Large-scale electric bus network transition planning via deep reinforcement learning

Document Type

Journal Article

Publication Date

2025

Subject Area

place - asia, place - urban, mode - bus, technology - alternative fuels, technology - emissions, infrastructure - fleet management, economics - subsidy, planning - methods, planning - integration

Keywords

Battery electric buses, Bus fleet transition, Charging facility planning, Deep reinforcement learning, Heterogeneous graph neural network

Abstract

Urban bus electrification is gaining global interest, playing a crucial role in reducing emissions. This study defines and addresses the electric bus network transition problem (EBNTP), jointly optimizing battery electric bus (BEB) fleet transitions and charging facility planning over a multi-period horizon. Existing research often neglects this interdependent long-term planning and lacks scalable solutions for large systems. This study proposes a deep reinforcement learning (DRL) approach, formulating EBNTP as a Markov Decision Process modeling sequential planning decisions, and introduces the DRL-HetGNN method, integrating heterogeneous graph neural networks (HetGNN) to capture network effects and enhance efficiency in large-scale applications. Using Hong Kong’s franchised bus system as a case study, DRL-HetGNN demonstrates superior performance and generalizability compared to benchmark methods. Scenario analyses explore budget allocations, independent operators, BEB subsidies, and price fluctuations, while examining policy-incentive mechanisms to accelerate electrification. The findings will support policymakers in planning sustainable public transportation systems.

Rights

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

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Transportation Research Part D Home Page:

http://www.sciencedirect.com/science/journal/13619209

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