Multi-sensor Data-driven Route Prediction in Instant Delivery with a 3-Conversion Network

Abstract

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.