The invention provides a traffic flow missing data estimation method based on the MCMC algorithm. The estimation method comprises the steps that S1) traffic flow data of N continuous days is received, a vector set of the traffic flow data is obtained according to the traffic flow data of the N continuous days, the vector set of the traffic flow data comprises observation data and missing data, and N represents a positive integer; S2) a Gaussian model is set according to the vector of the traffic flow data in the ith day; S3) the occurrence probability of the missing data is calculated according to an estimation value of a parameter space of the Gaussian model, the occurrence probability of the parameter space is calculated according to the present observation data and the newest missing data, and the estimation value of the parameter space of the Gaussian model is updated according to the occurrence probability of the parameter space; and S4) the step S3) is implemented repeatedly till Markov chain convergence is obtained, and the missing data of the traffic flow is estimated and thus, obtained. The method of the invention can greatly improve the estimation precision and speed of the missing data of the traffic flow.


    Access

    Download


    Export, share and cite



    Title :

    Traffic flow missing data estimation method based on Markov chain Monte Carlo (MCMC) algorithm


    Contributors:
    LI ZHIHENG (author) / ZHANG YI (author) / LI LI (author) / YAO DANYA (author) / HU JIANMING (author) / WANG SHUOFENG (author)

    Publication date :

    2015-12-30


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung





    Image Segmentation by Data Driven Markov Chain Monte Carlo

    Tu, Z. / Zhu, S. / Shum, H. et al. | British Library Conference Proceedings | 2001


    Sensitivity Analysis of Markov Chain Monte Carlo

    Millwater, Harry / Vazquez, Eric / Wu, Justin et al. | AIAA | 2010


    Markov Chain Monte Carlo Modular Ensemble Tracking

    Penne, T. / Tilmant, C. / Chateau, T. et al. | British Library Online Contents | 2013