Traffic flow prediction is an important task that can directly impact the control of traffic flow positively and improve the overall traffic throughput. Although a large number of studies have been performed to improve traffic flow prediction, there are very few works on purely temporal prediction models, which is important for execution on an edge device that does not have access to spatial flow information. In order to explore the temporal prediction models further, we propose an innovative hybrid long short-term memory (LSTM) model, which we call Grid LSTM based Attention Modelling for Traffic Flow Prediction (GLSTM-A), that helps to encode temporal information better at various levels/scenarios. The proposed architecture incorporates a Grid LSTM to capture historical dependencies and a simple LSTM layer dedicated to the short-term analysis of recent data. Moreover, an innovative attention mechanism is designed to focus on the importance of data features automatically for further enhancing the model's predictive capabilities. Our proposed GLSTM-A outperforms other popular temporal prediction models such as temporal convolutional network (TCN), Bi-LSTM and LSTM, in terms of prediction accuracy and memory efficiency as mentioned in the experimental results. Experimental results and ablation studies on benchmark datasets demonstrate the superior performance of the proposed model over existing state-of-the-art models in various time series prediction tasks.


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    Titel :

    Grid LSTM based Attention Modelling for Traffic Flow Prediction


    Beteiligte:


    Erscheinungsdatum :

    24.06.2024


    Format / Umfang :

    302883 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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