In an intelligent transportation system, the effects and relations of traffic flow at different points in a network are valuable features which can be exploited for control system design and traffic forecasting. In this paper, we define the notion of causality based on the directed information, a well-established data-driven measure, to represent the effective connectivity among nodes of a vehicular traffic network. This notion indicates whether the traffic flow at any given point affects another point's flow in the future and, more importantly, reveals the extent of this effect. In contrast with conventional methods to express connections in a network, it is not limited to linear models and normality conditions. In this work, directed information is used to determine the underlying graph structure of a network, denoted directed information graph, which expresses the causal relations among nodes in the network. We devise an algorithm to estimate the extent of the effects in each link and build the graph. The performance of the algorithm is then analyzed with synthetic data and real aggregated data of vehicular traffic.

中文翻译：

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车辆交通流量因果图

在智能交通系统中，网络中不同点的交通流的影响和关系是有价值的功能，可用于控制系统设计和交通预测。在本文中，我们基于定向信息（一种完善的数据驱动度量）定义因果关系的概念，以表示车辆交通网络节点之间的有效连通性。此概念表明将来任何给定点的交通流量是否会影响另一点的交通流量，更重要的是，它揭示了这种影响的程度。与表达网络中连接的常规方法相比，它不限于线性模型和正态条件。在这项工作中，定向信息用于确定网络的基础图结构，表示有向信息图，它表示网络中节点之间的因果关系。我们设计了一种算法来估计每个链接中影响的程度并构建图。然后使用车辆交通的合成数据和实际汇总数据分析算法的性能。