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 ...

The simulation of local signal controllers has become increasingly sophisticated in recent years and has been paralleled by improvements in the integration of adaptive systems into simulation. This paper describes and demonstrates an emerging methodology for the evaluation of adaptive signal control that is termed “system-in-the-loop simulation.” This methodology extends existing software-in-the-loop simulation by linking virtualized traffic controllers with real-world adaptive-control systems. In addition, the authors propose an analysis methodology that fuses data on simulated probe vehicles with data on high-resolution controller events. Through this data fusion, traditional measures of simulation performance such as delay can be enhanced with operational measures of performance that characterize quality of progression and capacity utilization. In addition, adaptive-control performance can be characterized in relation to overall impact on traveler delay and also described in terms that are meaningful for improvement of control schemes. An example case study is presented: the ACS-Lite adaptive system was tested on a 19-intersection system in Morgantown, West Virginia, under a special-event scenario. Free, fully actuated control was compared with traditional time-of-day and traffic-responsive control both with and without the use of the adaptive-control system ACS-Lite. Overall delay results are presented and contrasted with more detailed analysis of event-based performance measures at a single intersection and on a networkwide basis.

The estimation of annual average daily traffic (AADT) is an important parameter collected and maintained by all US departments of transportation. There have been many past research studies that have focused on ways to improve the estimation of AADT. This paper builds upon previous research and compares eight methods, both traditional and cluster-based methodologies, for aggregating monthly adjustment factors for heavy-duty vehicles (US Department of Transportation Federal Highway Administration (FHWA) vehicle classes 4Á13). In addition to the direct comparison between the methodologies, the results from the analysis of variance show at the 95% confidence level that the four clusterbased methods produce statistically lower variance and coefficient of variation over the more traditional approaches. In addition to these findings Á which are consistent with previous total volume studies Á further analysis is performed to compare total heavy-duty monthly adjustment factors, both directions of traffic, with direction-based monthly adjustment factors. The final results show that the variance as well as the coefficient of variation improve on average by 25% when directional aggregate monthly adjustment factors are used instead of total direction.

Vehicle reidentification methods can be used to anonymously match vehicles crossing 2 different locations on the basis of vehicle attribute data. In this article, reidentification methods are developed to match commercial vehicles that cross 2 weigh-in-motion sites in Oregon that are separated by 145 miles. Using vehicle length and axle data as attributes to characterize vehicles, a Bayesian model is developed that uses probability density functions obtained by fitting Gaussian mixture models to a sample data set of matched vehicles. The reidentification model when applied to a test data set (where each downstream vehicle also crosses the upstream site) matches vehicles with an accuracy of 91% when both axle weight and axle spacings data are used. To account for the fact that not all vehicles in the downstream also cross the upstream site, an additional new step is developed to screen mismatched vehicles produced by the algorithm. For this step, several screening methods are developed that allow the user to trade off the total number of matched vehicles and error rate. For evaluating the effectiveness of the screening methods, 2 scenarios are considered. In the first scenario, only common vehicles that cross both the upstream and downstream sites are considered, whereas in the second scenario all downstream vehicles are considered. It is shown that the mismatch error can be reduced to as low as 1% and 5% at the expense of not matching about 25% of the common vehicles (crossing both sites) for the first and second scenarios, respectively.

Vehicle attributes (e.g., length, sensor signature) collected at upstream and downstream points can be used to reidentify individual vehicles anonymously so that useful quantities such as travel times and origin–destination flows can be estimated. In typical reidentification algorithms, each downstream vehicle is matched to the most “similar” upstream vehicle on the basis of some defined metric. However, this process usually results in matching one upstream vehicle to more than one downstream vehicle, and some upstream vehicles are not assigned to any downstream vehicles. This paper presents a two-stage methodology to alleviate this problem, first by developing a Bayesian method for matching the most similar vehicles and then by defining and solving an assignment problem to ensure that each vehicle is matched only once. The results indicate that the proposed method, when applied to the sample field data collected by automatic vehicle classification and weigh-in-motion sensors, reduces the mismatch error by 15% to 60% and by an overall average of 42%. For the sample data, vehicles are matched with 99% accuracy after the methodology presented here is applied.