Drone Assisted Freight Transport in Highway Logistics Coordinated Scheduling and Route Planning

This study aims to improve the drone-highway collaborative transport model to enhance efficiency and reduce operational costs. A multi-objective optimization model was developed using Particle Swarm Optimization (PSO) combined with the Simulated Annealing (SA) algorithm. Based on empirical data from the Shanghai-Nanjing Expressway (G42), the effectiveness of the drone-assisted transport model was analyzed across different scenarios. The results show that drone collaboration effectively alleviates peak-hour congestion, enhances long-distance transport efficiency and performs particularly well in tasks ranging from 100 to 400 km. The findings indicate that during peak hours (8:00–10:00, 18:00–20:00), the drone-assisted model maintains transport efficiency above $82 \%$. For long-distance transport, travel time is reduced by $14 \%-18 \%$, while perunit operating costs decrease by $9.5 \%-18 \%$. Additionally, the study finds that improving drone scheduling requires balancing transport demand and resource allocation. The most significant time savings occur when scheduling falls between 5 and 15 times, with diminishing returns beyond 15. Further analysis highlights that well-developed intelligent transport infrastructure plays a key role in enhancing drone delivery performance. In particular, a well-planned layout of takeoff and landing sites with V2X communication support can improve scheduling accuracy and reduce transport uncertainties. This study provides theoretical support for integrating drones into highway logistics and offers recommendations for improving drone endurance, refining smart scheduling algorithms, strengthening regulatory frameworks and advancing infrastructure development to foster the growth of low-altitude economy in smart logistics.