Automatic identification of points of interest in global navigation satellite system data: a spatial temporal approach

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.