Traffic speed prediction uses historical data to model traffic patterns and generates forecasts for future steps. As the number of vehicles surges significantly, traffic congestion could negatively affect the quality of life, human health, and the environment. Thus, finding a method providing accurate and robust forecasts for road users and traffic management is a crucial task. In traffic prediction, capturing both long and short-term patterns is necessary for forecasting precisely. However, traditional models only perform well in short-term modeling and vice versa. This paper aims to create a hybrid model, namely LSTM-SVR, to overcome the mentioned difficulty. The LSTM-SVR combines long short-term memory (LSTM) for modeling long-term dependencies with support vector regression (SVR) for capturing short-term features. The experiments corroborate that the model outperforms popular selected baselines. The results also show the ability of the proposed model to capture peak hours and short-term patterns. This research provides a reliable forecasting tool for traffic engineers and new insights into hybrid model design in traffic speed prediction. The source code for the model from this paper is publicly available and can be found at https://github.com/adasken/lstm-svr.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Short-Term Traffic Speed Prediction Using Hybrid LSTM-SVR Model


    Additional title:

    Lect. Notes in Networks, Syst.


    Contributors:

    Conference:

    International Conference on Robot Intelligence Technology and Applications ; 2022 ; Daejeon, Korea (Republic of) December 07, 2022 - December 09, 2022



    Publication date :

    2023-03-01


    Size :

    13 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    A Short-term Traffic Speed Prediction Model Based on LSTM Networks

    Hsueh, Yu-Ling / Yang, Yu-Ren | Springer Verlag | 2021


    Short-Term Traffic Flow Prediction: Using LSTM

    Poonia, Pregya / Jain, V. K. | IEEE | 2020


    D-LSTM: Short-Term Road Traffic Speed Prediction Model Based on GPS Positioning Data

    Meng, Xianwei / Fu, Hao / Peng, Liqun et al. | IEEE | 2022


    Short-term traffic flow prediction method based on Spearman-LSTM model

    ZANG JINGFENG / JIA QINGYANG / LIU SHUANGLIN et al. | European Patent Office | 2024

    Free access

    Short-term traffic flow prediction method based on Conv1D-LSTM model

    ZHANG ZHIPENG / LIU YUHANG / DAI LEI et al. | European Patent Office | 2023

    Free access