Smart Road Traffic Handler: Cause Identification and Resolution Using Image Processing

Yadav, Mukesh; Ghadge, Kasturi; Shetty, Nitya; Chanchlani, Deepa; Narwani, Priyanka; Joseph, Richard

doi:10.1109/confluence.2019.8776938

Cited by 1 publication

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[3][4][5][6] The influence of traffic bottlenecks is generally regarded as a major cause of traffic congestion. 7,8 Traffic bottlenecks mitigation or elimination is believed to be a top priority for the management of traffic congestion. 9,10 Traffic bottleneck identification is fundamental for alleviating congestion and improving the performance of the road network in the intelligent transportation system (ITS).…”

Section: Introductionmentioning

confidence: 99%

“…To solve the traffic congestion problem, many works have been done to analyze the reason for traffic congestion 3‐6 . The influence of traffic bottlenecks is generally regarded as a major cause of traffic congestion 7,8 . Traffic bottlenecks mitigation or elimination is believed to be a top priority for the management of traffic congestion 9,10 .…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Zhao

Cheng

Liu

et al. 2020

Concurrency and Computation

View full text Add to dashboard Cite

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.

show abstract