Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota
In this project, we developed an innovative methodology to solve a long-standing traffic engineering problem, i.e.
measuring traffic speed using data from single inductive loop detectors. Traditionally, traffic speeds are estimated using aggregated detector data with a manually calibrated effective vehicle length. The calibration effort (usually through running probe vehicles), however, is time consuming and costly. Instead of using aggregated data, in this project, our data collection system records every vehicle-detector actuation "event" so that for each vehicle we can identify the time gap and the detector occupation time. With such high-resolution "event-based" data, we devised a
method to differentiate regular cars with longer vehicles. The proposed method is based on the observation that longer vehicles will have longer detector occupation time. Therefore, we can identify longer vehicles by detecting the changes of occupation time in a vehicle platoon. The "event-based" detector data can be obtained through the implementation of the SMART-Signal (Systematic Monitoring of Arterial Road Traffic Signals) system, which was
developed by the principal investigator and his students in the last five years. The method is tested using the data from Trunk Highway 55, which is a high-speed arterial corridor controlled by coordinated traffic signals. The result shows that the proposed method can correctly identify most of the vehicles passing by inductive loop detectors. The identification of long vehicles will improve the estimation of effective vehicle length on roads. Consequently, speed estimation from the inductive loop detector is improved.
Department of Civil Engineering,
University of Minnesota
Liu, Henry; Sun, Jie.
Improving Freeway Traffic Speed Estimation Using High-Resolution Loop Detector Data.
Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota.
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