With the continuous and rapid growth of motor vehicle ownership, the road traffic flow increases steeply, bringing great pressure to the urban road network, resulting in increasingly serious urban road congestion. Accurate prediction of road traffic flow is the basis for scientific and dynamic control of traffic facilities, reducing congestion and improving operational efficiency and road safety. To capture the spatio-temporal correlation that exists between nodes in a real traffic network, this paper proposes a method for predicting traffic flow based on Graph Sampling Aggregation Network and Long Short-Term Memory (LSTM) network, which learns spatial relationships through Graph Sampling Aggregation Network (GraphSAGE), splices the feature information of each roadway node at each collection moment into a vector as the features of the input LSTM unit, and utilizes the Long Short-Term Memory Network (LSTM) to achieve the temporal relationship learning of traffic flow features, and then the current moment flow features are fused to predict the node flow. To verify the model accuracy, it is compared with other centralized neural network models. The results indicate that the presented GraphSAGE-LSTM model has higher prediction performance than the other three control models, and can meet the requirements of traffic flow prediction based on different time intervals of samples.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Traffic Flow Prediction Method Based on Multi-Source Data Fusion


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Jia, Limin (editor) / Wang, Yanhui (editor) / Easa, Said (editor) / Xiang, Zhen (author) / Gu, Jidong (author) / Dai, Xufeng (author) / Jiang, Chuan (author)

    Conference:

    International Conference on SmartRail, Traffic and Transportation Engineering ; 2024 ; Chongqing, China October 25, 2024 - October 27, 2024



    Publication date :

    2025-07-19


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Urban traffic flow prediction method based on multi-source data fusion

    LIU JIANQI / HE QI / ZENG BI et al. | European Patent Office | 2020

    Free access

    Urban intersection traffic flow prediction method based on multi-source data fusion

    YANG HUICHANG | European Patent Office | 2021

    Free access

    Multi-modal traffic flow prediction method based on multi-source data feature fusion

    CHEN LULU / LI SHIJIE / JIANG HUAIGUANG et al. | European Patent Office | 2025

    Free access

    Traffic flow prediction method based on multi-feature fusion

    LIU CONG'AI / QIN YUHUA / ZHANG XIAOJING | European Patent Office | 2024

    Free access

    Air quality prediction method based on traffic jam index and multi-source data fusion

    HU JUNTAO / ZHANG SHICHENG / CUI CAN et al. | European Patent Office | 2024

    Free access