It is known that short-term traffic state forecasting is one of the most critical aspects in intelligent transportation systems (ITS). Previous attempts to forecast short-term traffic state concentrate on single-spot forecasting. Multi-spot forecasting based on multivariate chaotic time series analysis is proposed in this paper, where traffic states in different spots are considered as a whole. Multivariate time series derived from multi-spot traffic state data are reconstructed with time delays and embedding dimensions based on multivariate phase space reconstruction theory. Then performing forecasting model, multi-spot traffic state can be obtained from new input data. To verify that the proposed method performs better than univariate ones, real time data each 6 mins traffic volume of six cross-sections in six continuous spots on Beijing Second Loop-line expressway are illustrated.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    A Multivariate Chaotic Time Series Approach for Road Network Short-Term Traffic State Forecasting


    Contributors:

    Conference:

    First International Conference on Transportation Engineering ; 2007 ; Southwest Jiaotong University, Chengdu, China



    Publication date :

    2007-07-09




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    A Multivariate Chaotic Time Series Approach for Road Network Short-Term Traffic State Forecasting

    Yao, Z.-s. / Shao, C.-f. / Xiong, Z.-h. et al. | British Library Conference Proceedings | 2007


    Multivariate Short-Term Traffic Flow Forecasting Using Time-Series Analysis

    Ghosh, Bidisha / Basu, Biswajit / O'Mahony, Margaret | IEEE | 2009



    A Bayesian network approach to time series forecasting of short-term traffic flows

    Changshui Zhang, / Shiliang Sun, / Guoqiang Yu, | IEEE | 2004


    Short term traffic forecasting using time series methods

    Moorthy, C. K. / Ratcliffe, B. G. | Taylor & Francis Verlag | 1988