【摘要】 随着城市化的持续推进，交通拥堵问题日益突出，准确识别路网瓶颈是缓解拥堵、提升交通运行效率的关键前提。传统方法常依赖静态拓扑指标和预设阈值，导致识别结果随机性较强、可靠性不足。提出了一种数据驱动的瓶颈识别框架，首先引入拥堵传播收敛交叉映射(congestion propagation convergent cross mapping, CP-CCM)模型，计算相邻路段间的非线性因果关系强度，构建以路段为节点的拥堵传播图，该图不仅刻画了路段自身拥堵对路网运行的影响，还反映了拥堵在全网范围内的传播效应；随后，利用改进的广度优先搜索算法，基于传播图为各节点生成拥堵传播树，用于刻画各节点的拥堵传播成本，并综合自身拥堵成本量化路段总拥堵成本，基于总拥堵成本为各个路段划分瓶颈等级。SUMO软件仿真实验表明，扩容瓶颈等级越高的路段，其对路网性能的改善幅度越显著；扩容瓶颈等级最高的路段时，整个路网的车均行程速度提升了6%,平均等待时间减少了25.6%,实现了路网运行效率的最大化提升，验证了瓶颈识别方法的有效性与准确性。更多还原

【Abstract】 With accelerating urbanization, traffic congestion has become a major challenge for urban mobility. Accurate identification of network bottlenecks is vital for congestion mitigation and traffic optimization. However, existing approaches relying on static topological indicators and preset thresholds often produce unstable and less reliable results. To address these limitations, a data-driven framework for bottleneck identification based on CP-CCM(congestion propagation-convergent cross mapping) model was proposed. The nonlinear causal dependencies between adjacent road segments was quantified and a congestion propagation graph was constructed. An improved breadth-first search algorithm was used to derive propagation trees and calculate each segment’s total congestion cost by integrating self-congestion and propagation effects. Segments were then ranked by congestion level to identify critical bottlenecks. SUMO simulation results showed that expanding top-level bottleneck segments increased average travel speed by 6.0% and reduced average waiting time by 25.6%, validating the accuracy and practical effectiveness of the proposed framework.更多还原