Browsing by Subject "Detection and identification systems"

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    Enriched Sensor Data for Enhanced Bridge Weigh-in-Motion (eBWIM) Applications
    (Center for Transportation Studies, 2018-11) Kumar, Ravi; Schultz, Arturo; Hourdos, John
    Bridge weigh-in-motion (BWIM) systems, which measure bridge deformation under live loading to estimate weights of passing vehicles, have been in development since Moses first introduced the concept in 1979. Despite advances made since its introduction, important limitations for BWIM systems still exist. A feasibility study was performed to determine if some of the limitations—including poor accuracy with multiple vehicle passage, either in tandem or side-by-side; and inability to accurately capture the passage of a vehicle moving at variable speeds—could be overcome by enriching the dataset available to the BWIM system. Non-contact measurements collected in real time on the topside of the bridge can enrich the dataset, and by taking advantage of these measurements a more accurate and effective enriched bridge weigh-in-motion (eBWIM) system can be developed. Several sensing technologies were reviewed including fiber Bragg gratings, MEMS accelerometers, microwave radar sensors, magnetic sensors, active infrared detectors, and video image vehicle detection systems. Preliminary results indicated that there was no clear candidate for a fully mature sensing system that would satisfy all the criteria in this study. However, microwave radar sensors have a reasonably low cost, are the least intrusive, and perform better in all weather conditions compared to the other sensors. A testbed using radar sensors is proposed to investigate the accuracy of the eBWIM system. If the desired accuracy of the eBWIM system can be achieved, its implementations should prove to be invaluable for enforcing bridge weight limits, studying truck traffic patterns, and managing bridge inventories.
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    A Positioning and Mapping Methodology Using Bluetooth and Smartphone Technologies to Support Situation Awareness and Wayfinding for the Visually Impaired
    (Center for Transportation Studies, University of Minnesota, 2018-11) Liao, Chen-Fu
    People with vision impairment often face challenges while traveling in an unfamiliar environment largely due to uncertainty and insufficient accessible information. To improve mobility, accessibility, and the level of confidence the visually impaired experience in using the transportation system, it is important to remove information barriers that could potentially impede their mobility. A "condition aware" infrastructure using Bluetooth low-energy (BLE) technology was developed to provide up-to-date and correct audible information to users at the right location. A Multivariable Regression (MR) algorithm using the Singular Value Decomposition (SVD) technique was introduced to model the relationship between Bluetooth Received Signal Strength (RSS) and the actual ranging distance in an outdoor environment. This methodology reduced the environmental uncertainty and dynamic nature of RSS measurements in a Bluetooth network. The range output from the MR-SVD model was integrated with an extended Kalman filter to provide positioning and mapping solutions. Using 6 BLE beacons at an intersection in St. Paul, Minnesota, our approach achieved an average position accuracy of 2.5 m and 3.8 m in X and Y directions, respectively. A few statistical techniques were implemented and were able to successfully detect whether the location of one or multiple BLE beacons in a network changed based on Bluetooth RSS indications. With the self-monitoring network, information associated with each Bluetooth beacon can be provided to the visually impaired at the right location to support their wayfinding in a transportation network.