A Map-Based Dead-Reckoning Protocol for Updating Location Information
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
ST-DBSCAN: An algorithm for clustering spatial-temporal data
Data & Knowledge Engineering
A model for enriching trajectories with semantic geographical information
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Extending the LBS-framework TraX: Efficient proximity detection with dead reckoning
Computer Communications
A clustering-based approach for discovering interesting places in trajectories
Proceedings of the 2008 ACM symposium on Applied computing
Mining user similarity based on location history
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
A framework for evaluating the computational aspects of mobile phones
A framework for evaluating the computational aspects of mobile phones
A Location-Aware Framework for Intelligent Real-Time Mobile Applications
IEEE Pervasive Computing
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Past research in travel surveys has shown that a GPS mobile phone-based survey is a useful tool for collecting information about individuals. While a passive travel survey collection is preferred to an active travel survey method, passive collection remains a challenge due to a lack of high accuracy algorithms to automatically identify trip starts and trip ends. This paper presents Automatic Spatial Temporal Identification of Points of Interest (ASTIPI), an unsupervised spatial temporal algorithm to identify POIs. ASTIPI utilizes the temporal and spatial properties of the dataset to obtain a high accuracy of POI identification, even on a reduced GPS dataset that uses techniques to conserve battery life on mobile devices. While reducing outliers within POIs, ASTIPI also has a linear running time and maintains the temporal orders of the location data so that arrival and departure information can be easily extracted and thus, users' trips can be quickly identified. Using real data from mobile devices, evaluations of ASTIPI and other existing algorithms are performed, showing that ASTIPI obtains the highest accuracy of POI identification with an average accuracy of 88% when performing on full datasets generated using the GPS Auto-Sleep module and an average accuracy of 59% when performing on reduced datasets generated using both the GPS Auto-Sleep module and the Critical Points algorithm.