Autonomous and conventional bus fleet optimization for fixed-route operations considering demand uncertainty
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Autonomous and conventional bus fleet optimization for fixed‑route operations considering demand uncertainty Qingyun Tian1 · Yun Hui Lin2 · David Z. W. Wang1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract The emerging technology of autonomous vehicles has been widely recognized as a promising urban mobility solution in the future. This paper considers the integration of autonomous vehicles into bus transit systems and proposes a modeling framework to determine the optimal bus fleet size and its assignment onto multiple bus lines in a bus service network considering uncertain demand. The mixed-integer stochastic programming approach is applied to formulate the problem. We apply the sample average approximation (SAA) method to solve the formulated stochastic programming problem. To tackle the nonconvexity of the SAA problem, we first present a reformulation method that transforms the problem into a mixed-integer conic quadratic program (MICQP), which can be solved to its global optimal solution by using some existing solution methods. However, this MICQP based approach can only handle the small-size problems. For the cases with large problem size, we apply the approach of quadratic transform with linear alternating algorithm, which allows for efficient solution to large-scale instances with up to thousands of scenarios in a reasonable computational time. Numerical results demonstrate the benefits of introducing autonomous buses as they are flexible to be assigned across different bus service lines, especially when demand uncertainty is more significant. The introduction of autonomous buses would enable further reduction of the required fleets and total cost.The model formulation and solution methods proposed in this study can be used to provide bus transit operators with operational guidance on including autonomous buses into bus services, especially on the autonomous and conventional bus fleets composition and allocation. Keywords Autonomous bus service · Demand uncertainty · Stochastic programming · Conic programming · Quadratic transform * David Z. W. Wang [email protected] Qingyun Tian [email protected] Yun Hui Lin [email protected] 1
School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
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Department of Industrial Systems Engineering and Management, National University of Singapore, 1 Engineering Drive 2, Blk E1A 06‑25, Singapore 117576, Singapore
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Transportation
Introduction Bus transportation companies usually operate a fleet of buses to serve multiple bus lines. A generic problem faced by transit operators is how to determine the optimal fleet size, as well as the best allocation of available bus fleet onto the service lines. This problem can be classified as a vehicle assignment problem (VAP) (Geetha and Nair 1993; Zak et al. 2009). On the tactic level, VAP deals with the interactive balance between bus service supply and demand. However, one intrinsic feature of publ
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