Nationwide Behavior-Aware Coordinates Mining From Uncertain Delivery Events

Zhiqing Hong, Guang Wang, Wenjun Lyu, Baoshen Guo, Yi Ding, Haotian Wang, Shuai Wang, Yunhuai Liu, Desheng Zhang

Abstract: Geocoding, associating textual addresses with corresponding GPS coordinates, is vital for many location-based services (e.g., logistics, ridesharing, and social networks). One of the most common Geocoding solutions is using commercial map services such as Google Maps. However, this is typically not practical for some location-based service providers due to real-world challenges like commercial competition and high costs (recurring fees). In this paper, we design a new cost-effective Geocoding framework to automatically infer the geographic coordinates from textual addresses. To achieve this, we take the E-Commerce logistics service as a concrete scenario and design CoMiner , an unsupervised coordinate inference framework based on textual address data, delivery event data, and courier trajectory data. CoMiner includes three main components, (1) A POI-level clustering model, (2) A Delivery Mobility Graph ( DMG ), and (3) A behavior-driven address ranking model. Furthermore, we design CoMiner-W , a coordinates mining algorithm based on WiFi data, to further enhance the effectiveness of CoMiner . We conduct extensive experiments on three large-scale datasets where CoMiner outperforms the state-of-the-art methods by 20.3%. Moreover, we have designed an abnormal delivery event detection system based on CoMiner and deployed it at JD Logistics, which brings a significant reduction in abnormal delivery event rates.