Mobile Positioning and Trajectory Reconstruction Based on Mobile Phone Network Data: A Tentative Using Particle Filter

Abstract

Mobile positioning is a key element in many geolocation applications and research fields about human mobility patterns, location-based services, targeted marketing, urban mobility, public health, and transport planning. The commonly used data for understanding the large scale mobility patterns are mobile network data or Call Detail Records (CDRs). However, CDR data has two major drawbacks: temporal and spatial uncertainties and sparseness. Although the first problem is widely covered by trajectory reconstruction techniques, the second problem still remains challenging. Hence, in this paper, we propose the adaptation of a new method based on a particle filter algorithm for mobile positioning and trajectory reconstruction. Our goal is to evaluate if this nonlinear method can out-perform the existent linear methods like Switching Kalman Filter. Therefore, the model performance and the effects of the parameters on accuracy were evaluated in controlled experimental settings. Additionally, the experiments were performed on a real dataset and compared with the results achieved by a linear approach.

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