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, Shlomo Havlin

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number8002
JournalNature Communications
Issue number1
StatePublished - 4 Dec 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s).


This work was supported by the National Natural Science Foundation of China (Grants 71890971/71890970, H.-J.H.; 72225012, D.L.; 72288101, H.-J.H. and D.L.; 71822101, D.L.; and 71890973/71890970, D.L.), the Fundamental Research Funds for the Central Universities (D.L.), the Israel Science Foundation (Grant No. 189/19, S.H.), the Binational Israel-China Science Foundation (Grant No. 3132/19, S.H.), and the European Union’s Horizon 2020 research and innovation program (DIT4Tram, Grant Agreement 953783, S.H. and E.B.L.).

FundersFunder number
Binational Israel-China Science Foundation3132/19
Horizon 2020 Framework Programme953783
National Natural Science Foundation of China72288101, 72225012, 71890973/71890970, 71890971/71890970, 71822101
Israel Science Foundation189/19
Fundamental Research Funds for the Central Universities


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