The role of topography on the spatial distribution of tornadoes was assessed through geospatial and statistical techniques. A 100-m digital elevation model was used to create slope, aspect, and surface roughness maps; and; tornado beginning and ending points and paths were used to extract terrain information. Tornado touchdowns, liftoffs, paths, and path-land angles were examined to determine whether tornado paths occur more frequently in or along certain terrain or slopes. Statistical analyses, such as bootstrapping, were used to analyze tornado touchdowns, liftoffs and paths and path-relative terrain angles. Results show that tornado paths are more common with downhill-movement. Tornadoes are not as likely to move uphill because the 73.6 percent northeast path bias represents the highest frequencies of path-angles. Tornado touchdowns and paths occur more often in smooth terrain, rather than rough terrain. Complex topographic variability seems to not have an effect on the spatial distribution of tornadoes.

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.