In parcel delivery, the "last mile" from the parcel hub to the customer is costly, especially for time-sensitive delivery tasks that have to be completed within hours after arrival. Recently, crowdshipping has attracted increased attention as a new alternative to traditional delivery modes. In crowdshipping, private citizens ("the crowd") perform short detours in their daily lives to contribute to parcel delivery in exchange for small incentives. However, achieving desirable crowd behavior is challenging as the crowd is highly dynamic and consists of autonomous, self-interested individuals. Leveraging crowdshipping for time-sensitive deliveries remains an open challenge. In this paper, we present an agent-based approach to on-time parcel delivery with crowds. Our system performs data stream processing on the couriers' smartphone sensor data to predict delivery delays. Whenever a delay is predicted, the system attempts to forge an agreement for transferring the parcel from the current deliverer to a more promising courier nearby. Our experiments show that through accurate delay predictions and purposeful task transfers many delays can be prevented that would occur without our approach.

翻译：在包裹配送中，从分拣中心到客户的"最后一公里"成本高昂，尤其针对需在抵达后数小时内完成的时效性配送任务。近年来，众包配送作为传统配送模式的新兴替代方案备受关注。在该模式下，普通市民（"大众"）通过日常生活路线中的短途绕行参与包裹配送，并获得小额激励作为回报。然而，由于大众行为具有高度动态性且由自主谋利的个体组成，实现理想的群体行为极具挑战性。如何利用众包配送完成时效性任务仍是一个未解决的难题。本文提出了一种基于智能体的众包准时包裹配送方法。该系统通过处理快递员智能手机传感器数据的实时流，预测配送延误。每当预测到延误时，系统会尝试促成当前配送员与附近更优快递员之间的包裹转运协议。实验表明，通过精准的延误预测和有目的的任务转运，本方法可有效规避传统方案中不可避免的诸多延误。