Highlights ► We present a method for estimating missing traffic volumes on an urban road network in real-time. ► Estimates of missing data are based on traffic equilibrium principles. ► The approach was developed to place the time-consuming computation in an offline phase. ► The objective is to have a real-time phase which is scalable to full city-wide deployments. ► The value of real-time data increases with variability and the proportion of links missing data.

    Abstract This paper presents a method for estimating missing real-time traffic volumes on a road network using both historical and real-time traffic data. The method was developed to address urban transportation networks where a non-negligible subset of the network links do not have real-time link volumes, and where that data is needed to populate other real-time traffic analytics. Computation is split between an offline calibration and a real-time estimation phase. The offline phase determines link-to-link splitting probabilities for traffic flow propagation that are subsequently used in real-time estimation. The real-time procedure uses current traffic data and is efficient enough to scale to full city-wide deployments. Simulation results on a medium-sized test network demonstrate the accuracy of the method and its robustness to missing data and variability in the data that is available. For traffic demands with a coefficient of variation as high as 40%, and a real-time feed in which as much as 60% of links lack data, we find the percentage root mean square error of link volume estimates ranges from 3.9% to 18.6%. We observe that the use of real-time data can reduce this error by as much as 20%.


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    Titel :

    Real-time traffic estimation using data expansion


    Beteiligte:
    Lederman, Roger (Autor:in) / Wynter, Laura (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2011-05-16


    Format / Umfang :

    18 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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