Understanding mobility based on GPS data

By Yu Zheng, Quannan Li, Yukun Chen, Xing Xie, Wei-Ying Ma. Ubiquitous Computing, 2008

1,238 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ccfef606b58af279018d5f40bb38f623422c1536

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Yu Zheng
2. Brian Y. Lim
3. Licia Capra
4. Anind K. Dey
5. Xing Xie
6. Kevin P. Heaslip
7. Sina Dabiri
8. Philip S. Yu

In Transportation

1. Yu Zheng
2. Xing Xie
3. Ouri Wolfson
4. Jing Yuan
5. Licia Capra
6. Wei-Ying Ma
7. Hai Yang
8. Guangzhong Sun

In Signal Processing

1. Yu Zheng
2. Xing Xie
3. Ouri Wolfson
4. Jing Yuan
5. Licia Capra
6. Guangzhong Sun
7. Wei-Ying Ma
8. Guoliang Li

Citations in the corpus

• Representation learning for geospatial areas using large-scale mobility data from smart card (2016, background): “Introduction As the use of location-aware personal devices such as smartphones rapidly spreads, a large amount of mobility data, including GPS or cell tower logs, has been automatically accumulated [17, 8].”
• Toolkit to support intelligibility in context-aware applications (2010, background): “Decision trees are popular in applications to recognize: identity / ability ( e.g. , [8]), interruptibility ( e.g. , [2, 42]), mobility ( e.g. , [45]), etc .”
• Accelerometer-based transportation mode detection on smartphones (2013, methodology): “[36, 37] use as part of their GPSbased transportation mode detection approach.”
• Using mobile phones to determine transportation modes (2010, neutral): “…323 Hours 85% [Mun et al. 2008] Still, Walk, Motorized GSM, WiFi 2 13 Hours 83% [Liao et al. 2007] Walk, Motorized (Bus/Car) GPS, GIS 1 60 Days 84% [Zheng et al. 2008] Walk, Bike, Motorized GPS 65 10 Months 76% [Miluzzo et al. 2008] Still, Walk, Run Accelerometer 8 4 Hours 78% Our work expands…”
• 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, neutral): “2011 [24, 23, 25].”
• Understanding Human Mobility from Twitter (2014, background): “Recently effort has focused on the study of human mobility using new tracking technologies such as mobile phones [1, 24], GPS [26, 2], Wifi [4], and RFID devices [3].”
• GeoLife: A Collaborative Social Networking Service among User, Location and Trajectory (2010, background): “Refer to papers [8, 11, 15] for details.”
• Urban Computing: Concepts, Methodologies, and Applications (2014, methodology): “Y. Zheng, Y. Chen, X. Xie, and W. Y. Ma. 2009a. GeoLife2.”
• Trajectory Data Mining (2015, methodology): “The dataset can be used to evaluate trajectory classification and activity recognition [Zheng et al. 2008a, 2008b].”
• Energy-efficient rate-adaptive GPS-based positioning for smartphones (2010, methodology): “[28] use supervised learning to infer motion modes (e.”
• Next place prediction using mobility Markov chains (2012, methodology): “The Geolife dataset [15] has been gathered by researchers from Microsoft Asia and consists of mobility traces collected from April 2007 to October 2011 using GPS-enabled devices, mostly in the area of Shanghai.”
• An Empirical Study of Geographic User Activity Patterns in Foursquare (2011, neutral): “ence with previous studies (Zheng et al. 2008), where user location was periodically sampled through GPS.”
• Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment (2015, background): “Another type of data is the device-level metric obtained from mobile devices, such as device and application usage[8, 21], network access bandwidth[10], energy computation[11], personal GPS locations[28], etc.”
• Mining interesting locations and travel sequences from GPS trajectories (2009, background): “3 Application Scenarios The work reported in this paper is an important component of our project GeoLife [12][19][20][21], whose prototype has been internally accessible within Microsoft since Oct.”
• Inferring transportation modes from GPS trajectories using a convolutional neural network (2018, methodology): “It is worth noticing that the highest accuracy of both deep-learning-based studies (Endo et al., 2016; Hao et al., 2017) are still lower than the work of Zheng et al. (2008b), which is purely based on manual features.”