Exploring for Route Preferences of Subway Passengers Using Smart Card and Train Log Data

Abstract

As the mode share of the subway in Seoul has increased, the estimation of passenger travel routes has become a crucial issue to identify the congestion sections in the subway network. This paper aims to estimate the travel train of subway passengers in Seoul. The alternative routes are generated based on the train log data. The travel route is then estimated by the empirical cumulative distribution functions (ECDFs) of access time, egress time, and transfer time. The train choice probability is estimated for alternative train combinations and the train combination with the highest probability is assigned to the subway passenger. The estimated result is validated using the transfer gate data which are recorded on private subway lines. The result showed that the accuracy of the estimated travel train is shown to be 95.6%. The choice ratios for no-transfer, one-transfer, two-transfer, three-transfer, and four-transfer trips are estimated to be 53.9%, 37.7%, 6.5%, 1.5%, and 0.4%, respectively. Regarding the practical application, the passenger kilometers by lines are estimated with the travel route estimation of the whole network. As results of the passenger kilometer calculation, the passenger kilometer of the proposed algorithm is estimated to be 88,314 million passenger kilometer. The proposed algorithm estimates the passenger kilometer about 13% higher than the shortest path algorithm. This result implies that the passengers do not always prefer the shortest path and detour about 13% for their convenience.

Data-Driven Approach for Measuring and Managing Physical Distancing in Subways during Pandemic Conditions

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Exploring Transit Use during COVID-19 Based on XGB and SHAP Using Smart Card Data

“…e transit network in Seoul is operated with only a 100% smart card system without any other payment method, for example, cash and ticket, and the smart card data in Seoul provides 99% of transit users' trip information. us, it is widely used for microscopic user behavior analysis [16][17][18].…”

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