Original scientific paper

https://doi.org/10.17559/TV-20220903113056

Identifying Left Behind Passengers at Subway Stations from Auto Fare Collection Data

Lianghui Xie ; School of Economics and Management, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, P. R. China
Zhenji Zhang ; School of Economics and Management, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, P. R. China
Daqing Gong ; School of Economics and Management, Beijing Jiaotong University, No. 3 Shangyuancun, Haidian District, Beijing 100044, P. R. China

Abstract

With the rapid growth in transport demand, it has become a frequent occurrence that passengers are left behind especially during peak hours in subway, which has led to a significant reduction in the level of service. In this paper, we propose a left behind passengers identifying method based on Automatic Fare Collection (AFC) and Automated Vehicle Location (AVL) data. Firstly, we choose the passengers with the limited deterministic information as the research objects; secondly, we propose a classification-based method for identifying left behind passengers by the probabilistic model; next, the accuracy and effectiveness of the proposed method is verified by the simulation experiment and the case of Beijing Subway. Ultimately, the proposed method will support research related to the operation, management and future development of subways.

Keywords

Automatic Fare Collection (AFC); left behind; probabilistic model; subway; temporal distribution

Hrčak ID:

284910

URI

https://hrcak.srce.hr/284910

Publication date:

29.10.2022.

Visits: 543 *