In this work, we perform the first study on couriers’ reporting behaviors and indoor mobility for instant delivery services. We found most of their indoor/outdoor reporting behaviors are consistent, which motivates us to use machine learning models to correct their indoor reporting behaviors by the outdoor reporting model. Further, we design a symbolic mobility graph, which captures their indoor mobility without prior knowledge of the indoor environment. Based on the experiment results, our system can improve the localization accuracy by 64% and 72% compared with the Wi-Fi GPS-based method and competitive accuracy with the fingerprinting-based method. With a case study, our system helps the delivery platform reduce the courier redundant time by 24%;
Reliability of two indoor positioning system (IPS) frameworks is studied in this paper. The experimental results show that TPT (Triangulation Positioning Technology) will make an inevitable influence to the system reliability due to the number restriction of the positioning antenna;