When adaptive traffic control systems (ATCSs) are evaluated, traffic signal engineers and practitioners often collect data for a few weeks before and after installation. Benefits are then estimated on the basis of this limited data set. The evaluation of an ATCS with microsimulation requires considerable collection of field data. However, once an ATCS is installed, an abundance of data is collected and stored by the ATCS itself. These data (mostly traffic volumes) can be used to recreate field variability of traffic conditions in a model and perform long-term ATCS evaluation studies. This paper reports on the projected long-term benefits of deploying an ATCS. Field traffic data were statistically processed and modeled in microsimulation. The Sydney coordinated adaptive traffic system (SCATS) and two time-of-day (TOD) plans were exposed to variability of field traffic flows modeled in VISSIM. A simple calculation was then performed to extrapolate results to a period of 10 years. Findings showed that SCATS outperformed existing TOD signal-timing plans by about 20% and was better than the best TOD plan that could be theoretically developed on the basis of collection of long-term data. Results from the study revealed that short-term analyses often obscured the true benefits of deploying an ATCS. A computation of the monetary value of achieved benefits showed that limited operational benefits reported in this paper, when projected over the long term, would exceed overall installation costs for SCATS in Park City, Utah. These benefits are expected to increase further with inclusion of the analysis periods that were not part of this study.


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    Title :

    Long-Term Benefits of Adaptive Traffic Control under Varying Traffic Flows during Weekday Peak Hours


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2012-01-01




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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