survey

Data-driven Human Mobility Modeling: A Survey and Engineering Guidance for Mobile Networking

Authors:

Karin Anna Hummel,

Wilfried N. Gansterer,

Günter HaringAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 48, Issue 3

Article No.: 38, Pages 1 - 39

https://doi.org/10.1145/2840722

Published: 22 December 2015 Publication History

Abstract

Over the last decades, modeling of user mobility has become increasingly important in mobile networking research and development. This has led to the adoption of modeling techniques from other disciplines such as kinetic theory or urban planning. Yet these techniques generate movement behavior that is often perceived as not “realistic” for humans or provides only a macroscopic view on mobility. More recent approaches infer mobility models from real traces provided by positioning technologies or by the marks the mobile users leave in the wireless network. However, there is no common framework for assessing and comparing mobility models.

In an attempt to provide a solid foundation for realistic mobility modeling in mobile networking research, we take an engineering approach and thoroughly discuss the required steps of model creation and validation. In this context, we survey how and to what extent existing mobility modeling approaches implement the proposed steps. This also summarizes helpful information for readers who do not want to develop a new model, but rather intend to choose among existing ones.

References

[1]

Osman Abul, Francesco Bonchi, and Mirco Nanni. 2008. Never walk alone: Uncertainty for anonymity in moving objects databases. In Proceedings of the IEEE International Conference on Data Engineering (ICDE). IEEE Computer Society, Washington, DC, 376--385.

Digital Library

[2]

Nils Aschenbruck, Raphael Ernst, Elmar Gerhards-Padilla, and Matthias Schwamborn. 2010. BonnMotion: A mobility scenario generation and analysis tool. In Proceedings of the International ICST Conference on Simulation Tools and Techniques (SIMUTools). Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, Brussels, Belgium, Article 51, 10 pages.

Digital Library

[3]

Nils Aschenbruck, Aarti Munjal, and Tracy Camp. 2011. Trace-based mobility modeling for multi-hop wireless networks. Computer Communications 34, 6 (2011), 704--714.

Digital Library

[4]

Dharmayashdev Rai Basgeet, Pascal Dugenie, Alistair Munro, Dritan Kaleshi, and J. Irvine. 2003. SMM: Mathematical framework of a scalable mobility model. In Proceedings of the 6th ACM International Workshop on Modeling Analysis and Simulation of Wireless and Mobile Systems (MSWIM). ACM, New York, NY, 74--81.

Digital Library

[5]

Michael Behrisch, Laura Bieker, Jakob Erdmann, and Daniel Krajzewicz. 2011. SUMO--Simulation of urban mobility. In Proceedings of the 3rd International Conference on Advances in System Simulation (SIMUL).

[6]

Christian Bettstetter, Giovanni Resta, and Paolo Santi. 2003. The node distribution of the random waypoint mobility model for wireless ad hoc networks. IEEE Transactions on Mobile Computing 2, 3 (2003), 257--269.

Digital Library

[7]

Vincent D. Blondel, Markus Esch, Connie Chan, Fabrice Clérot, Pierre Deville, Etienne Huens, Frédéric Morlot, Zbigniew Smoreda, and Cezary Ziemlicki. 2012. Data for development: The D4D challenge on mobile phone data. CoRR abs/1210.0137 (2012), 1--10. http://arxiv.org/abs/1210.0137.

[8]

Victor J. Blue and Jeffrey L. Adler. 2001. Cellular automata microsimulation for modeling bi-directional pedestrian walkways. Transportation Research Part B: Methodological 35, 3 (2001), 293--312.

[9]

Chiara Boldrini, Marco Conti, and Andrea Passarella. 2009. The sociable traveller: Human travelling patterns in social-based mobility. In Proceedings of the ACM International Symposium on Mobility Management and Wireless Access (MobiWAC). ACM, New York, NY, 34--41.

