On the Levy-Walk Nature of Human Mobility

By Injong Rhee, Minsu Shin, Seongik Hong, Kyunghan Lee, Song Chong. IEEE INFOCOM 2008 - The 27th Conference on Computer Communications, 2008

1,381 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Transportation

View Paper: https://www.semanticscholar.org/paper/7e782eb57c19622553bcfd9ac9e37d0d66274839

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Citations in the corpus

Understanding movements of organisms: it's time to abandon the Lévy foraging hypothesis (2015, background): “…et al. 1999; Yoda et al. 2012), thus eliminating situations where the distribution of step lengths peaks at some value distinctly greater than zero and generally restricting attention to the ‘tail’ of the distribution (Rhee et al. 2011; Lopez-Lopez et al. 2013; Schultheiss & Cheng 2013).”
Achieving k-anonymity in privacy-aware location-based services (2014, methodology): “The Levy walk model [32], which has been shown to better describe the mobility patterns of human being [33], is used to generate synthetic user contact events.”
Characterizing the human mobility pattern in a large street network. (2008, background): “…2006), and mobile phones (Gonzalez, Hidalgo and Barabási 2008), as well as the more accurate GPS data capturing trajectories of human movement (Rhee et al. 2008), all the studies without an exception adopt a Euclidean space, assuming human mobility is not constrained by the underlying street…”
Exploring Millions of Footprints in Location Sharing Services (2011, positive): “A Lévy Flight is characterized by a mixture of short, random movements with occasional long jumps.”
BonnMotion: a mobility scenario generation and analysis tool (2010, background): “22 Truncated Lévy Walk (“TLW”) Creates scenarios according to [27].”
Understanding intra-urban trip patterns from taxi trajectory data (2012, methodology): “Human mobility has become a hot research topic recently, since the wide use of location-aware devices such as GPS (global positioning system) receivers and mobile phones offers great convenience for collecting large volumes of individual trajectory data (González et al. 2008; Jiang et al. 2009; Rhee”
Human mobility: Models and applications (2017, methodology): “Indeed, a common problem arising when calculating the MSD, is that the location of an individual’s origin is often ambiguous [58].”
SLAW: A New Mobility Model for Human Walks (2009, methodology): “We apply Delaunay triangulation on the waypoints we extracted from real traces [35].”
Revealing daily travel patterns and city structure with taxi trip data (2013, background): “…(Chowell et al. 2003), big data are more accurate, objective, plentiful and highly accessible, providing opportunities to better describe and understand human mobility (Lu and Liu 2012, Brockmann, Hufnagel and Geisel 2006, Rhee et al. 2011, Gonzalez, Hidalgo and Barabasi 2008, Song et al. 2010).”
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].”
Mobile Data Offloading: How Much Can WiFi Deliver? (2013, methodology): “To find the best fit, we performed two well-known test methods: Cramer–Smirnov–Von–Mises (CSVM) statistical hypothesis test [12], [33] and Akaike test [28], where the tested distributions are exponential, log-normal, Weibull, and truncated Pareto distributions.”
L\'evy walks (2014, methodology): “Reproduced from Rhee et al. (2011). and searching for wild honey, or collecting berries and plant foods.”
Exploring Universal Patterns in Human Home-Work Commuting from Mobile Phone Data (2013, methodology): “Researchers have made progress in this effort using a variety of data sources such as circulating bank notes [1], taxi trip records [2, 5051], Foursquare check-in data [3], Tweets [52], and even GPS devices [4-6].”
Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-In Data (2013, background): “Different studies suggest that P(Dd) can be fitted by different statistical distributions such as power law P(Dd),Dd [2,10], exponential law P(Dd),exp(2kDd) [30,31], or exponentially truncated power law P(Dd),exp(2kDd)Dd [7,9].”
Serendipity: enabling remote computing among intermittently connected mobile devices (2012, methodology): “Speciﬁcally, we use Levy Walk Model [28], Random WayPoint Model (RWP) [29], and Time-Variant Community Mobility Model (TVCM) [21].”