Calibration of automatic performance measures - speed and volume data: volume 2, evaluation of the accuracy of approach volume counts and speeds collected by microwave sensors.
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Calibration of automatic performance measures - speed and volume data: volume 2, evaluation of the accuracy of approach volume counts and speeds collected by microwave sensors.

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      This study evaluated the accuracy of approach volumes and free flow approach speeds collected by the Wavetronix SmartSensor Advance sensor for the Signal Performance Metrics system of the Utah Department of Transportation (UDOT), using the field data collected by JAMAR counter boards for free flow approach volumes and a TruCam LiDAR gun for approach speeds. The Advance sensor is primarily designed for dilemma zone reduction. It does not have the capability to differentiate vehicles between lanes, but the Advance sensor currently used has a detection range of up to 600 ft. and has the capability to track vehicles approaching the intersection. UDOT wanted to use this capability to get added values from their investment in the Advance sensors. The approach volume accuracy was analyzed with three factors: sensor position, number of approach lanes, and approach volume level. The results showed that the high accuracy is achieved when the number of approach lanes is low, or closer to one-lane, and the approach volume level is low. The overall range of accuracy for the approach volume counts was found to range from approximately 77.8% (22.2% undercount) to 105.7% (5.7% overcount). The accuracy of approach speeds was analyzed with two factors: the number of lanes and offset position of the lanes relative to the location of the speed gun. The offset position was first tested and found not to affect the accuracy of approach speeds. In general, the difference in means was approximately ±2 mph and was not considered practically significant. The 85th percentile speed for sites with more than 50 samples were then evaluated. For these sites, the average difference in 85th percentile speed was -0.43 mph, the biggest negative difference being -1.6 mph, and the biggest positive difference being 1.5 mph. A Bootstrapping analysis was then performed to predict the expected distribution of speed differences in 85th percentile speeds. This analysis also showed the 85th percentile speeds by the LiDAR gun and the Advance sensor were not significantly different for practical traffic engineering applications.
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