Digital Library

[10]

Josh Broch, David A. Maltz, David B. Johnson, Yih-Chun Hu, and Jorjeta Jetcheva. 1998. A performance comparison of multi-hop wireless Ad-hoc network routing protocols. In Proceedings of the 4th Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom). ACM, New York, NY, 85--97.

Digital Library

[11]

Francesco Calabrese, Massimo Colonna, Piero Lovisolo, Dario Parata, and Carlo Ratti. 2011. Real-time urban monitoring using cell phones: A case study in Rome. IEEE Transactions on Intelligent Transportation Systems 12, 1 (2011), 141--151.

Digital Library

[12]

Francesco Calabrese, Giusy Di Lorenzo, Liang Liu, and Carlo Ratti. 2011. Estimating origin-destination flows using mobile phone location data. Pervasive Computing 10, 4 (2011), 36--44.

Digital Library

[13]

Francesco Calabrese, Giusy Di Lorenzo, and Carlo Ratti. 2010. Human mobility prediction based on individual and collective geographical preferences. In Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems (ITSC). 312--317.

[14]

Tracy Camp, Jeff Boleng, and Vanessa Davies. 2002. A survey of mobility models for ad hoc network research. Wireless Communications and Mobile Computing 2, 5 (2002), 483--502.

[15]

Augustin Chaintreau, Pan Hui, Jon Crowcroft, Christophe Diot, Richard Gass, and James Scott. 2007. Impact of human mobility on opportunistic forwarding algorithms. IEEE Transactions on Mobile Computing 6 (2007), 606--620.

Digital Library

[16]

Yung-Chih Chen, J. Kurose, and D. Towsley. 2012. A mixed queueing network model of mobility in a campus wireless network. In Proceedings of the IEEE INFOCOM. IEEE, 2656--2660.

[17]

Zhuo Chen, Lu Wang, and Nelson H.C. Yung. 2011. Adaptive human motion analysis and prediction. Pattern Recognition 44, 12 (2011), 2902--2914.

Digital Library

[18]

Eunjoon Cho, Seth A. Myers, and Jure Leskovec. 2011. Friendship and mobility: User movement in location-based social networks. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). ACM, New York, NY, 1082--1090.

Digital Library

[19]

Marco Conti, Silvia Giordano, Martin May, and Andrea Passarella. 2010. From opportunistic networks to opportunistic computing. IEEE Communications Magazine 48, 9 (2010), 126--139.

Digital Library

[20]

Caitlin Cottrill, Francisco Pereira, Fang Zhao, Inas Dias, Hock Lim, Moshe Ben-Akiva, and P. Christopher Zegras. 2013. Future mobility survey: Experience in developing a smartphone-based travel survey in Singapore. Transportation Research Record: Journal of the Transportation Research Board 2354 (2013), 59--67.

[21]

Yves-Alexandre de Montjoye, Zbigniew Smoreda, Romain Trinquart, Cezary Ziemlicki, and Vincent D. Blondel. 2014. D4D-senegal: The second mobile phone data for development challenge. CoRR abs/1407.4885 (2014), 1--11.

[22]

Michael Solomon Desta, Esa Hyytiä, Jörg Ott, and Jussi Kangasharju. 2013. Characterizing content sharing properties for mobile users in open city squares. In Proceedings of the 10th Annual Conference on Wireless On-demand Network Systems and Services (WONS). IEEE, Banff, Alberta, Canada, 147--154.

[23]

Rex W. Douglass, David A. Meyer, Megha Ram, David Rideout, and Dongjin Song. 2015. High resolution population estimates from telecommunications data. EPJ Data Science 4, 1 (2015), 1--13.

[24]

Zhenguo Du, Peilin Hong, and Kaiping Xue. 2012. Hotspot gravitation mobility model. IEEE Communications Letters 16, 2 (2012), 193--195.

[25]

Matt Duckham and Lars Kulik. 2005. A formal model of obfuscation and negotiation for location privacy. In Proceedings of the 3rd International Conference on Pervasive Computing (PERVASIVE). Springer, 152--170.

