Dynamic traffic bottlenecks identification based on congestion diffusion model by influence maximization in metro‐city scales

Summary Traffic bottlenecks dynamically change with the variance of traffic demand. Identifying traffic bottlenecks plays an important role in traffic planning and provides decision making. However, traffic bottlenecks are difficult to identify because of the complexity of traffic road networks and...

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Bibliographic Details
Published in:Concurrency and computation 2021-03, Vol.33 (6), p.n/a
Main Authors: Zhao, Baoxin, Xu, Cheng‐Zhong, Liu, Siyuan, Zhao, Juanjuan, Li, Li
Format: Article
Language:English
Subjects:
Algorithms
Approximation
bottlenecks identification
Decision making
influence maximization
Maximization
Optimization
Roads
Traffic congestion
traffic congestion diffusion
Traffic control
Traffic flow
traffic flow influence
Traffic models
Traffic planning
Transportation networks
Transportation planning
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Summary:Summary Traffic bottlenecks dynamically change with the variance of traffic demand. Identifying traffic bottlenecks plays an important role in traffic planning and provides decision making. However, traffic bottlenecks are difficult to identify because of the complexity of traffic road networks and many other factors. In this article, we propose an influence spreading based method to find the dynamic changed traffic bottlenecks, where the influence caused by bottlenecks is maximal. We first build a traffic congestion diffusion (TCD) model to capture traffic flow influence (TFI) spreading over traffic road networks. The bottlenecks identification problem based on TCD is modeled as an influence maximization problem, that is, selecting the most influential nodes such that the deterioration of traffic condition is maximal. With the proof of the submodularity of TFI spreading over traffic networks, a provably near‐optimal algorithm is used to solve the NP‐hard problem. With the exploration of unique properties of TFI spread, an approximate influence maximization method for TCD (TCD‐AIM) is proposed. To the best of our knowledge, this should be the first model for a metro‐city scale from the influence perspective. Experimental results show that TCD‐AIM finds bottlenecks with up to 130% congestion density increase in the future.
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.5790