On-Demand Mobility Cargo Demand Estimation in Northern California Region

Rimjha, Mihir; Tarafdar, Sayantan; Hinze, Nicolas; Trani, Antonio; Swingle, Howard; Smith, J.C.; Marien, Ty V.; Dollyhigh, Sam

doi:10.1109/icns50378.2020.9223015

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…Therefore, once certified, shippers will be confident that pilotless drones are safer than other modes of surface transportation because of the reduced conflict with traffic in the vast three-dimensional airspace, the impossibility of human errors, and advancements in air traffic management [12]. Subsequently, shippers can encourage a policy shift towards cargo drones by demonstrating safe operations in a few focused regions that maximize their safety, environmental, and economic impacts [32].…”

Section: Advanced Air Mobilitymentioning

confidence: 99%

Reducing Risks by Transporting Dangerous Cargo in Drones

Bridgelall

2022

Sustainability

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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.

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Section: Advanced Air Mobilitymentioning

confidence: 99%