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首页> 外文期刊>European Journal of Operational Research >Robust routing, its price, and the tradeoff between routing robustness and travel time reliability in road networks
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Robust routing, its price, and the tradeoff between routing robustness and travel time reliability in road networks

机译:强大的路由,其价格以及路由鲁棒性与道路网络中旅行时间可靠性之间的权衡

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We propose in this article an adaptive algorithm for optimal and robust guidance for the users of the road networks. The algorithm is based on the Stochastic On Time Arrival (SOTA) family of routing algorithms, which is appropriate for taking into account the variability of travel times through the road networks. The SOTA approach permits the derivation of the maximum cumulative probability distribution of the time arrival toward a given destination in the network. Those distributions allow the selection of the most reliable origin-destination paths under given travel time budgets. We investigate here the introduction of robustness against link and path failures in the criterion of the guidance strategy selection. Our algorithm takes into account the reliability of itinerary travel times, since it is based on a SOTA approach. In addition, the algorithm takes into account itinerary robustness, by favoring itineraries with possible and reliable alternative diversions, in case of link failures, with respect to itineraries without or with less reliable alternatives. We first analyze the algorithm in its static version, without considering the traffic dynamics, and show some interesting properties. We then combine the robust guidance algorithm with a dynamic traffic model by using the traffic simulator SUMO (Simulation of Urban Mobility), and illustrate its effectiveness in some dynamic scenarios. (C) 2018 Elsevier B.V. All rights reserved.
机译:我们提出了本文的自适应算法,用于道路网络的用户的最佳和强大指导。该算法基于随机到达(SOTA)路由算法系列,这适合考虑通过道路网络的旅行时间的变化。 SOTA方法允许导出时间到达网络中给定目的地的时间的最大累积概率分布。这些分布允许在给定的旅行时间预算下选择最可靠的原始目的地路径。我们在此调查了引导策略选择标准中的鲁棒性和路径故障的引入。我们的算法考虑了行程旅行时间的可靠性,因为它基于SOTA方法。此外,该算法通过在不可能或不可靠的替代方案的情况下接受可能和可靠的替代转移的行程来考虑行程鲁棒性。我们首先在其静态版本中分析算法,而不考虑流量动态,并显示一些有趣的属性。然后,我们通过使用流量模拟器SUMO(城市移动性模拟)将强大的引导算法与动态流量模型组合,并说明了在某些动态方案中的有效性。 (c)2018年elestvier b.v.保留所有权利。

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