A data-driven system for cooperative-bus route planning based on generative adversarial network and metric learning

Jiguang Wang, Yilun Zhang, Xinjie Xing, Yuanzhu Zhan, Wai Kin Victor Chan () and Sunil Tiwari
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Jiguang Wang: Tsinghua University
Yilun Zhang: Shanghai Jiao Tong University
Xinjie Xing: University of Liverpool
Yuanzhu Zhan: University of Birmingham
Wai Kin Victor Chan: Tsinghua University
Sunil Tiwari: ESSCA School of Management

Annals of Operations Research, 2024, vol. 339, issue 1, No 17, 427-453

Abstract: Abstract Faced with dynamic and increasingly diversified public transport requirements, bus operators are urged to propose operational innovations to sustain their competitiveness. In particular, ordinary bus operations are heavily constrained by well-established route options, and it is challenging to accommodate dynamic passenger flows effectively and with a good level of resource utilization performance. Inspired by the philosophy of sharing economy, many of the available transport resources on the road, such as minibuses and private vehicles, can offer opportunities for improvement if they can be effectively incorporated and exploited. In this regard, this paper proposes a metric learning-based prediction algorithm which can effectively capture the demand pattern and designs a route planning optimizer to help bus operators effectively deploy fixed routing and cooperative buses with traffic dynamics. Through extensive numerical studies, the performance of our proposed metric learning-based Generative Adversarial Network (GAN) prediction model outperforms existing ways. The effectiveness and robustness of the prediction-supported routing planner are well demonstrated for a real-time case. Further, managerial insights with regard to travel time, bus fleet size, and customer service levels are revealed by various sensitivity analysis.

Keywords: Data-driven route planning; Generative adversarial network; Urban transportation system; Mixed-integer programming (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10479-022-04842-w

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