A conceptual view on trajectories
Data & Knowledge Engineering
A clustering-based approach for discovering interesting places in trajectories
Proceedings of the 2008 ACM symposium on Applied computing
Learning transportation mode from raw gps data for geographic applications on the web
Proceedings of the 17th international conference on World Wide Web
Understanding mobility based on GPS data
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
From trajectories to activities: a spatio-temporal join approach
Proceedings of the 2009 International Workshop on Location Based Social Networks
Activity identification from GPS trajectories using spatial temporal POIs' attractiveness
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks
Discovering regions of different functions in a city using human mobility and POIs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Semantic trajectories modeling and analysis
ACM Computing Surveys (CSUR)
A Gravity Model for Speed Estimation over Road Network
MDM '13 Proceedings of the 2013 IEEE 14th International Conference on Mobile Data Management - Volume 02
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The collection of huge amount of tracking data made possible by the widespread use of GPS devices, enabled the analysis of such data for several applications domains, ranging from traffic management to advertisement and social studies. However, the raw positioning data, as it is detected by GPS devices, lacks of semantic information since this data does not natively provide any additional contextual information like the places that people visited or the activities performed. Traditionally, this information is collected by hand filled questionnaire where a limited number of users are asked to annotate their tracks with the activities they have done. With the purpose of getting large amount of semantically rich trajectories, we propose an algorithm for automatically annotating raw trajectories with the activities performed by the users. To do this, we analyse the stops points trying to infer the Point Of Interest (POI) the user has visited. Based on the category of the POI and a probability measure based on the gravity law, we infer the activity performed. We experimented and evaluated the method in a real case study of car trajectories, manually annotated by users with their activities. Experimental results are encouraging and will drive our future works.