NDLI: Trajectory inference for mobile devices using connected cell towers

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Trajectory inference for mobile devices using connected cell towers

Content Provider	ACM Digital Library
Author	Bailey, James Bhattacharya, Tanusri Perera, Kushani Kulik, Lars
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.
Starting Page	1
Ending Page	10
Page Count	10
File Format	PDF
ISBN	9781450339674
DOI	10.1145/2820783.2820804
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-11-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Partial spatial knowledge Cell towers Cellular trajectory inference Mobile computing
Content Type	Text
Resource Type	Article

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