GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory

By Yu Zheng, Xing Xie, Wei-Ying Ma. IEEE Data Engineering Bulletin, 2010

1,349 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/27faf0300710a345ff878e3a09f3803e36a0a064

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Citations in the corpus

A Survey on Deep Learning for Human Mobility (2020, methodology): “[58] MoveSim 2020 GAN, self-attention CNN distances, rд , p (r, d ), DailyLoc, G-rank, I-rank [238]”
Human mobility: Models and applications (2017, background): “For a review of potential applications see [62].”
Driving with knowledge from the physical world (2011, methodology): “Here, we use two users’ 1-year GPS logs (released in GeoLife dataset [28][29]) to determine the ground truth of the exact road segments a driver traversed and corresponding travel times.”
Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends (2020, methodology): “[121], for example, use the Microsoft Geolife dataset [118] and query Baidu Maps to derive input features such as the distance of a trajectory to a subway or bus route.”
PathS: Enhancing Geographical Maps with Environmental Sensed Data (2015, unknown): “, brightness, noise, GPS position [15], accessibility, etc.”
Trajectory Data Mining (2015, methodology): “The noise ﬁltering method, which has been used in T-Drive [Yuan et al. 2010a, 2011a, 2013a] and GeoLife [Zheng et al. 2009a; Zheng et al. 2010] projects, ﬁrst calculates the travel speed of each point in a trajectory based on the time interval and distance between a point and its successor (we call…”
Optimal Geo-Indistinguishable Mechanisms for Location Privacy (2014, methodology): “The GeoLife GPS Trajectories dataset contains 17621 traces from 182 users, moving mainly in the north-west of Beijing, China, in a period of over five years (from April 2007 to August 2012).”
Predicting future locations with hidden Markov models (2012, unknown): “2011 [24, 23, 25].”
Learning travel recommendations from user-generated GPS traces (2011, unknown): “…real-world location histories, some recommender systems, such as Geowhiz [Horozov et al. 2006], CityVoyager [Takeuchi and Sugimoto 2006], and GeoLife [Zheng et al. 2009a; Li et al. 2008; Zheng et al. 2010b], have been designed to recommend geographic locations like shops or restaurants to users.”
Protecting Locations with Differential Privacy under Temporal Correlations (2014, methodology): “Geolife data [40] was collected from 182 users in a period of over three years.”
TrajPrism: A Multi-Task Benchmark for Language-Grounded Urban Trajectory Understanding (2026, unknown): “Traditional trajectory benchmarks such as Porto 2 , San Francisco 3 , and Beijing [35, 34] focus on geometric tasks like similarity search, travel-time estimation, and classification.”