Integrated shared autonomous vehicles and bus bridging service for MRT disruption management

Document Type

Journal Article

Publication Date

2025

Subject Area

mode - mass transit, mode - bus, place - asia, place - urban, economics - operating costs, operations - capacity, ridership - demand, technology - automatic vehicle monitoring, technology - intelligent transport systems, planning - integration, planning - methods

Keywords

Shared autonomous vehicle, Bus bridging service, Resilience response, Disruption management, Column-and-constraint generation, Robust optimization

Abstract

The bus bridging service is one of the most widely used and effective methods to respond to mass rapid transit (MRT) disruptions. However, the traditional bus bridging services are typically criticized for their inability to provide customized services. Recently, the combination of autonomous driving technology and shared mobility has brought shared autonomous vehicles (SAVs), which have the advantages of being able to respond to real-time centralized dispatching and are suitable for transporting affected passengers during MRT disruptions. This paper introduces SAVs in the bus bridging service to overcome disadvantages of traditional bus bridging service. Specifically, buses are utilized to transport passengers gathered at disrupted MRT stations to nearby bus stops or operational MRT stations. Subsequently, passengers have the option to continue using the public transportation system or to choose SAVs to reach their destinations. As the exact values of passenger demand can hardly be observed and predicted when MRT disruptions happen, the investigated problem is formulated as a robust optimization model to determine the deployment of buses and SAVs, passenger flows, and SAV flows with uncertain demand. The objective is to minimize the sum of the operational cost of buses and SAVs and the financial penalty caused by unserved passengers. To solve the problem exactly, we reformulate the robust optimization model as a two-stage robust optimization model with recourse actions, and a column-and-constraint generation algorithm is developed. The proposed model and algorithm are tested on a case study in Beijing. The computational results show that introducing SAVs can bring significant benefits by reducing both the number of required buses and operational cost, and the proposed algorithm can find optimal solutions efficiently. Sensitivity analyses of several parameters, including robust parameters, numbers and capacities of buses and SAVs, and penalty coefficient, are performed to provide managerial insights.

Rights

Permission to publish the abstract has been given by Elsevier, copyright remains with them.

Comments

Transportation Research Part C Home Page:

http://www.sciencedirect.com/science/journal/0968090X

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