Route Reconstruction Using Low-Quality Bluetooth Readings

Abstract

Route reconstruction targets at recovering the actual routes of objects moving on an underlying road network from their timestamped position measurements. This fundamental pre-processing step to many location-based applications has been extensively studied for GPS data, which are object-centric and relatively densely sampled data. In this paper, we investigate the problem of route reconstruction using data collected from roadside Bluetooth scanners. In many cities, Bluetooth scanners are installed in road networks for monitoring the movement of Bluetooth-enabled devices. To address new challenges caused by such reader-centric Bluetooth data including spatial and temporal distortion, a new route reconstruction framework is proposed to transform Bluetooth readings through a family of distortion suppression strategies such that the transformed data can work well with the Hidden Markov model (HMM) map-matching approach. Extensive experiments are conducted to evaluate different transformation strategies with realworld datasets. The experimental results show that when the algorithm uses the baseline or the proposed transformation strategies, the map matching F1 score can be increased by up to 10% depending on the severity of distortion. CCS CONCEPTS • Information systems → Location based services.