This study evaluates the accuracy of approach volumes and free flow approach speeds collected by the Wavetronix SmartSensor Advance sensor 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 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. The Utah Department of Transportation (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. It was found that the accuracy of the approach volume counts was not affected by the sensor position. As a result of the sensor's inability to differentiate lanes, the more cars travel alongside each other, the more likely they are to be detected together as one vehicle. The overall range of accuracy for the approach volume counts was found to range from approximately 76% (24% undercount) to 106% (6% 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. First, the lane position and offset were tested to see if any effect exists on the difference between the measurements of the speed by the LiDAR gun and the Advance sensor. Then the difference between mean speeds was tested. Each site was analyzed individually and there were some sites which had a statistically significant difference while there were others which did not. However, the difference was considered not to be practically significant because of the difference in mean speeds of the sample being approximately ±2 mph. The speeds were also used to calculate the 85th percentile speed for all sites with more than 50 samples. For these sites, the average difference in 85th percentile speed was -0.43 mph, the biggest negative difference was -1.6 mph, and the biggest positive difference was 1.5 mph. Because of the limited number of samples taken at each site, a statistical resampling method called Bootstrapping was performed to predict the expected distribution of speed differences in 85th percentile speeds. The results of 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. However, it is recommended that more research be performed to better understand the applicability of 85th percentile speed measurements.

Over the past few years, the Utah Department of Transportation (UDOT) has developed a system called the Signal Performance Metrics System (SPMS) to evaluate the performance of signalized intersections. This system currently provides data summaries for several performance measures including: 1) Purdue Coordination Diagram, 2) Speed, 3) Approach Volume, 4) Purdue Phase Termination Charts, 5) Split Monitor, 6) Turning Movement Volume Counts, 7) Arrivals on Red, and 8) Approach Delay. There is a need to know the accuracy of the data that are being collected by the Wavetronix SmartSensor Matrix and displayed in the SPMS. The TAC members determined that the following factors would affect the accuracy of radar-based traffic sensors the most: sensor position, number of approach lanes, and volume level. The speed limit factor was added to the study after most of the data collection was completed. The purpose of this research was to collect data at various intersections to determine the accuracy of the data collected by the Wavetronix SmartSensor Matrix.A Mixed Model Analysis of Variance (ANOVA) was employed to analyze the effects that each factor had on the accuracy of the traffic volume count. A total of 14 tests were performed to examine the effects of the factors on traffic volume count accuracy. The sensor position factor was not found to be a statistically significant factor affecting the accuracy of traffic volume counts. The effect of speed limit on traffic volume count accuracy was determined to be inconclusive due to the lack of samples to be tested. The remaining two factors, volume level and number of approach lanes, were found to have a statistically significant effect on the accuracy of traffic volume counts. Based on these two factors, a matrix was created to meet the needs of UDOT to present accuracy values on the SPMS website. This matrix includes the mean, 95 percent confidence interval of the mean, standard deviation, number of samples, and the minimum number of samples needed.