Upper Great Plains Transportation Institute (UGPTI)

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The Upper Great Plains Transportation Institute (UGPTI) is a research, education, and outreach center at North Dakota State University which is guided, in part, by an advisory council composed of representatives of various organizations, industries, and agencies affecting or affected by transportation. The UGPTI website may be found at https://www.ugpti.org/

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Browsing Upper Great Plains Transportation Institute (UGPTI) by browse.metadata.department "Physics"

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    Hyperspectral Applications in the Global Transportation Infrastructure
    (2015) Bridgelall, Raj; Rafert, J. Bruce; Tolliver, Denver D.; Upper Great Plains Transportation Institute
    Hyperspectral remote sensing is an emerging field with potential applications in the observation, management, and maintenance of the global transportation infrastructure. This study introduces a general analytical framework to link transportation systems analysis and hyperspectral analysis. The authors introduce a range of applications that would benefit from the capabilities of hyperspectral remote sensing. They selected three critical but unrelated applications and identified both the spatial and spectral information of their key operational characteristics to demonstrate the hyperspectral utility. The specific scenario studies exemplifies the general approach of utilizing the outputs of hyperspectral analysis to improve models that practitioners currently use to analyze a variety of transportation problems including roadway congestion forecasting, railway condition monitoring, and pipeline risk management.
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    Hyperspectral Range Imaging for Transportation Systems Evaluation
    (2016) Bridgelall, Raj; Rafert, J. Bruce; Tolliver, Denver D.; Atwood, Don; Upper Great Plains Transportation Institute
    Transportation agencies expend significant resources to inspect critical infrastructure such as roadways, railways, and pipelines. Regular inspections identify important defects and generate data to forecast maintenance needs. However, cost and practical limitations prevent the scaling of current inspection methods beyond relatively small portions of the network. Consequently, existing approaches fail to discover many high-risk defect formations. Remote sensing techniques offer the potential for more rapid and extensive non-destructive evaluations of the multimodal transportation infrastructure. However, optical occlusions and limitations in the spatial resolution of typical airborne and spaceborne platforms limit their applicability. This research proposes hyperspectral image classification to isolate transportation infrastructure targets for high-resolution photogrammetric analysis. A plenoptic swarm of unmanned aircraft systems will capture images with centimeter-scale spatial resolution, large swaths, and polarization diversity. The light field solution will incorporate structure-from-motion techniques to reconstruct three-dimensional details of the isolated targets from sequences of two-dimensional images. A comparative analysis of existing low-power wireless communications standards suggests an application dependent tradeoff in selecting the best-suited link to coordinate swarming operations. This study further produced a taxonomy of specific roadway and railway defects, distress symptoms, and other anomalies that the proposed plenoptic swarm sensing system would identify and characterize to estimate risk levels.