Dynamic Spatiotemporal Causality Analysis for Network Traffic Flow Based on Transfer Entropy and Sliding Window Approach

Senyan Yang,Mingyu Liu,Xilong Cai,Lianju Ning,Ronghui Zhang

doi:10.1155/2021/6616800

Abstract

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.

Highlights

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. e 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. is study proposes a dynamic spatiotemporal causality modeling approach to analyze traffic causal relationships for the large-scale road network
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. e 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
Introduction e rapid development of sensing and communication technologies in transportation promotes the accumulation of huge multisource spatiotemporal traffic data, which is collected by loop detectors, vehicle GPS, and mobile phones [1], presenting the characteristics of traffic big data. e valuable knowledge can be extracted from the huge observational spatiotemporal traffic data, which could be applied in the data-driven intelligent transportation systems (ITS) [2]

Summary

Literature Review

Spatiotemporal data mining approaches have been widely applied to traffic congestion propagation and prediction. Chu et al [13] proposed a time-varying dynamic Bayesian network for traffic causality modeling, studied the region macro structure based on vehicle trajectory data, and extracted the road junction dependency structure from sensor data. Li et al [16] developed a Granger causality-based causal dependence mining approach for traffic predictions and revealed the relationship between the road network structure and the correlation among traffic flow time series through causal dependence graph. Compared to the Granger causality method, transfer entropy does not need to assume the form of the causal relationship between variables, which is suitable for the long time series analysis of nonlinear systems, and has been widely applied in neuroscience [23], chemistry [24], finance [25], industrial processes [26], and so on. Transfer entropy can measure both the direction and quantity of information transmission, which is suitable for the nonlinear spatiotemporal causality modeling of network traffic flow

Methods

Spatiotemporal Causality Modeling for Network Traffic Flow

Experiments and Discussion