An Optimization Approach considering Passengers’ Space-Time Requirements for Bus Bridging Service under URT Disruption

Abstract

Rapid urbanization and growth of population in megacities generate severe pressures on urban rail transit (URT) system. The quantity and frequency of disruptive events have increased significantly, which might have obvious adverse impacts. A large number of passengers are stranded at disrupted URT station when a disruptive event occurs. One essential solution for passenger evacuation is the bus bridging service. This paper is aimed at addressing the passenger evacuation problem caused by a disruptive event in the URT network, by proposing a bus bridging service model considering the passengers’ space-time requirements. The model is proposed to minimize the waiting time of passengers and considers factors including bus service capacity limitations, bus stop parking capacity, and the maximum bridging time limit of a single bus. Buses are assumed to provide bridging service on either the local bus route or the direct bus route. The optimal routes and scheduling plans of bridging bus are designed. The model is applied to an example of a disruptive event in Shanghai URT line 9. The results of this example show that the proposed model is capable of reducing the waiting time of passengers and the number of buses used by 3.2% and 24.7%, compared with the traditional bus bridging service. Further analysis of the example shows that it is not a cost-effective solution to reserve a large number of buses for URT disruption. Decision-makers should comprehensively trade off between passengers’ space-time demands and monetary costs of bus bridging service.

Enhancing resilience assessment and bus bridging service design for large‐scale rail transit systems under disruptions

“…These inputs serve as the basis for generating a set of candidate bus bridging routes, followed by the allocation of available buses to the designed routes. The majority of existing studies related to bus bridging service design generate OD pairs and corresponding commuter demand via assuming they are determined 32,36,37 or subject to a certain distribution 33,34 or estimating from historical auto fare card data. 31,38 In the next step, the candidate bus bridging routes could be generated in a predefined way 32,34 or by more complicated approaches such as shortest path algorithm, 30 kth shortest path algorithm, 31,34 column generation, 38 and optimization model considering both the spacetime requirements of commuters and bus scheduling constraints.…”

“…The trajectories of these affected commuters under normal conditions can be obtained from decision variable $$l^\omega _{v,t}$$ and train timetable parameter $$I_{v,t,j}$$ simply. On the other hand, few papers have considered the commuter travel demand whose origin and destination happen to be the disrupted segment, 36 whereas our study considers all demand affected by the disruption event at the network level.…”