Nye, Benjamin Gilbert2017-11-162017-11-162017-12-01http://hdl.handle.net/2097/38225Each year maintenance and rehabilitation occur on interstates and highways to repair damage, improve rideability, and increase safety. To perform many of these activities a short or long-term work zone is required. However, short and long-term work zones can have significant impacts on traffic flow, especially during peak travel times. To mitigate the impact of work zones, the Kansas Department of Transportation (KDOT) has developed a lane closure guide to assist KDOT personnel and contractors in determining times during the day that a lane can be closed to traffic. The existing lane closure guide was comprised of limited data sources and assumptions based on past traffic counts. The purpose of this research study was to evaluate the existing guide and update it using a consistent data source that reflects current roadway conditions. During the evaluation of the existing lane closure guide, several inconsistencies with traffic counts, directional splits, and adjustment factors were found. To eliminate the consistencies, data from the Kansas City Traffic Management Center was used. During the procedure of updating the lane closure guide a repeatable data extraction process and a quality assurance/quality control process were utilized. In addition to updating the KDOT lane closure guide, sensor data verification was performed on one KC Scout sensor on K-10 using road tubes. The data from the road tubes was then compared to the data extracted from KC Scout during the same time interval. The comparison found the road tubes and KC Scout counted statistically the same number of cars for the chosen interval. However, the comparison found the road tube’s average speed for chosen interval to be on average 10 percent higher than KC Scout, which was statistically significant.en-USWork zonesTMC dataLane closureEvaluating and updating the Kansas Department of Transportation’s lane closure guide in the Kansas City metropolitan area using Traffic Management Center dataThesis