Vehicle speed prediction is of great significance for intelligent transportation and eco-driving. Currently, mainstream methods for speed prediction rely more on the vehicle's own historical data, ignoring the influence of the surrounding traffic environment. This paper proposes a vehicle speed prediction method based on Informer, which integrates real-time multi-source traffic information to improve prediction accuracy. K-means clustering is used to cluster the following mode and traffic flow mode. During prediction, a back propagation neural network is employed for recognition, and the recognition results are used as inputs to the prediction model, achieving the extraction and integration of traffic information. Experimental results demonstrate that the Informer-based vehicle speed prediction method outperforms current mainstream deep learning methods in prediction accuracy, and the integration of multi-source traffic information in speed prediction surpasses methods that do not integrate traffic information.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Vehicle Speed Prediction Method Integrating Multi-Source Traffic Information Based on Informer


    Contributors:
    He, Hongwen (author) / Xu, Heng (author) / Li, Menglin (author) / Niu, Zegong (author)


    Publication date :

    2024-08-23


    Size :

    2211827 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    High-speed train braking system fault prediction method based on Informer

    DONG HONGHUI / WU ZHONGQIANG / JIA LIMIN et al. | European Patent Office | 2023

    Free access

    Long-time-sequence traffic flow prediction method based on graph convolution-Informer model

    GUO ZIQIANG / CHENG BAOXI / YANG XIAOLEI et al. | European Patent Office | 2021

    Free access

    Ship traffic flow long sequence space-time prediction method based on ST-Informer

    JIANG BAODE / LUO HAIYAN / JIANG YING | European Patent Office | 2023

    Free access