An Analysis of Human Mobility Using Real Traces

We present a new analysis for human mobility through real traces captured in a recreational environment. The goal of this analysis is to investigate the motion components, in both a qualitative and quantitative way, and thus, to get a better knowledge of pedestrian mobility behavior. Moreover, some results based upon human mobility captured in a real scenario with GPS equipment are presented. In these captured data, we verified that the speed and acceleration components follow a Normal distribution, while the direction angle variation component and the pause time measure follow a Lognormal distribution. Finally, we show that velocity and direction angle change components of captured scenario have a temporal dependence unlike the random mobility models.

[1]  Christian Bettstetter,et al.  Mobility modeling in wireless networks: categorization, smooth movement, and border effects , 2001, MOCO.

[2]  Ray Jain,et al.  The art of computer systems performance analysis - techniques for experimental design, measurement, simulation, and modeling , 1991, Wiley professional computing.

[3]  Mingyan Liu,et al.  Random waypoint considered harmful , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[4]  Injong Rhee,et al.  Human Mobility Patterns and Their Impact on Delay Tolerant Networks , 2007, HotNets.

[5]  ChaintreauAugustin,et al.  Impact of Human Mobility on Opportunistic Forwarding Algorithms , 2007 .

[6]  Tristan Henderson,et al.  CRAWDAD: A Community Resource for Archiving Wireless Data at Dartmouth , 2005, IEEE Pervasive Comput..

[7]  Marcelo Dias de Amorim,et al.  Otiy: Loactors tracking nodes , 2007, ArXiv.

[8]  Tracy Camp,et al.  MANET simulation studies: the incredibles , 2005, MOCO.

[9]  Kevin C. Almeroth,et al.  Towards realistic mobility models for mobile ad hoc networks , 2003, MobiCom '03.

[10]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[11]  Mingyan Liu,et al.  Building realistic mobility models from coarse-grained traces , 2006, MobiSys '06.

[12]  Christophe Diot,et al.  Impact of Human Mobility on Opportunistic Forwarding Algorithms , 2007, IEEE Transactions on Mobile Computing.

[13]  Martin May,et al.  Analyzing the impact of mobility in ad hoc networks , 2006, REALMAN '06.

[14]  CampTracy,et al.  MANET simulation studies , 2005 .

[15]  Carlos Alberto V. Campos,et al.  A Markovian Model Representation of Individual Mobility Scenarios in Ad Hoc Networks and Its Evaluation , 2007, EURASIP J. Wirel. Commun. Netw..

[16]  Mingyan Liu,et al.  A general framework to construct stationary mobility models for the simulation of mobile networks , 2006, IEEE Transactions on Mobile Computing.

[17]  David Kotz,et al.  Extracting a Mobility Model from Real User Traces , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[18]  Injong Rhee,et al.  On the levy-walk nature of human mobility , 2011, TNET.

[19]  Ravi Jain,et al.  Model T++: an empirical joint space-time registration model , 2006, MobiHoc '06.

[20]  Ahmed Helmy,et al.  The IMPORTANT framework for analyzing the Impact of Mobility on Performance Of RouTing protocols for Adhoc NeTworks , 2003, Ad Hoc Networks.

[21]  Jean-Yves Le Boudec,et al.  Perfect simulation and stationarity of a class of mobility models , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[22]  Tracy Camp,et al.  A survey of mobility models for ad hoc network research , 2002, Wirel. Commun. Mob. Comput..

[23]  Tristan Henderson,et al.  CRAWDAD: a community resource for archiving wireless data at Dartmouth , 2005, CCRV.

[24]  Marcelo Dias de Amorim,et al.  Otiy: locators tracking nodes , 2007, CoNEXT '07.