Digital Library

[26]

Nathan Eagle and Alex Pentland. 2006. Reality mining: Sensing complex social systems. Personal Ubiquitous Comput. 10, 4 (2006), 255--268.

Digital Library

[27]

Frans Ekman, Ari Keränen, Jouni Karvo, and Jörg Ott. 2008. Working day movement model. In Proceedings of the 1st ACM SIGMOBILE Workshop on Mobility Models. ACM, New York, NY, 33--40.

Digital Library

[28]

Suzanne P. Evans. 1973. A relationship between the gravity model for trip distribution and the transportation problem in linear programming. Transportation Research 7, 1 (1973), 39--61.

[29]

Steven Farber, Tijs Neutens, Harvey J. Miller, and Xiao Li. 2013. The social interaction potential of metropolitan regions: A time-geographic measurement approach using joint accessibility. Annals of the Association of American Geographers 103, 3 (2013), 483--504.

[30]

Domenico Ferrari, Giuseppe Serazzi, and Alessandro Zeigner. 1983. Measurement and Tuning of Computer Systems. Prentice Hall, Englewood Cliffs, NJ.

[31]

Michal Ficek and Lukas Kencl. 2012. Inter-call mobility model: A spatio-temporal refinement of call data records using a gaussian mixture model. In Proceedings of the IEEE INFOCOM. IEEE, 469--477.

[32]

Marco Fiore, Jérôme Härri, Fethi Filali, and Christian Bonnet. 2007. Understanding vehicular mobility in network simulation. In Proceedings of the IEEE 4th International Conference on Mobile Adhoc and Sensor Systems (MASS). IEEE, Los Alamitos, CA, 1--6.

[33]

Morgan R. Frank, Lewis Mitchell, Peter Sheridan Dodds, and Christopher M. Danforth. 2013. Happiness and the patterns of life: A study of geolocated tweets. Scientific Reports 3 (Sept 2013), 2625. http://dx.doi.org/10.1038/srep02625.

[34]

Adriano Galati, Karim Djemame, and Chris Greenhalgh. 2013. A mobility model for shopping mall environments founded on real traces. Networking Science 2, 1--2 (2013), 1--11.

[35]

Olof Görnerup, Nima Dokoohaki, and Andrea Hess. 2015. Privacy-preserving mining of frequent routes in cellular network data. In Proceedings of the IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom). 581--587.

Digital Library

[36]

Matthias Grossglauser and D.N.C. Tse. 2002. Mobility increases the capacity of ad hoc wireless networks. IEEE/ACM Transactions on Networking 10, 4 (2002), 477--486.

Digital Library

[37]

Jérôme Härri, Fethi Filali, and Christian Bonnet. 2009. Mobility models for vehicular ad hoc networks: A survey and taxonomy. IEEE Communications Surveys Tutorials 11, 4 (2009), 19--41.

Digital Library

[38]

Dirk Helbing and Péter Molnár. 1995. Social force model for pedestrian dynamics. Phys. Rev. E 51, 5 (1995), 4282--4286.

[39]

David A. Hensher and Kenneth J. Button. 2008. Handbook of Transport Modelling. Elsevier.

[40]

John D. Herbert and Benjamin H. Stevens. 1960. A model for the distribution of residential activity in urban areas. Journal of Regional Science 2, 2 (1960), 21--36.

[41]

Andreas Horni, Kai Nagel, and Kay W. Axhausen. 2011. High-Resolution Destination Choice In Agent-Based Demand Models. Eidgenössische Technische Hochschule Zürich, IVT, Institut für Verkehrsplanung und Transportsysteme.

[42]

Wei-Jen Hsu, Thrasyvoulos Spyropoulos, Konstantinos Psounis, and Ahmed Helmy. 2009. Modeling spatial and temporal dependencies of user mobility in wireless mobile networks. IEEE/ACM Transactions on Networking 17, 5 (2009), 1564--1577.

