Reservation reward-based approach for reducing energy consumption peaks in urban rail transit

Meiling Ding, Xin Guo, Wen-Long Shang, Jianjun Wu and Ziyou Gao

Applied Energy, 2025, vol. 384, issue C, No S0306261925001965

Abstract: In light of the swift expansion of urban rail transit systems, this paper addresses the challenges of reducing energy consumption using a reservation reward-based approach, thereby reducing the overall environmental footprint of rail operations. The approach encourages passengers to shift from peak to off-peak hours, reducing energy use and improving service quality. A multi-objective mixed-integer programming model with a pre-departure idea is proposed to evaluate energy consumption peaks while maintaining high levels of passenger service. To achieve this, a pre-departure strategy is embedded to encourage passengers to adjust their departure times from peak hours to off-peak hours. Secondly, a layered-sorted-based heuristic multi-objective optimization algorithm is designed to solve the model, demonstrating excellent convergence through all results. Finally, Computational results confirm the approach's effectiveness and provide valuable insights into key parameters affecting sustainability. This research supports sustainable urban transit by reducing energy peaks, enhancing efficiency, and minimizing environmental impacts.

Keywords: Urban rail transit; Energy consumption peaks; Reservation reward-based approach; Layered-sorted-based heuristic algorithm; High-intensity operation (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125466

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