A Novel Spatial-Aware Fast Traffic Vulnerable Areas Recognition for Urban Road Network

With the increase in urban traffic congestion, the problem of identifying vulnerable areas of road networks has attracted wide attention. Existing studies have mainly used mediated centrality methods or algorithms based on link analysis to find critical and vulnerable nodes in road network systems through network centrality methods. However, the experimental results of the mediated centrality approach under large-scale road network datasets have a certain degree of randomness, while the link analysis algorithms ignore the structure and interrelationships among nodes. For this reason, this paper proposes a new method to quickly identify vulnerable areas of road networks based on interflow degree and spatial density clustering. The main contribution of this paper is the introduction of a clustering-based road network density-related area vulnerability indicator $AVI$ aimed at assessing the level of area vulnerability. The indicator focuses on the relationship between the road network structure and nodes, avoiding randomness by considering all intersection nodes. Theoretical analysis indicates that the proposed method demonstrates excellent global stability and achieves a significantly lower time complexity of $O(n^{2})$ compared to the traditional Betweenness Centrality method, which has a time complexity of $O(n^{5})$. Through experimental validation of data from Beijing, London, New York, and Tokyo, the results show that the proposed method has high accuracy and reliability in identifying vulnerable areas of the road network with acceptable time complexity.