{\sf TriDeliver}: Cooperative Air-Ground Instant Delivery with UAVs, Couriers, and Crowdsourced Ground Vehicles

2026-04-10

AI Summary

This study addresses the challenge of surging on-demand delivery demands, which cannot be efficiently met by any single delivery modality—such as couriers, drones, or crowdsourced vehicles—in isolation. To tackle this issue, the authors propose a hierarchical collaborative delivery framework that establishes, for the first time, a tripartite coordination system integrating couriers, drones, and crowdsourced vehicles. Central to this framework is a transfer learning–based knowledge transfer mechanism that leverages historical courier behavior to enhance unmanned systems’ decision-making, enabling efficient multi-agent task scheduling. By combining neural network fine-tuning with real-world trajectory data analysis, the proposed approach significantly outperforms state-of-the-art methods on real datasets, reducing delivery costs by 65.8%, shortening delivery time by 17.7%, and decreasing interference with original tasks of crowdsourced vehicles by 43.6%.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Coordination and Collaboration
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Instant delivery, shipping items before critical deadlines, is essential in daily life. While multiple delivery agents, such as couriers, Unmanned Aerial Vehicles (UAVs), and crowdsourced agents, have been widely employed, each of them faces inherent limitations (e.g., low efficiency/labor shortages, flight control, and dynamic capabilities, respectively), preventing them from meeting the surging demands alone. This paper proposes {\sf TriDeliver}, the first hierarchical cooperative framework, integrating human couriers, UAVs, and crowdsourced ground vehicles (GVs) for efficient instant delivery. To obtain the initial scheduling knowledge for GVs and UAVs as well as improve the cooperative delivery performance, we design a Transfer Learning (TL)-based algorithm to extract delivery knowledge from couriers'behavioral history and transfer their knowledge to UAVs and GVs with fine-tunings, which is then used to dispatch parcels for efficient delivery. Evaluated on one-month real-world trajectory and delivery datasets, it has been demonstrated that 1) by integrating couriers, UAVs, and crowdsourced GVs, {\sf TriDeliver} reduces the delivery cost by $65.8\%$ versus state-of-the-art cooperative delivery by UAVs and couriers; 2) {\sf TriDeliver} achieves further improvements in terms of delivery time ($-17.7\%$), delivery cost ($-9.8\%$), and impacts on original tasks of crowdsourced GVs ($-43.6\%$), even with the representation of the transferred knowledge by simple neural networks, respectively.

Problem

• instant delivery
• UAVs
• crowdsourced ground vehicles
• cooperative delivery
• delivery efficiency

Innovation

• TriDeliver
• cooperative air-ground delivery
• transfer learning
• crowdsourced ground vehicles
• instant delivery