"A link model approach to identify congestion hotspots"

Congestion emerges when high demand peaks put transportation systems under stress. Understanding the interplay between the spatial organization of demand, the route choices of citizens, and the underlying infrastructures is thus crucial to locate congestion hotspots and mitigate the delay. Here we develop a model where links are responsible for the processing of vehicles, that can be solved analytically before and after the onset of congestion, and providing insights into the global and local congestion. We apply our method to synthetic and real transportation networks, observing a strong agreement between the analytical solutions and the Monte Carlo simulations, and a reasonable agreement with the travel times observed in 12~cities under congested phase. Our framework can incorporate any type of routing extracted from real trajectory data to provide a more detailed description of congestion phenomena, and could be used to dynamically adapt the capacity of road segments according to the flow of vehicles, or reduce congestion through hotspot pricing.当需求高峰给交通系统带来压力时，拥堵便会产生。因此，明晰需求空间布局、出行者的路径选择与底层基础设施之间的相互作用，对于定位拥堵热点并缓解延误至关重要。本研究构建了一个以道路路段承担车辆通行处理功能的模型，该模型可在拥堵发生前后进行解析求解，并能为全局与局部拥堵分析提供见解。我们将所提方法应用于人工合成与真实交通网络，结果显示解析解与蒙特卡洛模拟（Monte Carlo Simulations）结果吻合度极佳，且与12座左右城市在拥堵阶段观测到的出行时间也具备合理的一致性。本研究框架可整合从真实轨迹数据中提取的任意类型出行路径方案，以更细致地刻画拥堵现象；同时还可根据车流量动态调整路段通行能力，或通过拥堵热点定价策略缓解拥堵。