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Service-oriented distributionally robust lane reservation
Journal of Industrial Information Integration ( IF 15.7 ) Pub Date : 2021-11-14 , DOI: 10.1016/j.jii.2021.100302
Lisha Han 1 , Peng Wu 1 , Chengbin Chu 2
Affiliation  

Most existing lane reservation studies usually consider a static transportation network with assuming constant link travel times. However, in reality the link travel times are highly uncertain due to various factors such as weather, accidents, road maintenance, intersections, etc. Moreover, the precise link travel time probability distribution is usually difficult to be obtained. This paper studies a new stochastic bus lane reservation problem with partial link travel time information, i.e., only the mean and covariance matrix are known. The objective is to maximize the bus service level measured by the probability of the event that all lines are jointly scheduled on time. For the problem, we formulate a service-oriented distributionally robust optimization model. Its complexity is shown to be NP-hard. To solve the problem, a sample average approximation (SAA)-based method is first adapted. Since the SAA-based approach is computational expensive, a new approximated mixed integer second-order cone programming (MI-SOCP)-based approach is developed. Computational results on a real-life case show that the proposed MI-SOCP-based approach can efficiently obtain satisfactory solutions of high quality. Besides, our results indicate that the proposed model and algorithm can provide better solutions with higher service level, as compared with general stochastic models with known distributions and without considering service levels.



中文翻译:

面向服务的分布式稳健车道预留

大多数现有的车道保留研究通常考虑静态交通网络,并假设链路旅行时间恒定。然而,在现实中,由于天气、事故、道路维护、路口等多种因素,链路旅行时间具有高度不确定性,而且通常难以获得精确的链路旅行时间概率分布。本文研究了一种新的具有部分路段行程时间信息的随机公交专用道预留问题,即仅已知均值和协方差矩阵。目标是通过所有线路联合准时调度事件的概率来衡量公交服务水平。针对该问题,我们制定了面向服务的分布鲁棒优化模型。它的复杂性被证明是 NP 难的。为了解决问题,首先采用基于样本平均近似 (SAA) 的方法。由于基于 SAA 的方法计算成本很高,因此开发了一种新的基于近似混合整数二阶锥规划 (MI-SOCP) 的方法。真实案例的计算结果表明,所提出的基于 MI-SOCP 的方法可以有效地获得令人满意的高质量解决方案。此外,我们的结果表明,与已知分布且不考虑服务水平的一般随机模型相比,所提出的模型和算法可以提供更好的解决方案和更高的服务水平。真实案例的计算结果表明,所提出的基于 MI-SOCP 的方法可以有效地获得令人满意的高质量解决方案。此外,我们的结果表明,与已知分布且不考虑服务水平的一般随机模型相比,所提出的模型和算法可以提供更好的解决方案和更高的服务水平。真实案例的计算结果表明,所提出的基于 MI-SOCP 的方法可以有效地获得令人满意的高质量解决方案。此外,我们的结果表明,与已知分布且不考虑服务水平的一般随机模型相比,所提出的模型和算法可以提供更好的解决方案和更高的服务水平。

更新日期:2021-11-26
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