This paper describes research investigating corridor travel time mean and variance estimation. Simultaneous travel time data were collected along two corridors of approximately 2.0 miles (3.2 kilometers) each. Travel time data from test vehicles instrumented with distance measuring instruments (DMIs), commercial vehicles by video, and intelligent transportation systems (ITS) data sources were collected. Link and corridor travel times were estimated from the ITS data source (either AVI or inductance loop detectors) and compared with travel times measured from instrumented test vehicles and commercial vehicles. Several important findings are described in this paper. AVI was found to be a cost-effective travel time data collection method based on a comparison of travel time estimates with estimates obtained from instrumented test vehicles. A procedure was developed to identify which drivers are in need of additional training on the test vehicle data collection technique. Commercial vehicles were found to have longer travel times than those reported by the ITS data source along the two study corridors. Finally, an empirical analysis is provided that identifies corridor travel time mean and variance estimates from link travel time mean and variance estimates that make up the entire corridor. The correlation between adjacent links along a corridor was investigated with empirical ITS travel time data from each site. Finally, the difficulty of using spot-speed measurements (i.e., estimates from loop detectors) for corridor travel time variance estimates is also identified.


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    Title :

    Lessons Learned Using ITS Data Sources to Estimate Corridor Travel Time Mean and Variance


    Contributors:

    Conference:

    Seventh International Conference on Applications of Advanced Technologies in Transportation (AATT) ; 2002 ; Boston Marriot, Cambridge, Massachusetts, United States



    Publication date :

    2002-07-31




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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