Transit-Based Evacuation for Urban Rail Transit Line Emergency

Abstract

Urban rail systems are the backbone of urban transit networks and are characterized by large passenger volumes, high speeds, punctuality, and low environmental impacts. However, unforeseen events such as rail transit line emergencies can lead to unexpected costs and delays. As a means of disruption management, we divide the decision support system for urban rail transit line emergency situations into two stages—transit-based evacuation and bus bridging management. This paper focuses on the transit-based evacuation under emergency scenarios on a single rail line. The model determines the vehicles and routes within traditional transit systems required to evacuate stranded passengers within a given time window. In addition, the proposed method ensures the reliability of traditional transit systems by considering the operating fleet and reserve fleet in the traditional transit systems. Therefore, the proposed optimization model is established with the objective of maximizing the total number of stranded passengers transferred within the given time window and headway constraint. Herein, we present the optimization model and solution method, and the proposed method is validated. The effectiveness of the proposed control method is evaluated in the Changchun urban transit network. By analyzing stranded passengers at stations under different numbers of vehicles and time periods, the results show that the proposed model can significantly provide routing arrangements to maximize the number of passengers evacuated from stations. The results are useful in the development of emergency evacuation plans to prevent secondary accidents and evacuate stranded passengers during a rail transit line emergency.

Influence of Evacuation Policy on Clearance Time under Large-Scale Chemical Accident: An Agent-Based Modeling

“…It is not surprising, but it highlights the necessity of utilizing public transit during the evacuation instead of private vehicles. Recent studies have shown that transit-based evacuation significantly reduced the clearance time [ 51 , 52 ]. Third, interventions for evacuation time periods can reduce overall clearance time since it disperses evacuation demands.…”

A Comprehensive Survey and Future Directions on Optimising Sustainable Urban Mobility

“…From the Asian applications, China was the country with more studies (comprising 48 articles [38,40,41,46,), from which Beijing stands out with 17 studies [62, 63, 65, 74, 80, 81, 83-85, 87, 88, 92-94, 96, 98, 102]. Real networks or case study applications may include universities' campi [36,[103][104][105][106][107][108], neighbourhoods and districts [46,80,95,98,102,109], or studies that only consider some transport lines/stations within a city or transport network [71,81,86,93,96,[110][111][112].…”

“…Some authors modelled more than one aspect, for example routes and frequencies setting [194], routes and vehicle scheduling of a customised bus service [89], routes and timetabling [187], frequencies setting and timetable development [193], and service area, frequencies, and vehicle scheduling [164]; Nesheli et al [127] focused on real-time operation aspects of bus systems, as vehicle holding time and boarding limit at stops, and also deciding if the vehicle could skip certain stops. Finally, for rail systems, studies focused on determining train timetables and rolling stock schedule [63], a conflict-free timetable for all trains running on a railway, considering multiplatform stations [172], frequencies setting and operational aspects (stop scheme and operation time) [93], rail scheduling and fleet management [100], and a transit-based evacuation plan for rail transit line emergencies [71].…”

Design and Research on the Intelligent System of Urban Rail Transit Project based on BIM+GIS

“…The stranded passenger's evaluation is done by the model which determines the vehicles and routes. The optimization model is proposed in this paper for maximizing the total number of stranded passengers within the given time window [16]. The validation of the proposed model and optimization model is done.…”