Para-Pred: Addressing Heterogeneity for City-Wide Indoor Status Estimation in On-Demand Delivery

On-demand delivery is a new form of logistics where customers place orders through online platforms and the platform arranges couriers to deliver them within a short time. The acquisition of indoor status (i.e., arrival or departure at the merchants) of couriers plays an important role in order dispatching and route planning. The Bluetooth Low Energy (BLE) device is a promising solution for city-wide indoor status estimation due to the low hardware and deployment costs and low power consumption. However, the environment and smartphone model heterogeneities affect the status characteristics contained in the Bluetooth signal, resulting in the decline of status estimation performance. The previous methods to alleviate the heterogeneity are not suitable for city-wide scenarios with thousands of merchants and hundreds of smartphone models. In this paper, we propose Para-Pred, an indoor status estimation framework based on the graph neural network, which directly Predicts the effective indoor status estimation model Parameters for unseen scenarios. Our key idea is to utilize similarity between the influence patterns of heterogeneities on the Bluetooth signal to directly infer unseen scenarios' influence patterns. We evaluate the Para-Pred on 109,378 couriers with 672 smartphone models in 12,109 merchants from an on-demand delivery company. The evaluation results show that across environment and smartphone model heterogeneities, the accuracy and recall of our method achieve 93.62% and 95.20%, outperforming state-of-the-art solutions.