论文标题
识别拥塞热点的链接模型方法
A link model approach to identify congestion hotspots
论文作者
论文摘要
当高需求峰值使运输系统承受压力时，就会出现拥堵。因此，了解需求的空间组织，公民的路线选择与基础基础设施之间的相互作用对于找到拥堵热点并减轻延迟至关重要。在这里，我们开发了一个模型，链接负责处理交通拥堵之前和之后可以通过分析解决的车辆处理，从而提供了对全球和地方交通拥堵的见解。我们将我们的方法应用于合成和真实的运输网络，观察分析解决方案与蒙特卡洛模拟之间有着强烈的一致性，以及与拥挤阶段在12个城市观察到的旅行时间的合理一致性。我们的框架可以合并从实际轨迹数据中提取的任何类型的路由，以提供有关拥塞现象的更详细说明，并可用于根据车辆的流动或通过热点定价减少拥堵来动态适应道路段的能力。
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 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.
