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<p>Battery electric buses (BEBs) face significant operational constraints that limit their flexibility, especially in rotating vehicles across multiple routes. This study focuses on addressing this limitation by introducing a strategic modelling approach that incorporates BEB rotation as a decision variable into an integrated planning optimization model. The proposed model jointly determines the optimal bus-to-route assignments, charging infrastructure siting and sizing, battery capacities, and charging schedules while accounting for electricity real-time pricing (RTP) rates, greenhouse gas (GHG) emissions charges, and battery degradation. Results of a real-world transit network demonstrate that enabling BEB rotation in the planning phase reduces total system costs by 37.88%, with a 12.18% reduction in capital costs and a 59.42% reduction in operational costs. Sensitivity analyses are conducted to validate the proposed model, highlighting the influence of varying key parameters, including energy consumption, infrastructure costs, charging power, electricity RTP rates, and GHG emissions charges on the optimized outcomes.</p>

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Advancing electric bus transit system optimization with bus rotation across routes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月22日

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  • Ali Shehabeldeen,
  • Moataz Mohamed

摘要

Battery electric buses (BEBs) face significant operational constraints that limit their flexibility, especially in rotating vehicles across multiple routes. This study focuses on addressing this limitation by introducing a strategic modelling approach that incorporates BEB rotation as a decision variable into an integrated planning optimization model. The proposed model jointly determines the optimal bus-to-route assignments, charging infrastructure siting and sizing, battery capacities, and charging schedules while accounting for electricity real-time pricing (RTP) rates, greenhouse gas (GHG) emissions charges, and battery degradation. Results of a real-world transit network demonstrate that enabling BEB rotation in the planning phase reduces total system costs by 37.88%, with a 12.18% reduction in capital costs and a 59.42% reduction in operational costs. Sensitivity analyses are conducted to validate the proposed model, highlighting the influence of varying key parameters, including energy consumption, infrastructure costs, charging power, electricity RTP rates, and GHG emissions charges on the optimized outcomes.