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Data-driven traffic congestion patterns analysis: a case of Beijing

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Abstract

With the rapid increase of urban population and the number of motor vehicles, the traffic congestion problem is becoming more and more serious in megacities. This paper aims to identify the traffic congestion patterns and analyze their spatial–temporal variations by conducting cluster analysis for daily traffic congestion index curves. First, since the importance of sampling points in different time segments is not exactly the same, the coefficient of variation is taken to assign weight for improving K-means clustering algorithm. The improved weighted K-means clustering algorithm is proposed to identify the representative traffic congestion patterns. Second, the paired t-test method is used to analyze the spatial and temporal variations of traffic congestion patterns. Finally, case studies are conducted based on the real-life traffic congestion index data in Beijing, including over 670, 000 records covering six districts from January 1, 2017 to December 31, 2017. The results illustrate that traffic congestion patterns are both temporal dependent and spatial dependent, and the automobile license plate restriction has significant influence on the traffic congestion patterns. This study could be instructive for formulating specific traffic optimization and control schemes to alleviate congestion and promote the balance of traffic conditions.

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  1. [1] http://jtw.beijing.gov.cn/

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Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (Nos. 71722007 & 71931001), the Funds for First-class Discipline Construction (XK1802-5), the Key Program of NSFC-FRQSC Joint Project (NSFC No. 72061127002 and FRQSC No. 295837), the Fundamental Research Funds for the Central Universities (buctrc201926).

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Authors and Affiliations

  1. School of Economics and Management, Beijing University of Chemical Technology, Beijing, 100029, China

    Xiang Li & Jiao Gui

  2. School of International Economics and Management, Beijing Technology and Business University, Beijing, 100048, China

    Jiaming Liu

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  1. Xiang Li
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  2. Jiao Gui
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  3. Jiaming Liu
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Correspondence to Xiang Li.

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Li, X., Gui, J. & Liu, J. Data-driven traffic congestion patterns analysis: a case of Beijing. J Ambient Intell Human Comput 14, 9035–9048 (2023). https://doi.org/10.1007/s12652-022-04409-4

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