Weigh-in-Motion (WIM) systems have improved the process of collecting data from heavy vehicles on the U.S. highway system and enforcing the laws that govern vehicle weights. The benefits of WIM are reaped by everyone from highway designers and voernments officials, to truck drivers and transportation industry owners. The data collected by WIM devices is essential for proper pavement design, developing pavement management systems, weight enforcement strategies, modeling traffic improvement projects, and predicting load-related distresses and performance. While WIM offers many advantages over its alternative, static weighing, the technology is limited by problems associated with the accuracy of its measurements. Weigh-in-Motion systems that lack accuracy require vehicles to travel slower and can result in higher queues, longer delays, and potential hazards. For these reasons, WIM system performance must be improved in order to adequately serve its purpose. In order to evaluate WIM system performance and determine what vehicle characteristics have the most affect on it, two systems in the Commonwealth of Virginia were evaluated. The first system was an in-service WIM system at the Troutsville weigh station on I-81. The Troutsville station had bending plate WIM scales located in both the northbound and southbound directions. The second system in a newly developed WIM system manufactured by Omni Weight Corporation (OWC) and was installed at the Virginia Smart Road for evaluation. The OWC scale is a completely sealed and buried system that has ten strain gauge sensors in its interior. Evaluation of both scales was performed by conducting a number of test runs under varying load conditions. Testing at Troutsville was performed using four different test vehicles with multiple loads on each. Variation in load was achieved by loading the test vehicles with various numbers of concrete Jersey Walls. Testing on the OWC scale was performed using only two test vehicles while varying the speed, load, tire pressure, and direction of travel over the scale. The study showed that the scales at the Troutsville weigh station yielded 10% error or less on only 77% of the tests, not complying with the required 95% set forth by ASTM E-1318. In comparison, using the manufacturer's processed data for the OWC scale yielded only 18% of its tests with 10% error or less, far below the ASTM standard. A model was developed to re-calculate the axle weights using the raw sensor data from the OWC scale; and an evaluation of the accuracy of this data showed that the OWC scale performed much better. While compliance with the ASTM standards was still not achieved, it rose from 18% to 71% of the tests having 10% error or less. Repeatability of the Troutsville scales and OWC scales was found to be comparable. / Master of Science

Weigh-in-Motion (WIM) systems improve the capacity of weigh station operations significantly by screening trucks while traveling at high speeds and only requiring trucks within a threshold of a maximum permissible gross of axle weight to be weighed on more accurate static scales. Consequently, the operation of a weigh station is highly dependent on the accuracy of the screening WIM system. This thesis develops a procedure for relating axle accuracy to gross vehicle accuracy and develops a field and modeling framework for evaluating weigh station operations. The WIM scale operation at the Stephens City weigh station in Virginia is examined to demonstrate how the field and modeling framework can be applied to evaluate the operation of a weigh station. Specifically, the field evaluation evaluated the accuracy of the WIM technology in addition to the operations of the weigh station in terms of service time, system time, and delay incurred at the static scales. During the field evaluation of the Stephens City WIM load cell system, the WIM technology was found to estimate truck weights to within 6 and 7 percent of the static weights 95 percent of the time. The modeling framework provides a methodology that can be used to determine the effects of the truck demand, the WIM accuracy, the system threshold, and the WIM calibration on system performance. The number of vehicles sent to the static scale and bypass lanes as well as the amount of delay experienced were analyzed for various system characteristics. The proposed framework can be utilized to estimate vehicle delay at a weigh station. / Master of Science