Abstract
The key nodes in a complex transportation network have a significant influence on the safety of traffic operations, connectivity reliability, and the performance of the entire network. However, the identification of key nodes in existing urban transportation networks has mainly focused on nonweighted networks and the network information of the nodes themselves, which do not accurately reflect their global status. Thus, the present study proposes a key node identification algorithm that combines traffic flow features and is based on weighted betweenness centrality. This study also uses weighted roads to construct an L-space weighted transportation network and an approximate algorithm for betweenness centrality in order to reduce the complexity of the calculations. The results of the simulation indicate that the proposed algorithm is not only capable of identifying the key nodes in a relatively short amount of time, but it does so with high accuracy. The findings of this study can be used to provide decision-making support for road network management, planning, and urban traffic construction optimization.
Highlights
Since Watts and Strogatz [1] developed a general network with small-world properties and Barabasi and Albert [2] created a scale-free network, the study of complex networks has been attracting increasing attention from scholars and industry professionals [3, 4]
Since the key nodes in an urban transportation network have a significant influence on the safety, reliability, and overall performance of the network, the identification of key nodes and the examination of their complexities have been the subjects of numerous studies [8, 9]
To overcome those reviewed limitations, this study proposes an enhanced Betweenness Centrality (BC) algorithm to better identify the key nodes in transport network
Summary
Since Watts and Strogatz [1] developed a general network with small-world properties and Barabasi and Albert [2] created a scale-free network, the study of complex networks has been attracting increasing attention from scholars and industry professionals [3, 4]. (3) the most of existing studies can only consider the physical structure of urban road network while some traffic-related factors are typically neglected To overcome those reviewed limitations, this study proposes an enhanced Betweenness Centrality (BC) algorithm to better identify the key nodes in transport network. When the complexity of the BC calculation is high and the computational complexity greatly restricts the size of the computable network (e.g., in the case of a complex traffic network in a large city), the time required for the algorithm is significant. Those barriers would lose its practical application value. To verify the effectiveness and efficiency of the proposed study, Nanjing, the capital city of Jiangsu Province, China, is used as the case study
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
