Road traffic prediction is a vital role of real-time traffic management in the intelligent transportation system (ITS). Many of existing prediction models are established and achieved good results. However, most of them ignored the intrinsic characteristics of traffic parameters data and also not considered on the spatiotemporal effects of the road section which can reflect the situation of the whole road section traffic. Therefore, multi-node road section traffic prediction is still an open problem. This paper, the empirical mode decomposition (EMD) technique is employed to decompose the traffic parameters information into many intrinsic mode function (IMF) components, which represent the original road traffic information in periodic sequence and random sequence. Then, by considering the superiority of convolution neural network (CNN) in multi-dimensional data processing which could handle the spatiotemporal effects, a prediction model based on CNN is used to achieve the prediction of periodic sequence and random sequence. Finally, two parts of the prediction results are combined to get the final prediction results. The dataset from Caltrans Performance Measurement System is used for building the model and compared with several well-known models, such as PCA-BP, Lasso-BP, and standard CNN. The results show that the proposed prediction model achieves higher accuracy with smaller prediction error.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multi-node Mode Decomposition Based Deep Learning Model for Road Section Traffic Prediction


    Contributors:


    Publication date :

    2019-06-01


    Size :

    678439 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Traffic flow prediction method based on combination of empirical mode decomposition and deep learning

    LI YONG / WANG SENZHENG / LIU MEIQI et al. | European Patent Office | 2024

    Free access

    Road traffic accident risk prediction deep learning algorithm

    YU ZHIQING / YAO HUI / LI KUN et al. | European Patent Office | 2022

    Free access

    Road traffic node road section driving adaptability evaluation method for automatic driving

    WANG SHUYI / LAI YUANWEN / SU YAN | European Patent Office | 2023

    Free access

    Urban road traffic state prediction system based on deep learning

    HAO WEI / YI KEFU / GAO ZHIBO et al. | European Patent Office | 2020

    Free access

    Short-term traffic flow prediction model based on variational mode decomposition multi-stage optimization

    CHEN YI / QI XINGYU / HU SHUIYUAN et al. | European Patent Office | 2023

    Free access