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Research Reports |
| Title: | Remote Sensing of Multimodal Transportation Assets Using Drones |
| Authors: | Raj Bridgelall, Taraneh Askarzadeh, and Denver Tolliver |
| University: | North Dakota State University |
| Publication Date: | Jul 2024 |
| Report #: | MPC-24-513 |
| Project #: | MPC-665 |
| TRID #: | 01929005 |
| Keywords: | asset management, condition surveys, drones, highway maintenance, inspection, railroads, remote sensing, structural health monitoring, vehicle design |
| Type: | Research Report – MPC Publications |
This comprehensive report synthesizes findings from three distinct yet interrelated studies, each exploring the developing role of drones in the condition monitoring of multimodal transportation assets. The first study, a systematic literature review (SLR) on railway inspection and monitoring (RIM), analyzes 47 articles from a corpus of 7,900 publications spanning 2014-2022. The study identifies cost reduction, safety enhancement, timesaving, and reliability as key motivators for drone adoption in RIM, categorizing applications into defect identification, situation assessment, infrastructure asset monitoring, and others. The second SLR focuses on drone usage in road condition monitoring (D-RCM), surveying 60 articles from 619 publications within the same timeframe. The study reveals similar drivers and categorizes applications into condition monitoring, situation assessment, and construction inspection, while also highlighting challenges such as payload limitations and visual line-of-sight maintenance. The third study introduces a propulsion efficiency index (PEX) for evaluating the performance of drone designs to carry heavier payloads. It establishes range, payload ratio, and aspect ratio as the minimum set of independent parameters for PEX computation, finding that these parameters account for more than 90% of the PEX distribution in the current design landscape. Collectively, these studies offer a multi-faceted analysis of drone applications in transportation, providing critical insights into their technical, economic, and societal implications.
Bridgelall, Raj, Taraneh Askarzadeh, and Denver Tolliver. Remote Sensing of Multimodal Transportation Assets Using Drones, MPC-24-513. North Dakota State University - Upper Great Plains Transportation Institute, Fargo: Mountain-Plains Consortium, 2024.
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