Traffic data collection and traffic state estimation are essential for traffic congestion management within smart cities framework. To overcome the inherent shortcomings of single-source data collection techniques, this paper investigates a data fusion method, employing fix and floating (or probe) data types, to estimate traffic states on both freeway and signalized arterial links. The proposed method explores the relationships among the number of probe data, sampling interval of probe vehicles, probe vehicles penetration rate, estimation interval, and traffic density. This paper introduces the definition of generalized penetration rate based on the quotient of travel time of probe vehicles and total travel time. Furthermore, traffic generalized density is estimated with probe data and observed long-term average of actual penetration rate of probe vehicles. Furthermore, real-time penetration rate of probe vehicles is estimated with probe data and density measurements collected by fixed loop detectors. The validity of the proposed method is investigated by extensive tests on NGSIM field data and microsimulation data that reveal accurate performance for real-time traffic state estimation. The proposed methods demonstrate promising extensions for network-wide traffic state estimation.


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    Title :

    Integration of loop and probe data for traffic state estimation on freeway and signalized arterial links


    Contributors:


    Publication date :

    2017-10-01


    Size :

    124173 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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