Analysis of the use of smart cards on the urban railway

TL;DR

This paper analyzes smart card data to identify user behavior patterns at Moscow railway stations, providing insights that inform urban railway planning and reflect changes in the urban environment.

Contribution

It introduces a time series similarity analysis method to uncover usage patterns from smart card data, aiding urban railway design and urban environment monitoring.

Findings

Identified main user behavior patterns at railway stations

Demonstrated how usage patterns reflect urban environment changes

Provided data-driven insights for railway planning

Abstract

The article analyzes the patterns of use of railway stations in the Moscow region. The basis for the analysis is the data of smart cards on the entrances and exits of passengers. The technical tool is time series similarity analysis. As a result, the work identifies the main patterns of user behavior on the use of railway stations (railway transport). The results of the work were used in the design of new lines of urban railways. Obviously, the use patterns reflect the current state of the transport system and the urban environment. Accordingly, the recorded changes in usage patterns can serve as indicators and metrics for changes in the urban environment.