With the continuous increase in the number of motor vehicles and the frequent occurrence of road congestion problems, it has become an important research topic to carry out comprehensive collection of traffic road network status information, processing analysis, prediction, and decision-making recommendation to effectively solve urban traffic problems. The traffic flow is one of the main parameters reflecting the road operation status. It is of great significance to timely and accurately grasp and predict the road traffic situation, which is of great significance for diverting vehicles in advance and improving the operation capacity and efficiency of the road network. Due to the complex spatial structure of the road network, the road traffic flow is non-Euclidean, non-directional, and the change over time is non-stationary, which has a strong time dependence, which leads to greater challenges in traffic flow prediction. This paper comprehensively considers the characteristics of the temporal and spatial correlation of traffic flow, and provides a city traffic flow prediction method based on graph convolutional neural network, which can effectively mine the temporal and spatial dynamic patterns of urban road network traffic flow and realize accurate traffic flow prediction.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Research on City Traffic Flow Forecast Based on Graph Convolutional Neural Network


    Contributors:
    Hu, Yaohui (author)


    Publication date :

    2021-03-26


    Size :

    1444887 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Multi-scale traffic flow prediction method based on graph convolutional neural network

    ZHANG MEIYUE / WANG SENZHANG / MIAO HAO et al. | European Patent Office | 2021

    Free access

    Urban Taxi Demand Forecast Based on Graph Convolutional Network

    Wang, Yaguan / Qin, Yong / Guo, Jianyuan | Springer Verlag | 2022


    Traffic flow prediction method based on time attention circulation graph convolutional neural network

    FAN WENDONG / SHU MIN / SONG YUN et al. | European Patent Office | 2023

    Free access

    Traffic flow prediction method based on graph convolutional network

    XU HUI / MENG FANYU / REN QIANQIAN et al. | European Patent Office | 2025

    Free access

    Short-term traffic flow prediction method based on integrated graph convolutional neural network

    LIU LUYANG / LYU SHUAIQI / BAO XU | European Patent Office | 2024

    Free access