Mining commuting behavior of urban rail transit network by using association rules

Automated Fare Collection (AFC) systems in rail transit services collect enormous amounts of detailed data on on-board transactions. A better understanding of travelers’ commuting and transfer behavior based on those massive volumes of AFC data would enable the rail service operators to evaluate their service quality and optimize operation strategies. This paper proposes an efficient and effective data mining procedure to figure out the association rules, aiming to extract connectivity and correlation of passenger flow among different services lines in urban rail transit networks. A case study based on the Beijing Subway network is conducted to demonstrate the applicability of the proposed method. Using up to 28 million AFC smart card transaction data, we match and analyze travelers’ trip chains to investigate the commuting trip patterns in terms of spatio-temporal distribution characteristics. An innovational non-nigh-to-five commuting behavior and traditional nine-to-five commuting behavior are divided by the obtained associated rules to ensure a more nuanced description of commuting behaviors. Further, the results indicated by stronger association rules (2-frequent itemset and 3-frequent itemset) also provide a better understanding of transfer behaviors, like the frequent transfers among different service lines, and potentially vulnerable stations in the network. The research outcomes can be used to assist rail transit service operators in developing optimal operation strategies like timetabling design to enhance the transfer performance between different rail lines.