| dc.contributor.author | Bridgelall, Raj | |
| dc.description.abstract | The transportation of dangerous goods by truck or railway multiplies the risk of harm to people and the environment when accidents occur. Many manufacturers are developing autonomous drones that can fly heavy cargo and safely integrate into the national air space. Those developments present an opportunity to not only diminish risk but also to decrease cost and ground traffic congestion by moving certain types of dangerous cargo by air. This work identified a minimal set of metropolitan areas where initial cargo drone deployments would be the most impactful in demonstrating the safety, efficiency, and environmental benefits of this technology. The contribution is a new hybrid data mining workflow that combines unsupervised machine learning (UML) and geospatial information system (GIS) techniques to inform managerial or investment decision making. The data mining and UML techniques transformed comprehensive origin–destination records of more than 40 commodity category movements to identify a minimal set of metropolitan statistical areas (MSAs) with the greatest demand for transporting dangerous goods. The GIS part of the workflow determined the geodesic distances between and within all pairwise combinations of MSAs in the continental United States. The case study of applying the workflow to a commodity category of dangerous goods revealed that cargo drone deployments in only nine MSAs in four U.S. states can transport 38% of those commodities within 400 miles. The analysis concludes that future cargo drone technology has the potential to replace the equivalent of 4.7 million North American semitrailer trucks that currently move dangerous cargo through populated communities. | en_US |
| dc.rights | In copyright. Permission to make this version available has been granted by the author and publisher. | |
| dc.title | Reducing Risks by Transporting Dangerous Cargo in Drones | en_US |
| dc.type | Article | en_US |
| dc.description | Raj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM). | en_US |
| dc.date.accessioned | 2023-05-23T16:38:13Z | |
| dc.date.available | 2023-05-23T16:38:13Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | https://hdl.handle.net/10365/33154 | |
| dc.subject | air logistics | en_US |
| dc.subject | autonomous aircraft | en_US |
| dc.subject | data mining | en_US |
| dc.subject | electrified aircraft | en_US |
| dc.subject | sustainable transport | en_US |
| dc.subject | transport safety | en_US |
| dc.identifier.orcid | 0000-0003-3743-6652 | |
| dc.identifier.citation | Bridgelall, Raj. “Reducing Risks by Transporting Dangerous Cargo in Drones.” Sustainability, 14(20), 13044, October 2022. DOI:10.3390/su142013044. | en_US |
| dc.description.sponsorship | The author conducted this work with support from North Dakota State University and the Mountain-Plains Consortium, a University Transportation Center funded by the U.S. Department of Transportation. Grant Number: 69A3551747108. | en_US |
| dc.description.uri | https://www.ugpti.org/about/staff/viewbio.php?id=79 | |
| dc.language.iso | en_US | en_US |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
| dc.contributor.organization | Upper Great Plains Transportation Institute | |
| ndsu.college | College of Business | |
| ndsu.department | Transportation and Logistics | |
| dc.identifier.doi | https://doi.org/10.3390/su142013044 | |
