The aim of this study is to construct an urban traffic flow prediction and route planning system based on deep learning models to solve the growing problem of urban traffic congestion. By analyzing and processing historical traffic data of a city and using a hybrid model combining Long Short-Term Memory Network (LSTM) and Graph Neural Network (GNN), we are able to accurately predict traffic conditions at a certain time and location in the future. To achieve this goal, we first preprocessed the traffic data, including converting dates to temporal features and One-Hot coding of intersection locations. Subsequently, we used deep learning techniques for training, during which an Adam optimizer as well as a categorical cross-entropy loss function were used to improve the accuracy and reliability of the predictions. On this basis, this study also develops an optimal path planning function based on the prediction results, which provides the optimal traffic jam avoidance routes by adjusting the weights of the road network and applying Dijkstra's algorithm to find the optimal paths from the start to the end points. The experimental results show that the system achieves good results in traffic flow prediction and path planning with an accuracy rate of 91%.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Deep-learning-based urban intelligent traffic flow prediction and optimization research


    Contributors:

    Conference:

    International Conference on Smart Transportation and City Engineering (STCE 2024) ; 2024 ; Chongqing, China


    Published in:

    Proc. SPIE ; 13575


    Publication date :

    2025-04-28





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Urban traffic flow prediction method based on deep learning

    WANG RONGXIU / DING YANQIU / ZHENG YAQIAN | European Patent Office | 2025

    Free access

    2-dimensional flow prediction based intelligent urban traffic-flow prediction method

    ZHAO LEI / SU QINGGANG / WANG ZHENYI et al. | European Patent Office | 2016

    Free access

    Deep learning based traffic flow prediction model on highway research

    Jia, Qingyang / Zang, Jingfeng / Liu, Shuanglin | SPIE | 2024



    Traffic flow prediction optimization method based on intelligent optimization algorithm

    LIU BINGJIE / ZHANG ZHENG / REN JIANLAN | European Patent Office | 2025

    Free access