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On the estimation of arterial route travel time distribution with Markov chains

机译：用马尔可夫链估计动脉路径旅行时间分布

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摘要

Recent advances in the probe vehicle deployment offer an innovative prospect for research in arterial travel time estimation. Specifically, we focus on the estimation of probability distribution of arterial route travel time, which contains more information regarding arterial performance measurements and travel time reliability. One of the fundamental contributions of this work is the integration of travel time correlation of route's successive links within the methodology. In the proposed technique, given probe vehicles travel times of the traversing links, a two-dimensional (2D) diagram is established with data points representing travel times of a probe vehicle crossing two consecutive links. A heuristic grid clustering method is developed to cluster each 2D diagram to rectangular sub spaces (states) with regard to travel time homogeneity. By applying a Markov chain procedure, we integrate the correlation between states of 2D diagrams for successive links. We then compute the transition probabilities and link partial travel time distributions to obtain the arterial route travel time distribution. The procedure with various probe vehicle sample sizes is tested on two study sites with time dependent conditions, with field measurements and simulated data. The results are very close to the Markov chain procedure and more accurate once compared to the convolution of links travel time distributions for different levels of congestion, even for small penetration rates of probe vehicles.
机译：探测车部署的最新进展为动脉行程时间估计的研究提供了创新的前景。具体而言，我们专注于动脉路线旅行时间的概率分布的估计，其中包含有关动脉性能测量和旅行时间可靠性的更多信息。这项工作的基本贡献之一是将路线的连续链接的旅行时间相关性整合到方法中。在所提出的技术中，给定探测车辆的行进链路的行进时间，建立二维（2D）图，其中数据点代表探测车辆穿越两个连续链路的行进时间。开发了一种启发式网格聚类方法，以将每个2D图关于旅行时间均匀性聚类到矩形子空间（状态）。通过应用马尔可夫链过程，我们将2D图的状态之间的相关性整合为连续链接。然后，我们计算过渡概率并链接部分旅行时间分布，以获得动脉路线旅行时间分布。在两个与时间相关的条件下的研究站点上，使用实地测量和模拟数据对带有各种探测车样本大小的程序进行了测试。结果与马尔可夫链过程非常接近，并且与链结行进时间分布的卷积相比，即使在探测车辆的穿透率较低的情况下，对于不同程度的拥塞，其结果也更为精确。

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