| dc.contributor.author | Bridgelall, Raj | |
| dc.description.abstract | Electric and autonomous aircraft (EAA) are set to disrupt current cargo-shipping models.
To maximize the benefits of this technology, investors and logistics managers need information on
target commodities, service location establishment, and the distribution of origin–destination pairs
within EAA’s range limitations. This research introduces a three-phase data-mining and geographic
information system (GIS) algorithm to support data-driven decision-making under uncertainty. Ana-
lysts can modify and expand this workflow to scrutinize origin–destination commodity flow datasets
representing various locations. The algorithm identifies four commodity categories contributing to
more than one-third of the value transported by aircraft across the contiguous United States, yet only
5% of the weight. The workflow highlights 8 out of 129 regional locations that moved more than 20%
of the weight of those four commodity categories. A distance band of 400 miles among these eight
locations accounts for more than 80% of the transported weight. This study addresses a literature
gap, identifying opportunities for supply chain redesign using EAA. The presented methodology can
guide planners and investors in identifying prime target markets for emerging EAA technologies
using regional datasets. | en_US |
| dc.rights | In copyright. Permission to make this version available has been granted by the author and publisher. | |
| dc.title | Data-Driven Deployment of Cargo Drones: A U.S. Case Study Identifying Key Markets and Routes | 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 | 2023-08-09T21:45:05Z | |
| dc.date.available | 2023-08-09T21:45:05Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | https://hdl.handle.net/10365/33223 | |
| dc.subject | Electric and autonomous aircraft | en_US |
| dc.subject | cargo shipping disruption | en_US |
| dc.subject | data-mining techniques | en_US |
| dc.subject | sustainable supply chain | en_US |
| dc.subject | supply chain redesign | en_US |
| dc.identifier.orcid | 0000-0003-3743-6652 | |
| dc.identifier.citation | Bridgelall, R. "Data-Driven Deployment of Cargo Drones: A U.S. Case Study Identifying Key Markets and Routes." Algorithms, DOI:10.3390/a16080373, 16(8):373. | en_US |
| dc.description.sponsorship | This research was funded by the United States’ Department of Transportation, grant name
Mountain Plains Consortium. | 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/a16080373 | |
