Fast and accurate short-term traffic volume prediction is the core content of ITS. However, the traditional prediction methods for a certain road section cannot provide useful information for urban traffic management. This study proposes a method to predict the traffic flow distribution of the road network for a period of time in the future. K-means clustering with dynamic time warping algorithm (DTW) is used to identify the characteristics of time series of traffic flow. Artificial neutral network (ANN) is trained to predict traffic flow of the whole network. The short-term traffic flow prediction model of urban road network based on DTW and ANN is established and compared with the ARIMA, the ANN model, and the emerging LSTM. DTW-ANN model has better prediction effect in data sets of various intervals and can effectively predict the overall traffic distribution of urban road network.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Short-Time Traffic Forecasting of Urban Road Network: An ANN Model Based on DTW Clustering


    Beteiligte:
    Sun, Yubo (Autor:in) / Wei, Chunyi (Autor:in) / Tang, Xin (Autor:in) / Yang, Fan (Autor:in)

    Kongress:

    19th COTA International Conference of Transportation Professionals ; 2019 ; Nanjing, China


    Erschienen in:

    CICTP 2019 ; 6070-6082


    Erscheinungsdatum :

    02.07.2019




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Cell-Link Model for state forecasting of urban road traffic network

    Liu, Siyan / Xi, Yugeng / Li, Dewei et al. | IEEE | 2014



    Clustering Based RBF Neural Network Model for Short-Term Freeway Traffic Volume Forecasting

    Park, B. / American Society of Civil Engineers | British Library Conference Proceedings | 1998


    Short-term traffic flow forecasting of urban network based on dynamic STARIMA model

    Min, Xinyu / Hu, Jianming / Chen, Qi et al. | IEEE | 2009