A Data-Driven Approach to Detect Passenger Flow Anomaly Under Station Closure

Yuhang Wu,Xue Li,Baojing Huang,Yingnan Zhang,Xinyue Xu

doi:10.1109/access.2020.3016398

Abstract

During daily subway operation, station closure has large impact on subway system organization and has received increasing attention. This article proposes an anomaly detection method based on ensemble algorithms to determine the range of station closure influence on passenger flow. Firstly, Ensemble Algorithm I is developed to identify the stations with passenger flow volume anomaly and origin-destination (OD) pairs with volume anomaly. Secondly, Ensemble Algorithm II is proposed to identify the OD pairs with travel time anomaly. Then, the spatial variation in passenger flow caused by station closure, i.e. shift of passenger flow to neighboring stations and shift of path flow, is analyzed, and the spatial-temporal influence range of station closure is determined. A case study of the Beijing subway system is performed to illustrate the validity of the proposed method.Compared with sub algorithm of ensemble learning and KNN algorithm, Ensemble Algorithm I and II are more robust and have less misjudgment.

Highlights

Because the pressure of city traffic increases, subway is favored as an advanced urban rapid transit system
The main contributions of this article are summarized as follows: (1) A data-driven algorithm based on two ensemble algorithms is proposed to detect anomaly of passenger flow under station closure
Ensemble Algorithm II based on independent sample t-test, Wilcoxon signed rank test, and Mann-Whitney rank sum test is proposed to identify the anomalous OD pairs from the anomalous travel time perspective during station closure

Summary

INTRODUCTION

Because the pressure of city traffic increases, subway is favored as an advanced urban rapid transit system. (1) A data-driven algorithm based on two ensemble algorithms is proposed to detect anomaly of passenger flow under station closure. The first step is to conduct a Kolmogorov-Smirnov (K-S) test to determine the distributions of the inbound and outbound volumes; and the second step is to apply the anomaly identification algorithms based on the Poisson distribution, local outlier factor algorithm, and Grubbs criterion to identify the passenger flow anomalies and combine them by weighting to obtain the final result. A: ANOMALY TEST OF PASSENGER FLOW VOLUME BASED ON THE POISSON DISTRIBUTION Let IXiN and OXiN represent the sets of the inbound volume and outbound volume, respectively, at station i during the normal period.

ENSEMBLE ALGORITHM II

DISCUSSION The following topics should be focused on in further studies:

Findings

CONCLUSION