Quality Control Procedures for Weigh-in-Motion Data

Abstract

IntroductionFor the past two decades, weigh-in-motion (WIM) sensors have been used in the United States to collect vehicle weight data for designing pavements and monitoring their performance. The use of these sensors is being expanded for enforcement purposes to screen vehicles in traffic streams for overweight violations. This screening procedure is broadly referred to as a virtual weigh station. WIM sensors are also used at static weigh stations for screening vehicles to be stopped and weighed statically, resulting in reduced driver delay and increased efficiency by only weighing trucks that are close to the legal weight limit. The effectiveness of static weigh stations in Indiana for identifying overweight trucks has not been previously documented. Therefore, it was not possible to determine if the virtual weigh stations were a more effective tool and warrant widespread deployment across the state.The WIM enforcement applications require a high level of data accuracy for optimal performance. This level of accuracy has been difficult to achieve with the traditional planning and design applications because the data is aggregated and not scrutinized for individual vehicles. If the WIM sensor is weighing too light, illegal trucks may not be identified reducing the enforcement effectiveness. If the WIM sensor is weighing too heavy, legal trucks may be identified as overweight reducing the enforcement efficiency.This project was initiated to develop a quality control program for the Indiana Department of Transportation (INDOT) to improve the accuracy of the data produced from the WIM system. The quality control program provides a mechanism for assessing the accuracy of vehicle classification, weight, speed, and axle spacing data and monitoring it over time. FindingsThis study found that static weigh stations in Indiana were effective for identifying safety violations, but ineffective for identifying overweight vehicles. It was also determined that the virtual weigh stations in Indiana were found to be approximately 55 times more effective than the static weigh stations for overweight truck identification.To achieve that level of effectiveness, the virtual weigh stations require a high level of WIM data accuracy and reliability that can only be attained with a rigorous quality control program. Accurate WIM data is also essential to the success of the Long-term Pavement Performance project and the development of new pavement design methods.Robust metrics were identified for the quality control program that can be continuously monitored using statistical process control procedures that differentiate between sensor noise and events that require intervention. The speed and axle spacing accuracy is assessed by examining the drive tandem axle spacing of the Class 9 vehicle. The population average of this metric should range between 4.30 and 4.36 feet. The weight accuracy is assessed by examining the total steer axle weight and left-right steer axle residual of the Class 9 vehicle population. The population average steer ...

Implementation of Weigh-in-Motion Data Quality Control and Real-Time Dashboard Development

“…INDOT processed data for 33 WIM/VWIM sites throughout the state which produced approximately 550 million total vehicle records per year in 2016. Several performance metrics have been proposed on limited data sets in past studies (Nichols & Bullock, 2004;Nichols et al, 2009;Nichols & Cetin, 2007). This study implemented an online WIM health-monitoring tool (Figure 2.3) and examined five areas.…”

“…An improved three-component mixture model was later proposed by Nichols and Cetin (2007) to determine sensor drifting in between the loaded and unloaded peak of the GVW distribution over several weeks. In the mid-2000s, statistical control procedures were developed for identifying out-of-range measurements of front axle left and right side weight residuals and exploring the impact of minimum temperature trends on the weight differential (Nichols & Bullock, 2004;Nichols, Bullock, & Schneider, 2009). These tests were performed in accordance to existing calibration specifications outlined by ASTM E1318-09 (2017).…”

NCDOT Quality Control Methods for Weigh-in-Motion Data

“…A Microsoft Access database or spreadsheet program can be used to analyze small data sets, but they are not feasible for analyzing a statewide WIM program. Therefore, the best way to effectively handle the tremendous amount of data generated by a WIM monitoring program is to upload it to a more robust relational database, such as Oracle or Microsoft SQL Server (Nichols et al, 2004).…”

Site Verification of Weigh-in-Motion Traffic and TIRTL Classification Data

“…Data quality plays an important role in identifying truck traffic design inputs that are required in the MEPDG. Joint efforts have been made by INDOT and Purdue University to ensure WIM data quality (4). However, the algorithms used for WIM vehicle classification may not function properly in some occasions.…”

“…These planes form a box with the minimum corner at (left, bottom, near), and the maximum corner at (right, top, far). The box is translated by: (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19) It can also be given as a translation followed by a scaling of the form:…”