Digital Library

[43]

Karin Anna Hummel and Andrea Hess. 2013. Movement activity estimation and forwarding effects for opportunistic networking based on urban mobility traces. Wireless Communications and Mobile Computing 13, 3 (2013), 343--360.

[44]

Esa Hyytiä, Pasi E. Lassila, and Jorma T. Virtamo. 2006. A Markovian waypoint mobility model with application to hotspot modeling. In Proceedings of the IEEE International Conference on Communications (ICC), Vol. 3. IEEE, 979--986.

[45]

Sibren Isaacman, Richard Becker, Ramón Cáceres, Margaret Martonosi, James Rowland, Alexander Varshavsky, and Walter Willinger. 2012. Human mobility modeling at metropolitan scales. In Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services (MobiSys). ACM, New York, NY, 239--252.

Digital Library

[46]

Ravi Jain, Dan Lelescu, and Mahadevan Balakrishnan. 2005. Model T: An empirical model for user registration patterns in a campus wireless LAN. In Proceedings of the 11th Annual International Conference on Mobile Computing and Networking (MobiCom). ACM, New York, NY, 170--184.

Digital Library

[47]

Andreas Janecek, Danilo Valerio, Karin Anna Hummel, Fabio Ricciato, and Helmut Hlavacs. 2012. Cellular data meet vehicular traffic theory: Location area updates and cell transitions for travel time estimation. In Proceedings of the ACM Conference on Ubiquitous Computing (UbiComp). ACM, New York, NY, 361--370.

Digital Library

[48]

Amit Jardosh, Elizabeth M. Belding-Royer, Kevin C. Almeroth, and Subhash Suri. 2003. Towards realistic mobility models for mobile ad hoc networks. In Proceedings of the 9th Annual International Conference on Mobile Computing and Networking (MobiCom). ACM, New York, NY, 217--229.

Digital Library

[49]

David B. Johnson and David A. Maltz. 1996. Dynamic source routing in Ad-hoc wireless networks. In Mobile Computing, T. Imielinski and H. Korth (Eds.). Kluwer Academic Publishers, Chapter 5, 153--181.

[50]

Raja Jurdak, Kun Zhao, Jiajun Liu, Maurice AbouJaoude, Mark Cameron, and David Newth. 2015. Understanding human mobility from Twitter. PLoS ONE 10, 7 (07 2015), e0131469.

[51]

Dmytro Karamshuk, Chiara Boldrini, Marco Conti, and Andrea Passarella. 2011. Human mobility models for opportunistic networks. IEEE Communications Magazine 49, 12 (2011), 157--165.

[52]

Dmytro Karamshuk, Chiara Boldrini, Marco Conti, and Andrea Passarella. 2012. An arrival-based framework for human mobility modeling. In Proceedings of the IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM). IEEE, 1--9.

[53]

Ari Keränen, Jörg Ott, and Teemu Kärkkäinen. 2009. The ONE simulator for DTN protocol evaluation. In Proceedings of the 2nd International Conference on Simulation Tools and Techniques (Simutools). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), Brussels, Belgium, 55:1--55:10.

Digital Library

[54]

Jonghyun Kim, Vinay Sridhara, and Stephan Bohacek. 2009. Realistic mobility simulation of urban mesh networks. Ad Hoc Netw. 7, 2 (2009), 411--430.

Digital Library

[55]

Minkyong Kim, David Kotz, and Songkuk Kim. 2006. Extracting a mobility model from real user traces. In Proceedings of the 25th IEEE International Conference on Computer Communications (INFOCOM). IEEE, 1--13.

[56]

Tetsuo Kobayashi, Harvey J. Miller, and Walied Othman. 2011. Analytical methods for error propagation in planar space-time prisms. Journal of Geographical Systems 13, 4 (2011), 327--354.

