The application of short-term traffic forecasting can guide the operation of traffic networks efficiently and reduce the traffic cost for travelers. On the basis of radial basis function (RBF) neural network, this paper introduces a tunable and transferable RBF (TT-RBF) model to conduct on-line forecasting and transfer forecasting. Considering the spatiotemporal correlation of traffic flows in a road network, a spatiotemporal state matrix formed by the detrended cross-correlation analysis is used for the model input. With the on-line forecasting process, an improved on-line structure and parameter adjustment are proposed to enhance the existing model. Thus, the TT-RBF model can be adaptive to time-varying traffic states, especially to deal with the difference between non-peak and peak hours. Moreover, the proposed model can be transferred from one road segment to act on other road segments. By this way, the traffic states of numerous road segments can be forecasted conveniently without complex model training processes. The floating car data of a typical road network in Beijing are used for the performance verification of the TT-RBF model, and some frequently used forecasting models are selected for comparisons. The numerical experiments show that the TT-RBF model can get more accurate results than those in single-step forecasting, multi-step forecasting, and transfer forecasting.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Tunable and Transferable RBF Model for Short-Term Traffic Forecasting


    Contributors:


    Publication date :

    2019-11-01


    Size :

    4031602 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Short-Term Forecasting of Traffic Volume

    Lin, Lei / Wang, Qian / Sadek, Adel W. | Transportation Research Record | 2013



    Enhancing Short-Term Traffic Forecasting with Traffic Condition Information

    Turochy, R. E. | British Library Online Contents | 2006


    Short-Term Forecasting of Uncertain Traffic States

    Xiong, Z. / Yao, Z. / Shao, C. | British Library Conference Proceedings | 2009


    Short-Term Traffic Forecasting Using High-Resolution Traffic Data

    Li, Wenqing / Yang, Chuhan / Jabari, Saif Eddin | IEEE | 2020