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Reliable route guidance: A case study from Chicago

机译：可靠的路线指引：芝加哥的案例研究

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摘要

Reliable route guidance can be obtained by solving the reliable a priori shortest path problem, which finds paths that maximize the probability of arriving on time. The goal of this paper is to demonstrate the benefits and applicability of such route guidance using a case study. An adaptive discretization scheme is first proposed to improve the efficiency in computing convolution, a time-consuming step used in the reliable routing algorithm to obtain path travel time distributions. Methods to construct link travel time distributions from real data in the case study are then discussed. Particularly, the travel time distributions on arterial streets are estimated from linear regression models calibrated from expressway data. Numerical experiments demonstrate that optimal paths are substantially affected by the reliability requirement in rush hours, and that reliable route guidance could generate up to 5-15% of travel time savings. The study also verifies that existing algorithms can solve large-scale problems within a reasonable amount of time.
机译：可以通过解决可靠的先验最短路径问题来获得可靠的路线引导，该先验最短路径问题可以找到使按时到达的可能性最大化的路径。本文的目的是通过案例研究来证明这种路线引导的好处和适用性。首先提出一种自适应离散化方案，以提高计算卷积的效率，这是可靠路由算法中用于获取路径行进时间分布的耗时步骤。然后讨论了在案例研究中根据实际数据构建链接旅行时间分布的方法。特别是，从高速公路数据中校准的线性回归模型可以估算出主干道上的行驶时间分布。数值实验表明，最佳交通路径在高峰时段受可靠性要求的影响很大，而可靠的路线引导最多可节省5-15％的旅行时间。该研究还验证了现有算法可以在合理的时间内解决大规模问题。

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