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Throughout the field of transportation engineering, decision makers require quality information. The information used in transportation operations, planning, and design is based, in part, on data from traffic detectors. The need for quality data has spurred innovations in data collection including the introduction of modern, commercially available, non-intrusive traffic detectors. As these new technologies become available, a need exists to understand their capabilities and limitations—especially limitations that are unique to a specific region.
This thesis examined the accuracy of four non-intrusive traffic detector technologies considered for potential data collection applications on Nebraska’s highways. The technologies evaluated included the Solo Pro II video image processor (VIP), 3M Canoga Microloop 702 magnetic induction detector, Image Sensing Systems RTMS G4 microwave radar detector, and Wavetronix SmartSensor 105 microwave radar detector. These four detectors were installed at the NTC/NDOR non-intrusive detector test bed along Interstate 80 near the Giles Road interchange in Omaha, Nebraska. Data were collected in June, July, and August of 2011, and these detectors were analyzed based on the accuracy of their volume, speed, and length-based vehicle classification.
The analysis in this thesis utilizes numerous graphical and statistical methods to demonstrate the significance of errors in the data from the four evaluated detectors. The impacts of lighting, rain, traffic volume, and various levels of temporal aggregation on the detectors’ accuracies were analyzed. Multiple regression analysis revealed that the volume accuracy of the Solo Pro II was affected by night lighting, as well as by the combined effect of dawn lighting and rain. The volume accuracies of the Microloop 702 and G4 were significantly affected by the combination of dusk lighting and rain, while the volume accuracy of the SmartSensor 105 was not found to be significantly affected by lighting or rain conditions. In addition to these results, this thesis analyzed the collected data in order to provide hypotheses pertaining to potential links between significant environmental factors and physical operating characteristics of the evaluated non-intrusive traffic detectors.