Browsing by Subject "Vehicle detectors"

Now showing 1 - 3 of 3
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    Improve Traffic Volume Estimates from MnDOT's Regional Traffic Management Center
    (Minnesota Department of Transportation, 2020-02) Kwon, Taek M.
    The Regional Transportation Management Center (RTMC) at the Minnesota Department of Transportation (MnDOT) deploys a large number of traffic detectors in the Twin Cities' freeway network and continuously collects traffic data. While RTMC mainly uses the data for traffic and incident management, the TFA (Traffic Forecasting and Analysis) office uses the same data for monitoring, forecasting, planning, and reporting of transportation applications. RTMC provides current and historical volume data generated from its freeway network, but it does not provide quality information on that data. The objective of this project was to develop a new tool that can quickly explore the quality of detector data. To allow exploration of data quality, 13 detector-health parameters were computed using raw volume and occupancy data and then they were stored in a relational database. The final detector-health system was implemented as a client server-based system, in that a single server served many remote clients through the Internet. This report provides descriptions of the detector-health parameters, principles applied, server implementation, client software, and some analyses and application examples.
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    Intelligent Pavement for Traffic Flow Detection – Phase I
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2012-09) Yu, Xun
    This project explored a new approach in detecting vehicles on a roadway by making a roadway section itself a traffic flow detector. Sections of a given roadway are paved with carbon-nanotube (CNT)/cement composites; the piezoresitive property of carbon nanotubes enables the composite to detect the traffic flow. Meanwhile, CNTs can also work as the reinforcement elements to improve the strength and toughness of the concrete pavement. In contrast to current traffic flow detection technologies that require separate devices to be installed either in the pavement or over the road, the proposed sensing approach enables the pavement itself to detect traffic flow parameters. Therefore, the proposed sensor is expected to have a long service life with little maintenance and wide-area detection capability.
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    Intelligent Pavement for Traffic Flow Detection – Phase II
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2012-09) Yu, Xun
    This project is the extension of a Northland Advanced Transportation System Research Laboratory (NATSRL) FY09 project, titled as “Intelligent Pavement for Traffic Flow Detection”, which aims to explore a new approach in detecting vehicles on a roadway by making a roadway section as a traffic flow detector. Sections of a given roadway are paved with carbon-nanotube (CNT) enhanced pavement; the piezoresitive property of carbon nanotubes enables the pavement to detect the traffic flow. Meanwhile, CNTs can also work as reinforcement elements to improve the strength and toughness of the concrete pavement. The proposed sensor is expected to have a long service life with little maintenance and wide-area detection capability. In the FY09 project, lab tests demonstrated that CNT based cement composite can detect the mechanical stress levels for both static and dynamic loads. In the FY10 project, the research was extended to cement mortar, which has much higher mechanical strength and more useful in real applications. The effects of water level and CNT doping levels on the piezoresistivity of the composites were also studied. Preliminary road tests were performed for the evaluation of this new traffic sensor.