基于路网压缩的城市路网脆弱路段识别

Identification of Urban Road Vulnerability Based on Road Network Compression

为了更高效地识别出突发环境下城市道路网络中的脆弱路段,首先通过网络特性分析,运用网络效率变化量与最大连通子图变化量这两类鲁棒性指标筛选出道路网络的潜在脆弱路段集合,并在此路段集合的基础上设计出一种基于可达性原理的路网矩阵压缩算法,该算法可将原始路网压缩成若干个彼此连通且相互独立的子路网。然后在压缩后的各个子路网上,考虑不同类型出行者对路段阻抗的随机估计偏差以及对应的路径选择行为,推导出一个多用户随机均衡配流模型并用MSA算法进行求解。最后通过改进原有的网络效率指标,构建出一个新的融合交通流随机分布特性的路网脆弱性指标,用来识别各子路网中的脆弱路段,再结合实测数据进行了模型验证。结果表明:相较于传统的遍历法,基于路网压缩的脆弱路段识别模型能够真实地刻画出突发环境下城市路网交通流分布的随机特性,而且求解模型所耗时间明显缩短(计算过程仅约2～3 min);该模型的求解结果对各个子路网中的脆弱路段有着更好的区分(区分度比传统的遍历法高出24.46%),这能够有效地降低传统识别方法对城市网络脆弱路段误判的可能性,并能够及时地为突发环境下的城市交通管理部门提供关于应急救援与人员疏散的决策支持。

In order to more effectively identify the vulnerable links on urban road network under sudden environment, first, by the network characteristic analysis, 2 robust indicators ( network efficiency variation and maximum connected sub-graph variation) are used to screen out the potential vulnerable link set of the road network, and a road network's matrix compression algorithm based on the accessibility principle is designed accordingly. This algorithm can compress the original road network into several interconnected and independent sub road networks. Then, on each compressed sub-network, considering the random estimation bias and path selection behavior of different travelers to the link impedance, a multi-user stochastic equilibrium flow assignment model is derived and the MSA algorithm is adopted to solve it. Finally, by improving the original network efficiency indicator, a new road network vulnerability indicator which combines the random distribution characteristics of traffic flow is constructed to identify the vulnerable links on each sub network, and the model is verified with the measured data. The result shows that ( 1 ) compared with the traditional traversing method, the vulnerable link identification model which is based on road network compression can truly depict the stochastic characteristics of traffic flow distribution on urban road network under sudden environment, and the time consumed to solve the model is significantly shortened ( the calculation process is only about 2-3 min);(2) the result of the model can better distinguish the vulnerable links on each road sub-network (its discrimination degree is 24. 46% higher than that of the traditional traversal method), which can effectively reduce the traditional method's misjudgement possibility of the vulnerable links on urban network, and can timely provide decision support about emergency rescue and evacuation for urban traffic management departments under sudden environment.