基于模糊时序网络的城市路网关键交叉口识别方法

在城市路网拓扑结构和动力学过程的基础上,增加对其时序特性的考虑,提出适用于城市路网关键交叉口识别的模糊时序网络模型。首先,阐述一般时序网络的描述方法和超邻接矩阵时序网络模型的原理,分析其优势以及将其用于城市路网分析的局限性;然后,提出优化措施,一方面结合交通网络的功能特性,以动态交通参数构造单个时间步网络的层内交叉口交互强度模糊指标,另一方面借鉴并改进邻居拓扑重叠系数,对其进行模糊化处理,实现两相邻时间步网络层间交叉口关联强度的差异化表达;之后,在改进时间步层内、层间关联描述矩阵基础上,搭建模糊超邻接矩阵(Fuzzy Supra-adjacency Matrix, FSAM)时序网络模型(FSAM模型);最后,以某城市核心区域147个交叉口构成的路网数据验证模型有效性。结果表明：以时序网络模型分析交叉口重要性非常必要,以中位数表达交叉口在时段内的重要性排序更为可靠;FSAM模型对交叉口重要性的排名时间序列有阶段持续性特征,且相比于特定时间步下基于单一指标的关键交叉口识别结果具有更丰富的内涵;不同时间颗粒度下,FSAM模型对交叉口重要性排序的一致性较好,结果较为稳定。综上,该模型可供城市路网关键交叉口识别之用。

A Fuzzy Temporal Network Model for Identifying CriticalIntersections in Urban Road Network

To incorporate the temporal characteristics of an urban road network, rather than considering its topologyand kinetics features only, a fuzzy temporal network model is proposed to accommodate the task of recognizing keynodes in the urban road network. Firstly, the general description of the temporal network is presented, and the temporalnetwork described with the supra-adjacency matrix is introduced. The pros and cons of their applications in trafficanalysis are discussed. Then improved methods are put forward correspondingly. On the one hand, regarding thefunctions of the road network, indexes with fuzziness calculated from dynamic traffic parameters are suggested todepict the interaction intensity between intersections in one interval layer. On the other hand, the layer couplingcoefficient is referred to and fuzzed to differentiate the interlayer correlation of intersections between two adjacentinterval networks. After that, the improved intralayer and interlayer matrices are integrated to build the Fuzzy Supraadjacency Matrix (FSAM) temporal network model (FSAM model). Finally, the model's effectiveness is illustratedwith the data of a busy local road network, including 147 intersections. The results show that it is necessary to analyzethe importance of intersections with temporal network models. It is more reliable to define the importance-ranking ofan intersection with the median. The ranking series reported by the FSAM model holds persistence for a period, and theproposed model is more comprehensive than identifying critical intersections based on a single index concerning onlyone isolated interval. Moreover, with different time granularity, the FSAM model exhibits good consistency in rankingthe intersections by their importance, and the results are stable. The model can be referred to identify criticalintersections in urban road networks.