The evolution of telecommunications and wireless technologies has brought in new sources of traffic data (particularly mobile data generated by vehicle probes), which could offer a breakthrough in the quality and extent of traffic data. This study reviews the Weather- Responsive Traffic Management Models (WRTM) models which were developed in previous FHWA funded weather-related projects and identifies the components within WRTM framework where mobile data could be incorporated, mainly, (1) supply-side model calibration; (2) demand-side calibration; (3) model validation; and (4) on-line implementation. This report summarizes the unique properties of mobile data in contrast to traditional traffic data, particularly regarding its much wider geographic coverage and travel time information. The different types of mobile data which could be offered from major vendors are also discussed. The study finds that vehicle trajectory data serves best for the purpose of improving WRTM models, from calibration of supply and demand side relations and model validation to the case of the on-line TrEPS implementation. A framework for how to implement the integration of mobile data and WRTM models was also developed. In this project the process of following the framework and incorporating mobile data into WRTM models is demonstrated by a case study. DYNAMSART (DYnamic Network Assignment-Simulation Model for Advanced Road Telematics), a DTA simulation-based TrEPS, is selected for this study. Vehicle trajectory data, collected by vehicles equipped with TomTom GPS devices circulating in New York City area during a two-week period, is also used.


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    Title :

    Use of Mobile Data for Weather-Responsive Traffic Management Models


    Contributors:
    H. Mahmassani (author) / R. Mudge (author) / T. Hou (author) / J. Kim (author)

    Publication date :

    2012


    Size :

    92 pages


    Type of media :

    Report


    Type of material :

    No indication


    Language :

    English




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