Short-term traffic prediction has been widely studied in the community of Intelligent Transport Systems for decades. Despite the advances in machine learning-based prediction techniques, a challenging problem that affects the applications of such methods in practice is the prevalence of insufficient data across an entire road network. To address this few-shot traffic prediction problem at a local network scale, we develop a hybrid framework in conjunction with the prior knowledge transferring algorithm and two widely used models, i.e. Long-short Term Memory and Spatial–Temporal Graph Convolutional Neural Network. The proposed modelling framework is trained and tested using five-minute interval traffic flow data collected from London under different few-shot learning scenarios. Results show that transferring local network prior knowledge can improve the accuracy of both one-step prediction and multi-step prediction under inadequate data conditions, regardless of the deep-learning tool used.


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    Title :

    Few-Shot traffic prediction based on transferring prior knowledge from local network


    Contributors:
    Yu, Lin (author) / Guo, Fangce (author) / Sivakumar, Aruna (author) / Jian, Sisi (author)


    Publication date :

    2023-12-31


    Size :

    23 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown







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