Mix Zones: User Privacy in Location-aware Services
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
Characterizing mobility and network usage in a corporate wireless local-area network
Proceedings of the 1st international conference on Mobile systems, applications and services
Cellular Census: Explorations in Urban Data Collection
IEEE Pervasive Computing
A prsimonious model of mobile partitioned networks with clustering
COMSNETS'09 Proceedings of the First international conference on COMmunication Systems And NETworks
Planet-scale human mobility measurement
Proceedings of the 2nd ACM International Workshop on Hot Topics in Planet-scale Measurement
Fast track article: From encounters to plausible mobility
Pervasive and Mobile Computing
Evaluating the privacy risk of location-based services
FC'11 Proceedings of the 15th international conference on Financial Cryptography and Data Security
Extrapolating sparse large-scale GPS traces for contact evaluation
Proceedings of the 5th ACM workshop on HotPlanet
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We analyze urban mobility by relying on the short-term mobility traces gathered from a publicly available web-based repository of GPS tracks - the Nokia Sports Tracker service. All mobility traces are obtained from a set of kml files. We show how the data collected voluntarily by individuals, equipped with GPS-enabled mobile phones, can be used to infer accurate, large-scale footprint of urban mobility. This method, unlike others - for example, personal interviewing, is more scalable and less time consuming. It exploits the fact that the on-line masses are willing to share their experience with others. We present a set of heuristics used to filter out bogus tracks from the dataset. We show that the mobility patterns, inferred from the remaining, credible, short-term mobility traces have macroscopic characteristics similar to the characteristics of mobility patterns retrieved from the long-term mobility traces, gathered in different urban environments. The results of our analysis lead to a proposal for creating city-specific mobility profiles. We discuss how such profiles could help improve location privacy and help develop new context-aware applications and services for mobile users.