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Managing Spatial Graph Dependencies in Large Volumes of Traffic Data for Travel-Time Prediction

机译:管理大量交通数据中的空间图相关性以进行旅行时间预测

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The exploration of the potential correlations of traffic conditions between roads in large urban networks, which is of profound importance for achieving accurate traffic prediction, often implies high computational complexity due to the implicated network topology. Hence, focal methods are required for dealing with the urban network complexity, reducing the performance requirements that are associated to the classical network search techniques (e.g., Breadth First Search). This paper introduces a graph-theory-based technique for managing spatial dependence between roads of the same network. In particular, after representing the traffic network as a graph, the local neighbors of each road are extracted using Breadth First Search graph traversal algorithm and a lower complexity variant of it. A Pearson product–moment correlation-coefficient-based metric is applied on the selected graph nodes for a prescribed number of level sets of neighbors. In order to evaluate the impact of the new method to the traffic prediction accuracy achieved, the most correlated roads are used to build a STARIMA model, taking also into account the possible time delays of traffic conditions between the interrelated roads. The proposed technique is benchmarked using traffic data from two different cities: Berlin, Germany, and Thessaloniki, Greece. Benchmark results not only indicate significant improvement on the computational time required for calculating traffic correlation metric values but also reveal that a different variant works better in different network topologies, after comparison to third-party approaches.
机译:对大型城市网络中道路之间交通状况的潜在相关性的探索,对于实现准确的交通预测至关重要,但由于涉及网络拓扑结构,因此通常意味着较高的计算复杂性。因此,需要聚焦方法来处理城市网络的复杂性,从而降低与经典网络搜索技术(例如,广度优先搜索)相关的性能要求。本文介绍了一种基于图论的技术来管理同一网络的道路之间的空间依赖性。特别地,在将交通网络表示为图之后,使用广度优先搜索图遍历算法及其较低复杂度的变体来提取每条道路的本地邻居。对于指定数量的邻居级别集,将基于Pearson乘积-矩相关系数的度量应用于所选图节点。为了评估新方法对所达到的交通预测准确性的影响,使用了最相关的道路来构建STARIMA模型,同时还考虑了相关道路之间交通状况的可能时延。使用来自两个不同城市(德国柏林和希腊萨洛尼卡)的交通数据对所提出的技术进行基准测试。基准测试结果不仅表明计算流量相关度量值所需的计算时间有了显着改善,而且还表明与第三方方法相比,不同的变体在不同的网络拓扑中效果更好。

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