A Passenger Flow Risk Forecasting Algorithm for High-Speed Railway Transport Hub Based on Surveillance Sensor Networks

Zhengyu Xie,Yong Qin

doi:10.1155/2016/5647909

Abstract

Passenger flow risk forecasting is a vital task for safety management in high-speed railway transport hub. In this paper, we considered the passenger flow risk forecasting problem in high-speed railway transport hub. Based on the surveillance sensor networks, a passenger flow risk forecasting algorithm was developed based on spatial correlation. Computational results showed that the proposed forecasting approach was effective and significant for the high-speed railway transport hub.

Highlights

In the 12th Five-Year Plan (2011–2015) period, high-speed railway in China had a fast development
With the fast development of high-speed railway, high-speed railway transport hub has become a vital node of passenger transport networks and several transport modes, that is, civil aviation, highway, urban rail transit, and public transport transferred in high-speed railway transport hub
For the three types of sensors (Figure 1), our study mainly focuses on the passenger flow risk forecasting of key area

Summary

Introduction

In the 12th Five-Year Plan (2011–2015) period, high-speed railway in China had a fast development. Most of high-speed railway transport hubs have emergency plans for different risks, especially passenger flow risk. With wide application of intelligent video surveillance in high-speed railway transport hubs, a comprehensive surveillance sensor networks is gradually formed, which provides powerful supports for risk detecting and forecasting. Based on real-time passenger flow status obtained by surveillance sensor networks, passenger flow risk forecasting can effectively prevent risks, reduce risks value, and decrease the negative effects caused by risks. It is necessary for high-speed railway transport hubs to study on passenger flow risk forecasting approach based on surveillance sensor networks.

Literature Review

Passenger Flow Risk Forecasting Frame

A Passenger Flow Risk Forecasting Algorithm Based on Spatial Correlation

Computational Experiments

Conclusion