Scott Petersen

Evaluation of Nonintrusive Technologies for Traffic Detection, Phase 3

The third phase of the project on evaluation of nonintrusive technologies for traffic detection is a pooled-fund study in which field tests of selected nonintrusive sensors were conducted to determine their accuracy for volume, speed, and classification by length and by axle configuration. Sensors were evaluated in a variety of traffic and environmental conditions at two freeway test sites, with additional tests performed at both signalized and unsignalized intersections. Emphasis was placed on urban traffic conditions, such as heavy congestion, and varying weather and lighting conditions. Although previous tests evaluated sensors’ volume and speed accuracy, the current generation of nonintrusive sensors introduces robust classification capabilities. New technologies, such as ground-mounted laser sensors and improved radar, contribute to this improved performance. Overall, the sensors performed better than their counterparts in previous phases of research for volume and speed accuracy. However, the additional classification capabilities had mixed results. The length-based sensors were generally able to report vehicle lengths within their tolerances, and the axle-based sensors provided accurate interaxle measurements, but significant errors were found in relating these data with a standardized classification scheme, such as FHWAs 13-class scheme. Agencies must perform independent analysis of their classification schemes to determine whether nonintrusive sensors will provide acceptable results.

Length-Based Vehicle Classification Schemes and Length Bin Boundaries

Vehicle classification data are an important component of traffic-monitoring programs. Although most vehicle classification conducted in the United States is axle based, some applications could be supplemented or replaced by length-based data. The typically higher deployment cost and reliability issues associated with collecting axle-based data as compared with length-based data present a challenge. This paper reports on analyses of alternative length-based vehicle classification schemes and appropriate length bin boundaries. The primary analyses use data from a set of 13 Long-Term Pavement Performance weigh-in-motion sites, all in rural areas; additional analyses are conducted with data from 11 Michigan Department of Transportation weigh-in-motion sites located in rural and small urban areas and one site located in an urbanized area. For most states, the recommended length-based vehicle classification scheme is a four-bin scheme (motorcycles, short, medium, and long) with an optional very long bin recommended for use by states in which significant numbers of longer combination vehicles operate.

Sensor Performance in Measuring Vehicle Length

Although most vehicle classification conducted in the United States is axle based, some applications could be supplemented or replaced by length-based data. Common length-based methods, including loop detectors and several types of nonloop sensors (both side-fire and in-road sensors), are more widespread and can be less expensive. The most frequently deployed data collection method is by loop detector, and most dual-loop installations can report vehicle length. This paper examines field and laboratory tests of loop detectors and nonloop sensors for their performance in determining vehicle length and vehicle speed. Field testing was conducted at four locations in Minnesota and South Dakota. Ten commercially available sensors were evaluated. The testing results indicated that across a variety of detection technologies, the loop detectors and nonloop sensors generally reported comparable length and speed data. The research also examined various loop configurations and found that 6- x 6-ft loops performed similarly to 6- x 8-ft loops, although 6- x 6-ft quadrupole loops performed poorly for vehicles with high beds because of the loops’ relatively small magnetic field. Loop detector performance was found not to degrade with the variety of lead-in wire lengths that were tested. Laboratory testing conducted with a loop simulator confirmed the field testing and found that loop detector data are generally repeatable.

Methods for Estimating Axle Factors and Axle Classes from Vehicle Length Data

This study developed methods to estimate axle factors and vehicle class from length-based data streams. A set of eight methods was proposed and evaluated in different testing schemes intended to observe performance on homogeneous and heterogeneous data. The initial analysis used length-based data from 61 sites in Wisconsin. The research team compared performance of the methods estimating axle factors and vehicle class proportions. Performance was comparable and consistent between homogeneous and heterogeneous subsets of data. The research team selected two methods for a final round of analysis based on their accuracy and robustness to heterogeneity. For the final round of analysis, the research team assembled a multistate dataset using data from Wisconsin and from 14 other states represented in a dataset from the Long Term Pavement Performance program. The final round of analysis compared performance under different seasons, facility type, and road character (urban vs. rural). Performance of the two identified methods was deemed appropriate and they are recommended for implementation.