Browsing by Subject "Loop detectors"

Now showing 1 - 4 of 4
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    Arterial Link Travel Time Estimation Using Loop Detector Data - Phase I
    (Center for Transportation Studies, University of Minnesota, 1997-05) Zhang, Michael; Wu, Tong Qiang; Kwon, Eil; Sommers, Kevin; Habib, Ahsan
    In recognition of the need for an effective yet inexpensive way of estimating arterial travel times, MnDOT has sponsored a research project to develop a travel time estimation model using loop detector data. This project is being jointly conducted by researchers from the University of Iowa and the University of Minnesota, and will be carried out in two phases. Phase I involves a literature review, traffic data collection and development of a travel time database, and Phase II covers model development, calibration and evaluation. This report summarizes the findings of Phase I.
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    Improving Freeway Traffic Speed Estimation Using High-Resolution Loop Detector Data
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2013-04) Liu, Henry; Sun, Jie
    In this project, we developed an innovative methodology to solve a long-standing traffic engineering problem, i.e. measuring traffic speed using data from single inductive loop detectors. Traditionally, traffic speeds are estimated using aggregated detector data with a manually calibrated effective vehicle length. The calibration effort (usually through running probe vehicles), however, is time consuming and costly. Instead of using aggregated data, in this project, our data collection system records every vehicle-detector actuation "event" so that for each vehicle we can identify the time gap and the detector occupation time. With such high-resolution "event-based" data, we devised a method to differentiate regular cars with longer vehicles. The proposed method is based on the observation that longer vehicles will have longer detector occupation time. Therefore, we can identify longer vehicles by detecting the changes of occupation time in a vehicle platoon. The "event-based" detector data can be obtained through the implementation of the SMART-Signal (Systematic Monitoring of Arterial Road Traffic Signals) system, which was developed by the principal investigator and his students in the last five years. The method is tested using the data from Trunk Highway 55, which is a high-speed arterial corridor controlled by coordinated traffic signals. The result shows that the proposed method can correctly identify most of the vehicles passing by inductive loop detectors. The identification of long vehicles will improve the estimation of effective vehicle length on roads. Consequently, speed estimation from the inductive loop detector is improved.
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    Investigating inductive loop signature technology for statewide vehicle classification counts
    (Minnesota Department of Transportation, 2018-10) Liao, Chen-Fu
    An inductive loop signature technology was previously developed by a US Department of Transportation (DOT) Small Business Innovation Research (SBIR) program to classify vehicles along a section of the roadway using existing inductive loop detectors installed under the pavement. It was tested and demonstrated in California that the loop signature system could obtain more accurate, reliable and comprehensive traffic performance measures for transportation agencies. Results from the studies in California indicated that inductive loop signature technology was able to re-identify and classify vehicles along a section of roadway and provide reliable performance measures for assessing progress, at the local, State, or national level. This study aimed to take advantage of the outcomes from the loop signature development to validate the performance with ground truth vehicle classification data in the Twin Cities Metropolitan Area (TCMA). Based on the results from individual vehicle class verification, class 2 vehicles had the highest match rate of 90%. Possible causes of classification accuracy for other vehicle classes may include types of loops, sensitivity of inductive loops that generates a shadow loop signal on neighboring lanes, and classification library that was built based on California data. To further understand the causes of loop signature performance and improve the classification accuracy, the author suggests performing additional data verification at a permanent Automatic Traffic Recorder (ATR) site. There is also an opportunity to investigate the classification algorithm and develop an enhanced pattern recognition methodology based on the raw loop signature profile of various types of vehicles in Minnesota.
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    Refining Inductive Loop Signature Technology for Statewide Vehicle Classification Counts
    (Minnesota Department of Transportation, 2021-12) Liao, Chen-Fu
    Transportation agencies in the U.S. use devices such as loop detectors, automatic traffic recorders (ATR), or weigh-in-motion (WIM) sensors to monitor the performance of traffic network for planning, forecasting, and traffic operations. With a limited number of ATR and WIM sensors deployed throughout the state roadways, temporary double tubes are often deployed to get axle-based vehicle classification counts. An inductive loop signature technology previously developed by a Small Business Innovation Research (SBIR) program sponsored by the US Department of Transportation is used to classify vehicles using existing loops. This technology has the potential to save time and money while providing the state, counties or cities more data especially in the metro area where loop detectors have already been installed. This research leveraged the outcomes from previous development to validate the classification accuracy with video data. A loop signature system was initially installed at a traffic station in Jordan, MN, to evaluate its performance. The system was later moved to another location on US-52 near Coates, MN, to validate its classification accuracy with more heavy- vehicle traffic. Individual vehicle records were manually verified and validated with ground-truth video data using both the 13 and 7-bin classification schemes from the Federal Highway Administration (FHWA) and the Highway Performance Monitoring System (HPMS). The combined results from both test sites indicated that the loop signature technology had an overall classification accuracy of 93% and 96% using the FHWA and HPMS schemes, respectively. The classification performance can be further improved by including additional vehicle signatures from the state to the classification library.