Mixed data and classification of transit stops

Abstract

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.

Understanding urban mobility patterns from a spatiotemporal perspective: daily ridership profiles of metro stations

“…Understanding urban mobility patterns, and exploring the relationship between daily travel rhythms and land use are important for city planners and transit authorities alike. These topics are essential for formulating effective policies and supporting scheduling, routing, and urban planning solutions (Kim et al, Kim et al 2016;Tupper et al 2016). Urban places are neither static nor homogeneous.…”