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Deep traffic congestion prediction model based on road segment grouping

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Abstract

Intelligent Transportation System (ITS) is now being widely built all over the world. Traffic congestion prediction, as a major part of ITS, not only provides reliable traffic information for travelers to save their time, but also assists traffic management agencies to manage the traffic system. We find that the existing papers, including neural networks, do not perform well due to the complex association features between the road sections. In addition, the lack of a better evaluation standard for traffic congestion also makes the effect of traffic congestion prediction worse. In this paper, we propose a method to generate traffic congestion index by mining free-stream speed and free-stream flow. Considering the association features of road segments in the road network, we propose a road segment grouping method based on association subgraph to pre-train deep learning model and realize information sharing between road segments. Combining the features of traffic data and CNN model, we propose a traffic congestion prediction model named SG-CNN which training process is optimized by road segment grouping algorithm. The experiments demonstrate that our proposed model has higher accuracy rate than other models.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, China

    Yue Tu, Shukuan Lin, Jianzhong Qiao & Bin Liu

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  1. Yue Tu
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  2. Shukuan Lin
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  3. Jianzhong Qiao
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  4. Bin Liu
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Correspondence to Shukuan Lin.

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Tu, Y., Lin, S., Qiao, J. et al. Deep traffic congestion prediction model based on road segment grouping. Appl Intell 51, 8519–8541 (2021). https://doi.org/10.1007/s10489-020-02152-x

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