Data correlation and causal analysis for traffic flow prediction

Abstract

Globally, traffic congestion has become a major issue due to several issues, including the rapid urban population increase, deteriorating infrastructure, improper and disorganized traffic signal timing, and a lack of real-time data. According to INRIX, a well-known provider of traffic data and analytics, the effects of this problem on U.S. travelers in 2017 were astronomical, totaling $305 billion in wasted fuel, lost time, and increased transportation costs in congested locations. Given the limitations of building new roads, communities must investigate cutting-edge tactics and technology to ease traffic while taking practical and economical restraints into account. This study employs the Granger causality test on a dataset of 48,120 entries, primarily focusing on the variables: number of VEHICLEs and number of intersection JUNCTIONs. The objective is to ascertain the potential mutual influence between these two variables. Initial results indicate a two-way Granger-causality between the variables, implying a feedback relationship. This discovery is fundamental in understanding traffic data dynamics and could be instrumental in enhancing traffic data prediction models.