As with travel time collection, the accuracy of observed travel time and the optimal travel time data quantity should be determined before using travel time reliability (TTR) data. The statistical accuracy of TTR measures should be evaluated so that the statistical behavior and belief can be fully understood. More specifically, this issue can be formulated as a question: using a certain amount of travel time data, how accurate is the TTR for a specific freeway corridor, time of day, and day of week? A framework for answering this question has not been proposed previously. Our study proposes a framework based on bootstrapping to evaluate the accuracy of TTR measures and answer the question. Bootstrapping is a computer-based method for assigning measures of accuracy to multiple types of statistical estimators without requiring a specific probability distribution. Three scenarios representing three traffic flow conditions (free-flow, congestion, and transition) were used to evaluate the accuracy of TTR measures under different traffic conditions and quantities of data. The results of the accuracy measurements demonstrated that: (1) the proposed framework supports assessment of TTR accuracy and (2) stabilization of the TTR measures did not necessarily correspond to statistical accuracy. The findings in our study also suggested that moment-based TTR measures may not be statistically sufficient for measuring freeway TTR. Additionally, our study suggested that 4 or 5 weeks of travel time data is sufficient for measuring freeway TTR under free-flow conditions, 40 weeks for congested conditions, and 35 weeks for transition conditions.


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    Titel :

    How accurate is your travel time reliability?—Measuring accuracy using bootstrapping and lognormal mixture models


    Beteiligte:
    Yang, Shu (Autor:in) / Cooke, Payton (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2018-11-02


    Format / Umfang :

    15 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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