Ranges of human mobility in Los Angeles and New York
By Sibren Isaacman, Richard A. Becker, Ramón Cáceres, Stephen G. Kobourov, Margaret Martonosi, James R. Rowland, Alex Varshavsky. 2011
82 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Computer Science Applications, Transportation
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Ramón Cáceres
- Tian He
- Rebecca N. Wright
- Margaret Martonosi
- Walter Willinger
- Xiaoming Fu
- Pengyu Zhang
- Yong Li
In Transportation
- Ramón Cáceres
- Margaret Martonosi
- Sibren Isaacman
- Tian He
- Fengli Xu
- Pengyu Zhang
- Alex Varshavsky
- Yong Li
Citations in the corpus
- coMobile: real-time human mobility modeling at urban scale using multi-view learning (2015, background): “, modeling how residents move around the cities [9]; estimating cellphone users’ travel range [10]; predicting where cellphone users will travel next [5].”
- Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data (2017, background): “than 90% of nighttime mobility records are created in the same location. Therefore, arXiv:1702.06270v2 [cs.CY] 25 Feb 2017 Mobile Operator Time Duration Location Data Source Information Type AT&T [7, 8] 2009 2-4 months NY and LA, USA voice call and message anonymized individual mobility informat”
- DP-WHERE: Differentially private modeling of human mobility (2013, methodology): “Daily range, or the maximum distance between any two points a person visits in a day, has proven useful for characterizing human mobility patterns [17, 18, 21].”
- Human mobility modeling at metropolitan scales (2012, background): “In other previous work [18, 17], we also characterized the daily range of travel of those same populations.”