Route prediction in instant delivery is still challenging due to the unique characteristics compared with conventional delivery services, such as strict deadlines, overlapped delivery time of multiple orders, and diverse individual preferences on delivery routes. Recently, development in the mobile Internet of Things (IoT) offers the opportunity to collect multi-sensor data with rich real-time information. Therefore, this study proposes a route prediction model called Roupid, which leverages multi-sensor data to improve the accuracy of route prediction in instant delivery. Specifically, we design a 3-Conversion Network-based route prediction framework to take full advantage of various information provided by multi-sensor data, including the encounter data sensed by Bluetooth low energy (BLE) beacons, active site data reported by smart handheld devices, and trajectory data detected by GPS. The 3-Conversion Network we propose is based on a deep neural network framework, which integrates an improved relational graph attention network with edge features (RGATE) to encode global information that couriers typically consider when planning routes. We evaluate our Roupid with real-world data collected from one of the largest instant delivery companies in the world, i.e., Eleme. Experimental results show that our Roupid outperforms other state-of-the-art baselines and offers up to 85.51% of the route prediction precision.

与传统配送服务相比，由于其独特的特点，例如严格的期限、多个订单的配送时间重叠以及配送路线的个人偏好不同等，即时配送的路线预测仍然具有挑战性。近年来，移动物联网（IoT）的发展为收集具有丰富实时信息的多传感器数据提供了机会。因此，本研究提出了一种名为Roupid的路线预测模型，利用多传感器数据来提高即时配送中路线预测的准确性。具体来说，我们设计了一个基于3-Conversion Network的路线预测框架，以充分利用多传感器数据提供的各种信息，包括蓝牙低功耗（BLE）信标感知的遭遇数据、智能手持设备报告的活动站点数据，以及GPS检测到的轨迹数据。我们提出的 3-Conversion Network 基于深度神经网络框架，它将改进的关系图注意力网络与边缘特征（RGATE）集成在一起，以编码快递员在规划路线时通常考虑的全局信息。我们使用从世界上最大的即时配送公司之一（即饿了么）收集的真实数据来评估我们的 Roupid。实验结果表明，我们的 Roupid 优于其他最先进的基线，并提供高达 85.51% 的路线预测精度。