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Related Experiment Video

Updated: May 21, 2026

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

Shared drone route scheduling optimization.

Chao Hong1, Yi Yan1, Zhigang Lian1

  • 1School of Electronic and Information Engineering, Shanghai DianJi University, Shanghai, China.

Plos One
|May 19, 2026
PubMed
Summary
This summary is machine-generated.

Shared passenger-carrying unmanned aerial vehicle (UAV) systems enhance urban air mobility by optimizing routes in multi-airport settings. A novel self-learning Ant-Lion Optimizer (SLALO) reduces navigation time by 27.3% and boosts UAV utilization.

Related Experiment Videos

Last Updated: May 21, 2026

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

Area of Science:

  • Operations Research
  • Transportation Science
  • Artificial Intelligence

Background:

  • Urban air mobility (UAM) faces scheduling challenges in multi-airport environments due to complex passenger demands and operational constraints.
  • Balancing efficiency and service quality is crucial for shared passenger-carrying unmanned aerial vehicle (UAV) systems.

Purpose of the Study:

  • To develop a shared UAV route scheduling optimization model for multi-airport, multi-station systems.
  • To address challenges in real-time scheduling, heterogeneous demands, and model compatibility.

Main Methods:

  • A novel optimization model integrating passenger order characteristics and UAV operational parameters.
  • Implementation of a quadratic soft time window mechanism for flexible arrival management.
  • Development of a self-learning Ant-Lion Optimizer (SLALO) with natural number encoding for real-time route assignment.

Main Results:

  • Achieved a 27.3% reduction in total system navigation time compared to non-pooling baselines.
  • Demonstrated an average UAV utilization rate of 78.6%.
  • SLALO exhibited superior convergence speed and solution quality compared to GA, PSO, and ALO.

Conclusions:

  • The proposed SLALO-based framework significantly enhances the efficiency and utilization of shared UAV systems for urban air mobility.
  • The model offers a viable solution for complex scheduling problems, improving operational costs and passenger satisfaction.
  • This intelligent shared mobility system has potential applications in regions with limited infrastructure, fostering economic integration.