, Professor, UMD-Electrical Engineering
Currently the Transportation Data Forecast and Analysis (TFA) section of MnDOT collects vehicle classification counts on roadways with higher traffic volumes that have restrictions on placing road tubes. This project would give MnDOT TFA an opportunity to collect vehicle classification and volume data from existing systems without investing capital equipment nor man power.
The objective of this project is to extract length-based vehicle classification data from the MnDOT Regional Transportation Management Center (RTMC) system and integrate them into the current traffic volume database. Currently, Wavetronix radar sensors and certain loop detectors in the Twin Cities' freeway network provide length-based vehicle classification through the RTMC Intelligent Roadway Information System (IRIS) server. This classification data is reported as vehicle counts per
detector in four classes, i.e., (1) motorcycles, (2) short vehicles (8' - 20'), (3) medium vehicles (20' -43'), and (4) long vehicles (43' or longer). This project would extract these RTMC length classification data and then integrate them to the current detector volume and health-parameter database. The classification data will be collected from the entire detectors in which the RTMC
server provides length classification. A software tool that can retrieve classification data and create a report will be developed as part of this project.