Travel time and its reliability are intuitive performance measures for freeway traffic operations. The objective of this project was to quantify segment-based and corridor-based travel time reliability measures on urban freeways. To achieve this objective, a travel-time estimation model and a travel-time reliability prediction framework were developed. The proposed travel-time estimation model considers spatially correlated traffic conditions. Segment-level and corridor-level travel-time distributions were estimated using travel time estimates and compared with estimates based on probe vehicle data. Corridor-level travel-time reliability measures were extracted from travel-time distributions. The proposed travel-time estimation model can well capture the temporal pattern of travel time and its distribution. For the corridor-level travel-time reliability prediction framework, travel time observations are classified based on weather conditions, segment travel-time distributions are estimated, and segment travel-time distributions are synthesized to corridor travel-time distributions. The segment travel-time distribution estimation model was found to capture the pattern of actual travel-time distributions and could adequately represent the short-term corridor-level travel-time distributions. The proposed travel-time reliability prediction framework provides a systematic way to estimate real-time and near-future corridor travel-time reliability by considering weather impact. A Vissim simulation calibrated to Iowa compared travel-time distribution based on simulated data to that based on probe vehicle data. The simulated travel-time distribution is similar to the travel-time distribution based on probe data.


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    Title :

    Assessing Segment- and Corridor-Based Travel-Time Reliability on Urban Freeways


    Contributors:
    J. Dong (author) / C. Lu (author) / C. Liu (author) / N. Hawkins (author)

    Publication date :

    2016


    Size :

    87 pages


    Type of media :

    Report


    Type of material :

    No indication


    Language :

    English







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