Urban vehicle emission pollution has become the main factor affecting urban air quality. How to achieve fine-grained emission prediction is of great significance to vehicle emission supervision. Existing works mainly considered the emission prediction as a time sequence forecasting task, and extracted the temporal and spatial features of emission sequence based on road network prior information. However, road emission patterns are usually influenced by diverse environmental factors like weather and traffic conditions. These urban multi-source data have different properties, how to utilize cross-domain factors to assist emission prediction remains to be studied. To this end, a multi-source fusion spatiotemporal network is devised to simultaneously capture external factors’ impact on emission prediction. Specifically, the proposed model adopt an attention adaptive fusion module to achieve multi-source heterogeneous data fusion. Experiment results on Beijing emission dataset have indicated that our method surpass the existing baselines.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multi-source Adaptive Fusion Spatiotemporal Network for Traffic Emission Prediction


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Yan, Liang (editor) / Duan, Haibin (editor) / Deng, Yimin (editor) / Zhang, Guoan (author) / Cao, Yang (author) / Pei, Lihong (author) / Kang, Yu (author)

    Conference:

    International Conference on Guidance, Navigation and Control ; 2024 ; Changsha, China August 09, 2024 - August 11, 2024



    Publication date :

    2025-03-02


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





    Attention-Based Spatiotemporal Adaptive Graph Diffusion Convolutional Network For Traffic Flow Prediction

    He, Qiansong / Xia, Dawen / Li, Jianjun et al. | Transportation Research Record | 2025


    Trajectory-Based Spatiotemporal Multi-Task Multi-Graph Network for Traffic State Prediction

    Fang, Jie / Chen, Wentian / Xu, Mengyun et al. | Transportation Research Record | 2023


    Optimised LSTM Neural Network for Traffic Speed Prediction with Multi-Source Data Fusion

    Yongpeng ZHAO / Yongcang LI / Changxi MA et al. | DOAJ | 2024

    Free access