Unveiling city jam-prints of urban traffic based on jam patterns

The complexity of traffic jam is expected to increase significantly as cities expand. While existing studies have examined local bottlenecks and global traffic patterns, the daily evolution of traffic congestion is unclear yet crucial for understanding network-scale jam formation. We analyze the daily patterns of traffic jams in typical urban networks using real-world data based on a recently developed jam tree model. We extend the model by integrating additional realistic jam components into the model and find that, while the locations of traffic jams can vary significantly, the daily distribution of the costs associated with these jams follows a consistent pattern, i.e., a power law with similar exponents. This consistent pattern persists across different days within individual cities while exhibiting distinct variations between different cities, forming a unique signature we term “jam-prints”. Our findings are useful for assessing the quality of urban traffic and for establishing new traffic management goals. Urban traffic congestion reveals consistent daily patterns, termed “jam-prints”, across cities. By analyzing real-world data with an enhanced jam tree model, the authors identify consistent patterns in traffic jam costs.