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June 17, 2026DronesOpen Access

Traffic-Predictive Drone Scheduling: Day-Ahead Synchronization of Mobile Depots and Parallel Aerial Sorties in Urban Airspace

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Authors

SHShabahat HasanTSTarek SheltamiAMAshraf Mahmoud

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Overview

Randomized trial demonstrates improved fleet efficiency in UAV logistics through predictive traffic integration.

Key Points

  • The aim is to enhance fleet utilization and mission performance in urban UAV logistics by integrating traffic predictions.
  • Developed a multi-UAV dispatch framework using XGBoost for traffic prediction.
  • Implemented Particle Swarm Optimization and Mixed-Integer Linear Programming for scheduling.
  • Conducted computational experiments across 30 synthetic routing instances.
  • Increased mean fleet utilization from 0.43 to 0.63, a 46.2% relative improvement.
  • Reduced total mission completion time by 69.87% compared to the truck-only baseline.
  • Achieved a 29.58% incremental efficiency gain over static speed drone deployments.

Cite This Study

Hasan et al. (2026) studied this question.

synapsesocial.com/papers/6a323e9ed50b63ecad207c64https://doi.org/10.3390/drones10060461
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