Andrew P. Nichols

Quality Control Procedures for Weigh-in-Motion Data

For 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 now being expanded for enforcement purposes to provide virtual weigh stations for screening vehicles in traffic streams for overweight violations. A 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 alternative approach to identifying overweight vehicles using virtual weigh stations requires 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. This report proposes a quality control program that addresses vehicle classification, speed, axle spacing, and weight accuracy by identifying robust metrics 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 population of Class 9 vehicles. The weight accuracy is assessed by examining the left-right steer axle residual weight of the population of Class 9 vehicles. Data mining of these metrics revealed variations in the data caused by incorrect calibration, sensor failure, temperature, and precipitation. The accuracy metrics could be implemented in a performance-based specification for WIM systems that is more feasible to enforce than the current specifications based on comparing static vehicle weights with dynamic vehicle weights measured by the WIM sensors. The quality control program can also be used by agencies to prioritize maintenance to more effectively allocate the limited funds available for sensor repair and calibration. This research provides a tool that agencies can use to obtain and sustain higher quality WIM data.

Performance Measures for Adaptive Signal Control: Case Study of System-in-the-Loop Simulation

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.

Bayesian Models for Reidentification of Trucks Over Long Distances on the Basis of Axle Measurement Data

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.

Design Guidelines for Deploying Closed Loop Systems

Closed loop systems are becoming the preferred method of operating coordinated traffic signal systems. Some of the benefits of a closed loop system include remote controller access, synchronization of controller time clocks, and orderly transition between coordinated plans. With ensured time clock synchronization, the coordination of traffic signals becomes more reliable, which leads to optimal vehicle progression through the system. However, due to the complexity and relative infrequent implementation in more rural districts of closed loop systems, it is very difficult to obtain a successful deployment. This manual provides a step-by-step procedure for designing the parameters, implementing, testing, and field tuning closed loop system. The procedure is described for an example closed loop system consisting of five intersections in West Lafayette, Indiana. The guidelines for the design of signal timing parameters will serve as a manual for all Indiana Department of Transportation districts. Detailed configuration procedures are provided for Econlite and Peek controllers, as well as their respective management software packages. Also included in the implementation procedure are instructions for interconnecting the controllers and establishing communication between the controllers. The testing procedure described in this manual is a hardware-in-the-loop simulation, which uses a controller interface device in conjunction with CORSIM software. Field tuning practices are documented for detectors, timing parameters, and coordination plans.

A Bayesian analysis of the effect of estimating annual average daily traffic for heavy-duty trucks using training and validation data-sets

The precise estimation of annual average daily traffic (AADT) is of significant importance worldwide for transportation agencies. This paper uses three modeling frameworks to predict the AADT for heavy-duty trucks. In total, 12 models are developed based on regression and Bayesian analysis using a training data-set. A separate validation data-set is used to compare the results from the 12 models, spanning the years 2005 through 2007 and taken from 67 continuous data recorders. Parameters of significance include roadway functional class, population density, and spatial location; five regional areas – northeast, northwest, central, southeast, and southwest – of the state of Ohio in the USA; and average daily truck traffic. The results show that a full Bayesian negative binomial model with a coefficient offset is the most efficient model framework for all four seasons of the year. This model is able to account for between 87% and 92% of the variability within the data-set.

Key Factors Affecting the Accuracy of Reidentification of Trucks over Long Distances Based on Axle Measurement Data

Vehicle reidentification methods can be used to anonymously match vehicles crossing two locations based on vehicle attribute data. This paper investigates key factors that affect the accuracy of vehicle reidentification algorithms. The analyses are performed with reidentification algorithms to match commercial vehicles that cross upstream and downstream pairs of weigh-in-motion (WIM) sites that are separated by long distances, ranging from 70 to 214 mi. The data to support this research come from 17 fixed WIM sites in Oregon. Data from 14 pairs of WIM sites are used to evaluate how various factors affect matching accuracy; factors include the distance between two sites, travel time variability, truck volumes, and sensor accuracy or consistency of measurements. After the vehicle reidentification algorithm is run for each of these 14 pairs of sites, the matching error rates are reported. The results from the testing data sets show a large variation in terms of accuracy. Sensor accuracy and volumes have the greatest impacts on matching accuracy; distance alone does not have a significant effect.

Detecting Differential Drift in Weigh-in-Motion Wheel Track Sensors

Weigh-in-motion (WIM) sensors are being used by state agencies for commercial vehicle weight enforcement applications that require a high level of accuracy for determining individual vehicle weights. Existing methods of evaluating WIM weight data accuracy have not been effective for detecting small but consistent calibration drifts. This paper proposes the use of a left–right differential metric that is computed as a percentage difference between the left and right wheel weights on the steer axle of Class 9 trucks for WIM sensors that measure and report individual left and right wheel weights. In this paper, the left–right differential is monitored over time by using statistical process control charts to identify when sensors are exhibiting statistically significant variations. The monitoring procedure can be integrated into existing quality control programs. Data from WIM sites in Indiana and California were used to evaluate the effectiveness of this metric.

Improving the Accuracy of Vehicle Reidentification Algorithms by Solving the Assignment Problem

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.

PLANNING PROCEDURES FOR ESTIMATING AN UPPER BOUND ON BUS PRIORITY BENEFITS

Bus priority system (BPS) developments have increased significantly over the past decade in North America. The purported benefits of a BPS are improved schedule adherence and overall reduced travel time. Benefits to the bus agency include reduced wear and tear on buses and improved fuel efficiency, which reduces operating costs. BPS costs can range from

Virtual Weigh Station

8,000 to