Title

Recognizing metro-bus transfers from smart card data

Document Type

Journal Article

Publication Date

2019

Subject Area

mode - bus, mode - subway/metro, place - asia, place - urban, technology - passenger information, planning - methods

Keywords

Transit, metro-to-bus transfer, association rules, cluster analysis, smart card data, case study

Abstract

Transfer points between metro and bus services remain an elusive, yet critical junction for transportation practitioners. Based on massive Smart Card (SC) data, previous studies apply a one-size-fits-all criterion to discriminate between transfers. However, this is not sufficiently convincing for different transfer pairs. To counter this problem, this study applies an association rules algorithm and cluster analysis to recognize metro-to-bus transfers using SC data, and demonstrates transfer recognition in a case study based on SC data collected during a week in Nanjing, China. It is shown that 85% of the transfer-recognition results are quite stable through the whole week, and the median transfer time between metro and bus is below 20 min. The method proposed in this study can be used to identify the busiest transfer points and to obtain average transfer times, which facilitates a smarter and more efficient public transit network.

Rights

Permission to publish the abstract has been given by Taylor&Francis, copyright remains with them.

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