ArticleNature communications2023
Spatiotemporal dynamics of traffic bottlenecks yields an early signal of heavy congestions.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Design of robust networks via reinforcement learning prompts the emergence of multi-backbones.Nature communications · 2026Article
- Determination of the latent geometry of atorvastatin pharmacokinetics by transfer entropy to identify bottlenecks.BMC pharmacology & toxicology · 2025Article
- Social implications of coexistence of CAVs and human drivers in the context of route choice.Scientific reports · 2025Article
- Addressing the urban congestion challenge based on traffic bottlenecks.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2024Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.