Anonymous probe vehicle data are being collected on roadways throughout the United States. These data are incorporated into local and statewide mobility reports to measure the performance of highways and arterial systems. Predefined spatially located segments, known as traffic message channels (TMCs), are spatially and temporally joined with probe vehicle speed data. Through the analysis of these data, transportation agencies have been developing agencywide travel time performance measures. One widely accepted performance measure is travel time reliability, which is calculated along a series of TMCs. When reliable travel times are not achieved because of incidents and recurring congestion, it is desirable to understand the time and the location of these occurrences so that the corridor can be proactively managed. This research emphasizes a visually intuitive methodology that aggregates a series of TMC segments based on a cursory review of congestion hotspots within a corridor. Instead of a fixed congestion speed threshold, each TMC is assigned a congestion threshold based on the 70th percentile of the 15-min average speeds between 02:00 and 06:00. An analysis of approximately 90 million speed records collected in 2013 along I-80 in northern New Jersey was performed for this project. Travel time inflation, the time exceeding the expected travel time at 70% of measured free-flow speed, was used to evaluate each of the 166 directional TMC segments along 70 mi of I-80. This performance measure accounts for speed variability caused by roadway geometry and other Highway Capacity Manual speed-reducing friction factors associated with each TMC.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Performance Measures to Characterize Corridor Travel Time Delay Based on Probe Vehicle Data


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2019-06-06




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English