Early Warning Mechanism for the Surge of Passengers in Metro Systems Based on Automated Fare Collection Data: Case Study of Guangzhou, China

Abstract

Recently, surges of passengers caused by large gatherings, temporary traffic control measures, or other abnormal events have frequently occurred in metro systems. From the standpoint of the operation managers, the available information about these outside events is incomplete or delayed. Unlike regular peaks of commuting, those unforeseen surges pose great challenges to emergency organization and safety management. This study aims to assist managers in monitoring passenger flow in an intelligent manner so as to react promptly. Compared with the high cost of deploying multisensors, the widely adopted automated fare collection (AFC) system provides an economical solution for inflow monitoring from the application point of view. In this paper, a comprehensive framework for the early warning mechanism is established, including four major phases: data acquisition, preprocessing, off-line modeling, and on-line detection. For each station, passengers’ tapping-on records are gathered in real time, to be further transformed into a dynamic time series of inflow volumes. Then, a sequence decomposition model is formulated to highlight the anomaly by removing its inherent disturbances. Furthermore, a novel hybrid anomaly detection method is developed to monitor the variation of passenger flow, in which the features of inflow patterns are fully considered. The proposed method is tested by a numerical experiment, along with a real-world case study of Guangzhou metro. The results show that, for most cases, the response time for detection is within 5 min, which makes the surge phenomenon observable at an early stage and reminds managers to make interventions appropriately.

[Retracted] Analysis of Stadium Operation Risk Warning Model Based on Deep Confidence Neural Network Algorithm

“…Huan et al first introduced a spatiotemporal map representing human behavior. Then, a clustering algorithm is used to construct the input video as a spatiotemporal map [ 19 ]. Due to the nonrigidity of the constructed 3D spatiotemporal maps and the inherent differences between the temporal and spatial dimensions, traditional 3D feature analysis methods cannot be applied to spatiotemporal maps.…”

Metro system disruption management and substitute bus service: a systematic review and future directions

“…SB services are an important element for metro system disruption in many Asian countries, especially in China. Examples of their deployment include Beijing , Shanghai (Hu et al, 2016;Lu et al, 2019;, Guangzhou (Huan et al 2019), Nanjing (Wang et al, 2016), Hong Kong (Zhang andLo, 2018, 2020), Taipei (Lai and Chen, 2017), and Singapore (Jin et al, 2014(Jin et al, , 2015bOng, 2017, 2018). One possible reason for the prominence of SB for metro system disruption in these metro systems is that they are relatively new and hence their network coverages are relatively sparse in their stage of development, which do not offer much route and network redundancy to manage disruptions within their systems.…”