Estimating traffic state, particularly traffic volume, is crucial in Intelligent Transportation Systems (ITS). Due to the absence or malfunction of detectors, some road segments are undetected, leading to a complete absence of volume data and thereby weakening the traffic monitoring capability of ITS. The existing estimation methods are either inapplicable to this scenario or yield poor results due to a lack of available data, which will compromise the traffic monitoring capability of ITS. To handle it, this work proposes a novel Road Network Similarity-based Transfer Learning method (RNS-TL) for real-time traffic estimation. Firstly, the Small-scale Road Network Similarity Evaluation Module (SSEM) is initially proposed which aims to identify the most similar road segments and their small-scale road networks for the undetected segments, serving as the source domain for transfer learning. Then, based on SSEM, a transfer learning framework is proposed where a traffic estimation model trained on the source domain is fine-tuned for the target undetected road segment. Finally, the results from two real-world traffic cases show that the estimation errors, MAE and RMSE, for the proposed method are 7.813 and 6.383, and 10.689 and 8.892, respectively, outperforming all comparison methods.


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    Title :

    Road Network Similarity-Based Transfer Learning Method for Traffic Volume Estimation in Undetected Road Segments


    Contributors:
    Cao, Shan (author) / Song, Chunyue (author) / Zhang, Jie (author) / Zhang, Xiangrui (author)


    Publication date :

    2025-06-01


    Size :

    2433233 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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