guidestar Collage
Non-Intrusive Traffic Detection (NIT) Phase III
Minnesota Guidestar
ITS Home | Projects | Contact Us

guidestar logo


Project Benefits

This project found that the current generation improves upon past sensors by providing a more robust data set beyond volume and speed data. Classification is an important capability that has been added and improved over past models. Along with this new function comes a new set of demands for ensuring that vehicles are detected and classified properly. While the length-based sensors generally provided relatively accurate length data, the axle-based sensors sometimes had issues with classifying trucks due to axle-counting errors.


Project Team


Project Documents

Evaluation of Non-Intrusive Technologies for Traffic Detection (NIT) Phase III


Project Description


The third phase of the “Evaluation of Non-Intrusive Technologies for Traffic Detection” (NIT) project is a pooled fund study led by the Minnesota Department of Transportation (Mn/DOT), with technical guidance from the project’s Technical Advisory Committee (TAC) and the Federal Highway Administration (FHWA). This phase of the project (TPF-5[171]) focused on conducting field tests of selected non-intrusive sensors to determine their accuracy for volume, speed and classification by length and classification by axle configuration. The project also identified deployment issues and costs associated with the technologies. 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. Standardized testing criteria were followed so that the results from this project can be directly compared to results obtained by other transportation agencies.


While previous tests have evaluated sensors’ volume and speed accuracy, the current generation of non-intrusive sensors has introduced robust classification capabilities. New technologies, such as axle detection sensors, and improved radar, contribute to this improved performance.