TransFloor: Transparent Floor Localization for Crowdsourcing Instant Delivery

Smart on-demand delivery services require accurate indoor localization to enhance the system-human synergy experience of couriers in complex multi-story malls and platform construction. Floor localization is an essential part of indoor positioning, which can provide floor/altitude data support for upper-level 3D indoor navigation services (e.g., delivery route planning) to improve delivery efficiency and optimize order dispatching strategies. We argue that due to label dependence and device dependence, the existing floor localization methods cannot be flexibly deployed on a large scale in numerous multi-story malls across the country, nor can they apply to all couriers/users on the platform. This paper proposes a novel self-evolving and user-transparent floor localization system named TransFloor, based on crowdsourcing delivery data (e.g., order status and sensors data) without additional label investment and specialized equipment constraints. TransFloor consists of an unsupervised barometer-based module--IOD-TKPD and an NLP-inspired Wi-Fi-based module--Wifi2Vec, and Self-Labeling is a perfect bridge between both to completely achieve label-free and device-independent floor positioning. In addition, TransFloor is designed as a lightweight plugin embedded into the platform without refactoring the existing architecture, and it has been deployed nationwide to adaptively launch real-time accurate 3D/floor positioning services for numerous crowdsourcing couriers. We evaluate TransFloor on real-world records from an instant delivery platform (involving 672,282 orders, 7,390 couriers, and 6,206 merchants in 388 malls during two months). It can achieve an average accuracy of 94.61% and demonstrate good robustness to device heterogeneity and adaptive durability, outperforming existing state-of-the-art methods. Crucially, it can effectively improve user satisfaction and reduce overdue delivery by providing accurate floor navigation information in complex multi-story malls. As a case study, the platform reduces erroneous order scheduling by 60% and overdue delivery by 2.7%, and increases delivery efficiency by reducing courier arrival time by 12.27 seconds accounting for 7.29%. We believe that the key ideas of TransFloor can be extended to other crowdsourcing scenarios for the public further.