Traffic flow prediction is crucial for enhancing transportation systems' efficiency and safety, providing references for intelligent traffic management and road resource allocation. However, capturing the intricate spatial and temporal relationships between various areas and time intervals poses a significant challenge. To address the sophisticated spatio-temporal correlations encountered in traffic flow forecasting, we develop an adaptive convolutional neural network based on time attention. For spatial considerations, we design deformable convolutional kernels integrated with an attention mechanism to extract road information and adapt it to the road network structure. Furthermore, we deploy the attention mechanism to dynamically aggregate multiple parallel convolutional kernels instead of the traditional single-layer convolution on each input, focusing precisely on key areas within the network. From a temporal perspective, a dynamic time attention mechanism is applied based on weekly, daily, and nearest temporal data to grasp critical moments in periodic traffic flow. By integrating essential temporal and spatial elements, the Dynamic Spatial Temporal Adaptive Convolutional Networks (DSTACN) are constructed. The proposed model is validated on three real-world datasets. The results demonstrate its forecasting accuracy surpassing other baseline models by up to 11.98%, highlighting the proposed approach's capability in handling the spatio-temporal dependencies inherent in traffic flow data.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Dynamic Spatio-Temporal Adaptive Convolutional Neural Networks for Traffic Flow Prediction


    Contributors:
    Sun, Zhanbo (author) / Liu, Zhuo (author) / Zhang, Chao (author) / Kong, Mingming (author) / Ji, Ang (author)


    Publication date :

    2024-09-24


    Size :

    5213537 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Spatio‐temporal adaptive graph convolutional networks for traffic flow forecasting

    Ma, Qiwei / Sun, Wei / Gao, Junbo et al. | Wiley | 2023

    Free access

    Spatio‐temporal adaptive graph convolutional networks for traffic flow forecasting

    Qiwei Ma / Wei Sun / Junbo Gao et al. | DOAJ | 2023

    Free access

    Deep Spatio-Temporal Convolutional Neural Network for City Traffic Flow Prediction

    Zhou, Zhiyuan / Qin, Yanjun / Luo, Haiyong | IEEE | 2021


    Dynamic Spatio-Temporal Residual Hypergraph Convolutional Networks for Traffic Flow Forecasting

    Su, Jun / Wang, Hairu / Przystupa, Krzysztof et al. | Transportation Research Record | 2025


    Spatio-temporal Dynamic Graph Convolutional Probability Sparse Attention Networks for Traffic Flow Forecasting

    Chen, Linlong / Chen, Linbiao / Wang, Hongyan et al. | Springer Verlag | 2025