[57]

Sokol Kosta, Alessandro Mei, and Julinda Stefa. 2010. Small world in motion (SWIM): Modeling communities in Ad-Hoc mobile networking. In Proceedings of the IEEE Conference on Sensor Mesh and Ad Hoc Communications and Networks (SECON). IEEE, 1--9.

[58]

Thomas Kunz, Atif A. Siddiqi, and John Scourias. 2001. The peril of evaluating location management proposals through simulations. Wireless Networks 7, 6 (2001), 635--643.

Digital Library

[59]

Patrick Laube, Matt Duckham, and Thomas Wolle. 2008. Decentralized movement pattern detection amongst mobile geosensor nodes. In Proceedings of the 5th International Conference Geographic Information Science (GIScience). 199--216. http://dx.doi.org/10.1007/978-3-540-87473-7_13.

Digital Library

[60]

Kyunghan Lee, Seongik Hong, Seong Joon Kim, Injong Rhee, and Song Chong. 2009. SLAW: A new mobility model for human walks. In Proceedings of the 28th IEEE International Conference on Computer Communications (INFOCOM). IEEE, 855--863.

[61]

Kyunghan Lee, Seongik Hong, Seong Joon Kim, Injong Rhee, and Song Chong. 2012. SLAW: Self-similar least-action human walk. IEEE/ACM Trans. Netw. 20, 2 (April 2012), 515--529.

Digital Library

[62]

John G. Markoulidakis, George L. Lyberopoulos, Dimitrios F. Tsirkas, and Efstathios D. Sykas. 1997. Mobility modeling in third-generation mobile telecommunications systems. IEEE Personal Communications 4 (1997), 41--56.

[63]

Christoph P. Mayer and Oliver P. Waldhorst. 2011. On the impact of graph structure on mobility in opportunistic mobile networks. In Proceedings of the IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). IEEE, 882--887.

[64]

Randy L. Mayes. 2009. Developing adequacy criterion for model validation based on requirements. In Proceedings of the IMAC-XXVII: Conference & Exposition on Structural Dynamics.

[65]

Harald Meyer, Oscar Trullols-Cruces, Andrea Hess, Karin A. Hummel, José M. Barceló-Ordinas, Claudio E. Casetti, and Gunnar Karlsson. 2011. VANET mobility modeling challenged by feedback loops. In Proceedings of the 10th IFIP Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net). IEEE, 95--102.

[66]

Harvey J. Miller. 2008. Time geography. In Encyclopedia of GIS. Springer, 1151--1156.

[67]

Darakhshan J. Mir, Sibren Isaacman, Ramon Caceres, Margaret Martonosi, and Rebecca N. Wright. 2013. DP-WHERE: Differentially private modeling of human mobility. In Proceedings of the IEEE International Conference on Big Data. 580--588.

[68]

Frédéric Morlot, Salah-Eddine Elayoubi, and François Baccelli. 2010. An interaction-based mobility model for dynamic hot spot analysis. In Proceedings of the 29th IEEE International Conference on Computer Communications (INFOCOM). IEEE, 2294--2302.

Digital Library

[69]

Aarti Munjal, Tracy Camp, and William C. Navidi. 2011. SMOOTH: A simple way to model human mobility. In Proceedings of the 14th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM). ACM, New York, NY, 351--360.

Digital Library

[70]

Mirco Musolesi and Cecilia Mascolo. 2006. A community based mobility model for ad hoc network research. In Proceedings of the 2nd International Workshop on Multi-hop Ad Hoc Networks: From Theory to Reality (REALMAN). ACM, 31--38.

Digital Library

[71]

Mirco Musolesi and Cecilia Mascolo. 2009. Mobility models for systems evaluation. A survey. In Middleware for Network Eccentric and Mobile Applications, Benoit Garbinato, Hugo Miranda, and Luis Rodrigues (Eds.). Springer, 43--62.

[72]

Samuel C. Nelson, Albert F. Harris, III, and Robin Kravets. 2007. Event-driven, role-based mobility in disaster recovery networks. In Proceedings of the 2nd ACM Workshop on Challenged Networks (CHANTS). ACM, New York, NY, 27--34.

