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 t...

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Veröffentlicht in:IEEE transactions on vehicular technology Jg. 74; H. 3; S. 3765 - 3779
Hauptverfasser: Cai, Zhi, Han, Xiao, Lu, Shuaibing, Shi, Meihui, Guo, Limin, Su, Xing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.03.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Accuracy
Algorithms
Analytical models
Area measurement
Bridges
Clustering
Complexity
fast identification
Network systems
Nodes
Randomness
road network density
Roads
Spatial awareness
Time complexity
Topology
Traffic congestion
Transportation
vulnerable areas
ISSN:0018-9545, 1939-9359
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Zusammenfassung: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 <inline-formula><tex-math notation="LaTeX">AVI</tex-math></inline-formula> 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 <inline-formula><tex-math notation="LaTeX">O(n^{2})</tex-math></inline-formula> compared to the traditional Betweenness Centrality method, which has a time complexity of <inline-formula><tex-math notation="LaTeX">O(n^{5})</tex-math></inline-formula>. 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.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2024.3484521