Spatiotemporal dynamics of traffic bottlenecks yields an early signal of heavy congestions

Jinxiao Duan, Guanwen Zeng, Nimrod Serok, Daqing Li, Efrat Blumenfeld Lieberthal (), Hai-Jun Huang () and Shlomo Havlin ()
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Jinxiao Duan: Beihang University
Guanwen Zeng: Bar-Ilan University
Nimrod Serok: Tel Aviv University
Daqing Li: Beihang University
Efrat Blumenfeld Lieberthal: Tel Aviv University
Shlomo Havlin: Bar-Ilan University

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: Abstract Heavy traffic jams are difficult to predict due to the complexity of traffic dynamics. Understanding the network dynamics of traffic bottlenecks can help avoid critical large traffic jams and improve overall traffic conditions. Here, we develop a method to forecast heavy congestions based on their early propagation stage. Our framework follows the network propagation and dissipation of the traffic jams originated from a bottleneck emergence, growth, and its recovery and disappearance. Based on large-scale urban traffic-speed data, we find that dissipation duration of jams follows approximately power-law distributions, and typically, traffic jams dissolve nearly twice slower than their growth. Importantly, we find that the growth speed, even at the first 15 minutes of a jam, is highly correlated with the maximal size of the jam. Our methodology can be applied in urban traffic control systems to forecast heavy traffic bottlenecks and prevent them before they propagate to large network congestions.

Date: 2023
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DOI: 10.1038/s41467-023-43591-7

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