Data-driven distributionally robust timetabling and dynamic-capacity allocation for automated bus systems with modular vehicles

Data-driven distributionally robust timetabling and dynamic-capacity allocation for automated bus systems with modular vehicles

Xia, D. (Beijing Jiaotong University)
Ma, Jihui (Beijing Jiaotong University)
Sharif Azadeh, S. (TU Delft Transport and Planning)
Zhang, Wenyi (Beijing Jiaotong University)

2023

The collaborative design of the timetable and dynamic-capacity allocation plan of emerging modular vehicles (MVs) is a promising solution to the mismatch between supply and demand in public transportation studies; however, such efforts are subject to high-level dynamics and uncertainty inherent in operating environments. In this study, we focus on the timetabling and dynamic-capacity allocation problem of MVs within the context of distributionally robust optimization under time-dependent demand uncertainty. The dynamic capacity refers to the number of modular units (MUs) comprising an MV can be potentially changed at different times and stops. A Wasserstein distance-based ambiguity set with a time-dependent and station-wise perturbation parameter is adopted to incorporate all potential distributions within a 1-Wasserstein distance for addressing the uncertainty of passenger demand. Further, a data-driven distributionally robust optimization model that considers time-varying capacity is formulated to minimize passenger waiting costs and dispatching costs of operators over all possible demand distributions within the ambiguity set. Subsequently, an expansion that allows for flexible formations of MVs assigned to each trip at each stop is proposed, and this results in more customized operational plans driven by the passenger demand. To improve the computational efficiency of realistic problems, we design a customized integer L-shaped method to exactly solve the models, which incorporates a class of valid equalities to further speed up the computation. The effectiveness of the proposed approaches in reducing the costs for both passengers and operators compared with the practical fixed-capacity operations is verified by real-world case studies based on the operating data of Beijing Bus Line 468. Furthermore, the superiority of the distributionally robust optimization method in comparison to the stochastic programming and the robust optimization approaches is demonstrated.

Bus timetabling
Distributionally robust optimization
Integer L-shaped method
Modular vehicles
Uncertainty and time-dependency

http://resolver.tudelft.nl/uuid:dcd0c533-b7e4-42cd-b570-67b3af6cd20b

https://doi.org/10.1016/j.trc.2023.104314

2024-03-01

0968-090X

Transportation Research. Part C: Emerging Technologies, 155

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2023 D. Xia, Jihui Ma, S. Sharif Azadeh, Wenyi Zhang