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 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, TriDeliver reduces the delivery cost by $65.8\%$ versus state-of-the-art cooperative delivery by UAVs and couriers; 2) 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.

翻译：即时配送（即在截止时间前送达物品）在日常生活中至关重要。尽管快递员、无人机（UAV）以及众包代理等多种配送主体已被广泛采用，但每种方式均存在固有局限性（例如，效率低下/劳动力短缺、飞行控制能力不足、动态能力受限），使其难以单独满足日益增长的配送需求。本文提出TriDeliver——首个层次化协同框架，该框架整合了人类快递员、无人机与众包地面车辆（GV）以实现高效的即时配送。为获取地面车辆与无人机的初始调度知识并提升协同配送性能，我们设计了一种基于迁移学习（TL）的算法，从快递员的行为历史中提取配送知识，并通过微调将知识迁移至无人机与地面车辆，进而用于包裹分派以实现高效配送。基于一个月真实轨迹与配送数据集的评估结果表明：1）通过整合快递员、无人机与众包地面车辆，TriDeliver相较于现有最优的无人机与快递员协同配送方案，配送成本降低了65.8%；2）即使仅通过简单神经网络表征迁移知识，TriDeliver在配送时间（-17.7%）、配送成本（-9.8%）以及对众包地面车辆原始任务的影响（-43.6%）方面均实现了进一步改进。