Trajectory inference for mobile devices using connected cell towers

Kushani Perera, Tanusri Bhattacharya, Lars Kulik, James Bailey

Abstract: Trajectory inference from raw location samples of a mobile device is an important task for many location based services, such as crowd sourced traffic monitoring, fleet management and personalized trip planning. This task becomes challenging when location samples are obtained only from the connected cell towers (GSM localization), instead of using other localization sensors such as GPS or Wi-Fi. Cell tower based localization consumes negligible energy compared to GPS or Wi-Fi and has high availability. However, it can have large inaccuracy, making the task of cellular trajectory mapping extremely challenging. In previous studies, cellular trajectory inference has been performed assuming the availability of knowledge of the cellular network or the signal strengths of the neighbouring cell towers. However, for a mobile application running on a user's device, this information may be hard to obtain and it may also require additional storage and computation costs. In this paper, we propose a novel cellular trajectory inference method which requires only the user's connected cell tower location, time and speed information. Exploiting the preciseness of the time dimension, we accurately compute the distance a user has travelled within a cell and use it to infer the straight line segments and turning points of a trajectory. We show that using the distance information of three consecutive cells, exact inference of the line segment is possible. Our method achieves high accuracy for trajectory inference in urban areas with high cell density and straight line road segments. It does not require any historical trajectory information or pre-training and incurs low storage and computation costs.