面向快递员揽收到达时间预测的多任务深度时空网络

快递员揽件到达时间预测，即预测用户下单后快递员的上门揽收时间，一直都是物流企业所关心的重要问题。准确的揽件到达时间预测可以优化揽件效率，提升用户体验。该问题主要存在以下挑战：(1)快递员揽件到达时间受到多种复杂时空因素的影响，包括待预测订单自身的时空特征，以及与其他待揽收订单之间的相互影响；(2)快递员在执行揽件任务期间，会不断接收到系统分配的新订单，造成揽收路线的动态变化，从而给揽件到达时间预测带来了更大的不确定性。针对以上挑战，提出了一种面向揽件到达时间预测的多任务深度时空网络MSTN4PAT模型，从海量的揽件历史数据中学习快递员揽件到达时间的复杂时空模式。MSTN4PAT充分挖掘待预测订单始发地与目的地之间的内在关联，使用多任务学习来建模订单之间的相互影响，并从特征宽度和特征深度2个角度高效融合各种特征，实现准确的揽件到达时间预测。在真实的揽件数据集上的实验结果表明，MSTN4PAT的预测效果明显优于对比模型。

Multi-task deep spatial-temporal networkfor couriers pick-up arrival time prediction

Predicting a couriers pick-up arrival time, i.e., estimating the arrival time of the courier after a user places a package pick-up order, is a fundamental task in logistics platforms. Accurate pick-up arrival time prediction can optimize logistics efficiency and improve user experience. The problem faces the following challenges: 1) The couriers pick-up arrival time is affected by a variety of complex spatiotemporal factors, including the spatiotemporal characteristics of the target order, as well as the correlations between the target order and other unpicked-up orders. 2) During the execution of the pick-up task, new orders will be continuously assigned to the courier by the system, resulting in changes in the package pick-up route, which brings great difficulties to the arrival time prediction. In response to the above challenges, a multi-task deep spatial-temporal network (named MSTN4PAT) is proposed to accurately predict package pick-up arrival time, which learns the complex spatiotemporal patterns of couriers pick-up arrival time from massive historical data. MSTN4PAT fully exploits the intrinsic relationship between the origin and destination of the target order, uses multi-task learning to model the interaction between orders, and efficiently integrates various features from the perspectives of feature width and feature depth to achieve accurate arrival time predictions. The experimental results on two real-world datasets show that MSTN4PAT significantly outperforms other comparative medels.