This study proposes a feature selection-based approach to identify reasonable spatial–temporal traffic patterns related to the target link, in order to improve the online-prediction performance. The prediction task is composed of two steps: one hybrid intelligent algorithm-based feature selector (FS) is proposed to optimise original state vectors, which are designed empirically during the offline process and optimised state vectors are employed to carry out the online prediction. Numerical experiments by three non-parametric algorithms are conducted with taxis’ global positioning system data in an urban road network of Changsha, China. It is concluded that: (i) under optimised state vectors, the prediction accuracies improve or almost maintain the same; (ii) K-nearest neighbour (KNN) with the simplest state vectors obtains the greatest improvement of prediction performance; (iii) although the performance improvement of ɛ-support vector regression is limited with optimised state vectors, it always outperforms backward-propagation neural network and KNN; and (iv) three non-parametric approaches with optimised state vectors outperform auto-regressive integrated moving average in relatively longer prediction horizons. In conclusion, such FS-based approach is able to improve or guarantee the prediction performance under the remarkably reduced model complexity, and is a promising methodology for short-term traffic prediction.


    Zugriff

    Zugriff über TIB


    Exportieren, teilen und zitieren



    Titel :

    Feature selection-based approach for urban short-term travel speed prediction


    Beteiligte:
    Zheng, Liang (Autor:in) / Zhu, Chuang (Autor:in) / Zhu, Ning (Autor:in) / He, Tian (Autor:in) / Dong, Ni (Autor:in) / Huang, Helai (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2018-03-13


    Format / Umfang :

    11 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Feature selection‐based approach for urban short‐term travel speed prediction

    Zheng, Liang / Zhu, Chuang / Zhu, Ning et al. | Wiley | 2018

    Freier Zugriff

    Urban Traffic Travel Time Short-Term Prediction Model Based on Spatio-Temporal Feature Extraction

    Leilei Kang / Guojing Hu / Hao Huang et al. | DOAJ | 2020

    Freier Zugriff



    Short-term travel time prediction

    Zhang, Xiaoyan | Online Contents | 2003