https://doi.org/10.1140/epjds/s13688-024-00471-4
Research
Scaling law of real traffic jams under varying travel demand
Department of Electronic Engineering, Tsinghua University, Haidian District, Beijing, China
b
linyuming9@mail.tsinghua.edu.cn
Received:
12
September
2023
Accepted:
3
April
2024
Published online:
11
April
2024
The escalation of urban traffic congestion has reached a critical extent due to rapid urbanization, capturing considerable attention within urban science and transportation research. Although preceding studies have validated the scale-free distributions in spatio-temporal congestion clusters across cities, the influence of travel demand on that distribution has yet to be explored. Using a unique traffic dataset during the COVID-19 pandemic in Shanghai 2022, we present empirical evidence that travel demand plays a pivotal role in shaping the scaling laws of traffic congestion. We uncover a noteworthy negative linear correlation between the travel demand and the traffic resilience represented by scaling exponents of congestion cluster size and recovery duration. Additionally, we reveal that travel demand broadly dominates the scale of congestion in the form of scaling laws, including the aggregated volume of congestion clusters, the number of congestion clusters, and the number of congested roads. Subsequent micro-level analysis of congestion propagation also unveils that cascade diffusion determines the demand sensitivity of congestion, while other intrinsic components, namely spontaneous generation and dissipation, are rather stable. Our findings of traffic congestion under diverse travel demand can profoundly enrich our understanding of the scale-free nature of traffic congestion and provide insights into internal mechanisms of congestion propagation.
Key words: Traffic congestion / Travel demand / Scaling law / Complex systems
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjds/s13688-024-00471-4.
© The Author(s) 2024
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