Data and codes for: 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, which can be solved analytically before and after the onset of congestion, and provide 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 f...交通拥堵的产生源于出行需求高峰对交通系统施加的负荷压力。因此，厘清需求空间分布、市民路径选择与底层基础设施之间的相互作用机制，对于精准定位拥堵热点区域并缓解交通延误至关重要。本研究构建了以路段（links）承担车辆通行处理功能的交通模型，该模型可在拥堵发生前后进行解析求解，为理解全局与局部拥堵的形成机制提供理论视角。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（Monte Carlo）模拟结果吻合度极高，且与12座城市在拥堵状态下实测的出行时间也具有合理的一致性。本研究框架可整合从真实轨迹数据中提取的任意类型路径选择策略，以更细致地刻画拥堵现象的演化过程，且可根据原文未完整表述的内容动态调整路段的通行容量。