United States Department of Transportation
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Analysis of Drone-based Last-mile Delivery Systems under Aerial Congestion: A Continuum Approximation Approach [supporting dataset]
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2023-09-07
[JSON-3.32 KB]
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Abstract:This paper presents a systematic analysis and design framework for a spectrum of last-mile delivery systems that leverage unmanned aerial vehicles (UAVs). Four distinct modes are considered: (1) direct drone deliveries from a fixed depot; (2) drone deliveries from parked trucks that carry bulk parcels to customer neighborhoods; (3) drone deliveries from nonstopping trucks that tour customer neighborhoods; and (4) as a benchmark, traditional truck-based home deliveries. We present a new continuum-approximation approach that is used for analysis of both truck routing and aerial-UAV traffic. We compared the operational cost and efficiency of different delivery schemes to reveal how a certain scheme is the most efficient under various scenarios. We demonstrate the applicability of our model on expansive real-world roadway networks and further conduct analysis on grid networks, yielding key analytical insights. The drone-based delivery is demonstrated to be superior to conventional truck-only delivery, suggesting a significant potential for socioeconomic benefit. It is observed that when servicing a relatively low demand over a small area, dispatching drones directly from the distribution facility is the most efficient method. As the demand grows or spans a wider area, collaborative strategies are preferred, as they better cope with certain aspects, such as the long line-haul cost or the ramping aerial congestion, by striking a balance between efficiency and flexibility.
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Content Notes:National Transportation Library (NTL) Curation Note: As this dataset is preserved in a repository outside U.S. DOT control, as allowed by the U.S. DOT’s Public Access Plan (https://doi.org/10.21949/1503647) Section 7.4.2 Data, the NTL staff has performed NO additional curation actions on this dataset. The current level of dataset documentation is the responsibility of the dataset creator. NTL staff last accessed this dataset at its repository URL on 2024-01-03. If, in the future, you have trouble accessing this dataset at the host repository, please email NTLDataCurator@dot.gov describing your problem. NTL staff will do its best to assist you at that time.
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