ridership data and stop characteristics to inform transit planning, presenting multiple clustering approaches and analyzing their implications.

Contribution

It introduces a comprehensive exploratory analysis of bus stop data, comparing various similarity measures for clustering stops based on location, route, and ridership patterns.

Findings

Identification of stop clusters with similar ridership patterns

Comparison of different similarity measures for clustering

Insights into bus stop behavior and characteristics

Abstract

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.