Summary Short-term traffic flow prediction in urban area remains a difficult yet important problem in intelligent transportation systems. Current spatio-temporal-based urban traffic flow prediction techniques trend aims to discover the relationship between adjacent upstream and downstream road segments using specific models, while in this paper, we advocate to exploit the spatial and temporal information from all available road segments in a partial road network. However, the available traffic states can be high dimensional for high-density road networks. Therefore, we propose a spatio-temporal variable selection-based support vector regression (VS-SVR) model fed with the high-dimensional traffic data collected from all available road segments. Our prediction model can be presented as a two-stage framework. In the first stage, we employ the multivariate adaptive regression splines model to select a set of predictors most related to the target one from the high-dimensional spatio-temporal variables, and different weights are assigned to the selected predictors. In the second stage, the kernel learning method, support vector regression, is trained on the weighted variables. The experimental results on the real-world traffic volume collected from a sub-area of Shanghai, China, demonstrate that the proposed spatio-temporal VS-SVR model outperforms the state-of-the-art. Copyright © 2015 John Wiley & Sons, Ltd.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Urban traffic flow prediction: a spatio-temporal variable selection-based approach




    Publication date :

    2016




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English


    Classification :

    BKL:    55.21 Kraftfahrzeuge / 55.21
    Local classification TIB:    275/7020



    Urban Traffic Flow Prediction Using a Spatio-Temporal Random Effects Model

    Wu, Yao-Jan / Chen, Feng / Lu, Chang-Tien et al. | Taylor & Francis Verlag | 2016


    Spatio-Temporal AutoEncoder for Traffic Flow Prediction

    Liu, Mingzhe / Zhu, Tongyu / Ye, Junchen et al. | IEEE | 2023


    Urban traffic prediction method and system based on spatio-temporal data flow fusion analysis

    REN MINGLUN / HUANG XIAODI / CHU WEI et al. | European Patent Office | 2020

    Free access

    Global spatio‐temporal dynamic capturing network‐based traffic flow prediction

    Haoran Sun / Yanling Wei / Xueliang Huang et al. | DOAJ | 2023

    Free access

    Global spatio‐temporal dynamic capturing network‐based traffic flow prediction

    Sun, Haoran / Wei, Yanling / Huang, Xueliang et al. | Wiley | 2023

    Free access