With the rapid development of sensor and communication technologies, a large amount of spatiotemporal traffic data has been accumulated, presenting the characteristics of big data. The potential information and regularity of traffic state evolution can be extracted from the huge traffic flow time series data and applied to intelligent transportation systems. This study proposes a dynamic spatiotemporal causality modeling approach to analyze traffic causal relationships for the large-scale road network. Transfer entropy algorithm is utilized to detect the spatiotemporal causality of network traffic states based on the extensive traffic time series data, which could measure the amount and direction of information transmission. A combination of Gaussian kernel density estimation and sliding window approach is proposed to calculate the transfer entropy and construct dynamic spatiotemporal causality graphs based on the causality significance test. The indexes of affected coefficient, influence coefficient, input degree, and output degree are defined to evaluate the causal interaction of traffic states among different road segments and identify the critical roads and potential bottlenecks of the existing road network. Experimental results based on real-world traffic sensor data indicate that the structures of traffic causality graphs are time-varying; the traffic cause-effect interaction among different road segments during the peak time is more significant than that during the nonpeak time; and the critical road segments can be identified, which are mainly located at the intersections of arterial roads, undertaking the convergence and dispersion of large traffic flows.

中文翻译：

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基于传递熵和滑窗法的网络流量动态时空因果关系分析

随着传感器和通信技术的飞速发展，已经积累了大量的时空交通数据，呈现出大数据的特点。可以从巨大的交通流时间序列数据中提取交通状态演变的潜在信息和规律性，并将其应用于智能交通系统。这项研究提出了一种动态的时空因果关系建模方法来分析大规模路网的交通因果关系。利用传输熵算法基于大量的流量时间序列数据来检测网络流量状态的时空因果关系，可以测量信息传输的数量和方向。提出了高斯核密度估计和滑动窗口方法相结合的方法，用于计算传递熵，并基于因果显着性检验构造动态时空因果图。定义影响系数，影响系数，输入度和输出度的指标，以评估不同路段之间交通状态之间的因果关系，并确定关键路段和现有路网的潜在瓶颈。基于实际交通传感器数据的实验结果表明，交通因果图的结构是随时间变化的。高峰时段不同路段之间的交通因果相互作用比非高峰时段更显着；并可以确定关键路段，