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Autonomous and conventional bus fleet optimization for fixed-route operations considering demand uncertainty
Transportation ( IF 4.3 ) Pub Date : 2020-10-28 , DOI: 10.1007/s11116-020-10146-4
Qingyun Tian , Yun Hui Lin , David Z. W. Wang

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.

中文翻译:

考虑需求不确定性的固定路线运营的自主和传统公交车队优化

自动驾驶汽车的新兴技术已被广泛认为是未来有前途的城市交通解决方案。本文考虑了自动驾驶汽车与公交系统的集成,并提出了一个建模框架,以确定最佳公交车队规模及其在考虑不确定需求的公交服务网络中的多条公交线路上的分配。应用混合整数随机规划方法来制定问题。我们应用样本平均逼近 (SAA) 方法来解决公式化的随机规划问题。为了解决 SAA 问题的非凸性,我们首先提出了一种将问题转化为混合整数二次二次规划 (MICQP) 的重构方法,可以通过使用一些现有的求解方法将其求解为其全局最优解。然而,这种基于 MICQP 的方法只能处理小规模的问题。对于问题规模较大的情况,我们采用二次变换和线性交替算法的方法,可以在合理的计算时间内有效地解决多达数千个场景的大规模实例。数值结果证明了引入自动驾驶公交车的好处,因为它们可以灵活地分配到不同的公交服务线路,尤其是在需求不确定性更为显着时。自动驾驶巴士的引入将进一步减少所需的车队和总成本。本研究提出的模型制定和解决方法可用于为公交运营商提供将自动驾驶公交车纳入公交服务的运营指导,
更新日期:2020-10-28
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