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Data correlation and causal analysis for traffic flow prediction
- 1 Beijing Union University, 97 East North Fourth Ring Road, Chaoyang District, Beijing, China
* Author to whom correspondence should be addressed.
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.
Keywords
Correlation Analysis, Granger Causality Test, Traffic Flow
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Cite this article
Lan,G. (2024). Data correlation and causal analysis for traffic flow prediction. Applied and Computational Engineering,33,49-56.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation
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