Digital Library

[73]

Tijs Neutens, Tim Schwanen, and Harvey J. Miller. 2010. Dealing with timing and synchronization in opportunities for joint activity participation. Geographical Analysis 42, 3 (2010), 245--266.

[74]

Calvin Newport, David Kotz, Yougu Yuan, Robert S. Gray, Jason Liu, and Chip Elliott. 2007. Experimental evaluation of wireless simulation assumptions. SIMULATION: Transactions of The Society for Modeling and Simulation International 83, 9 (2007), 643--661.

Digital Library

[75]

Injong Rhee, Minsu Shin, Seongik Hong, Kyunghan Lee, and Song Chong. 2008. On the levy-walk nature of human mobility. In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM). IEEE, 924--932.

[76]

Injong Rhee, Minsu Shin, Seongik Hong, Kyunghan Lee, Seongjoon Kim, and Song Chong. 2009. CRAWDAD data set ncsu/mobilitymodels (v. 2009-07-23). Retrieved from http://crawdad.org/ncsu/mobilitymodels/.

[77]

Michele Rossi, Leonardo Badia, Nicola Bui, and Michele Zorzi. 2005. On group mobility patterns and their exploitation to logically aggregate terminals in wireless networks. In Proceedings of the IEEE 62nd Vehicular Technology Conference (VTC-2005-Fall), Vol. 3. IEEE, 1863--1867.

[78]

Radhika Ranjan Roy. 2011. Handbook of Mobile Ad Hoc Networks for Mobility Models. Springer.

[79]

Paolo Santi. 2012. Mobility Models for Next Generation Wireless Networks: Ad Hoc, Vehicular and Mesh Networks. Wiley.

Digital Library

[80]

Matthias Schwamborn, Nils Aschenbruck, and Peter Martini. 2010. A realistic trace-based mobility model for first responder scenarios. In Proceedings of the 13th ACM International Conference on Modeling, Analysis, and Simulation of Wireless and Mobile Systems (MSWIM). ACM, New York, NY, 266--274.

Digital Library

[81]

Filippo Simini, Marta C. Gonzalez, Amos Maritan, and Albert-Laszlo Barabasi. 2012. A universal model for mobility and migration patterns. Nature 484 (2012), 96--100.

[82]

Catherine Soanes and Angus Stevenson (Eds.). 2005. The Oxford Dictionary of English (revised edition). Oxford University Press.

[83]

Chaoming Song, Tal Koren, Pu Wang, and Albert-Laszlo Barabasi. 2010. Modelling the scaling properties of human mobility. Nature Physics 6, 10 (2010), 818--823.

[84]

Vikram Srinivasan, Mehul Motani, and Wei Tsang Ooi. 2006. Analysis and implications of student contact patterns derived from campus schedules. In Proceedings of the 12th Annual International Conference on Mobile Computing and Networking (MobiCom). ACM, 86--97.

Digital Library

[85]

Herbert Stachowiak. 1973. Allgemeine Modelltheorie. Springer, Vienna -- New York.

[86]

Diane Tang and Mary Baker. 1999. Analysis of a metropolitan-area wireless network. In Proceedings of the ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom). ACM, New York, NY, 13--23.

Digital Library

[87]

Joanne Treurniet. 2014. A taxonomy and survey of microscopic mobility models from the mobile networking domain. ACM Comput. Surv. 47, 1, Article 14 (2014), 32 pages.

Digital Library

[88]

Cristian Tuduce and Thomas R. Gross. 2005. A mobility model based on WLAN traces and its validation. In Proceedings of the 24th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM), Vol. 1. IEEE, 664--674.

[89]

Wouter Van den Broeck, Corrado Gioannini, Bruno Gonçalves, Marco Quaggiotto, Vittoria Colizza, and Alessandro Vespignani. 2011. The GLEaMviz computational tool, a publicly available software to explore realistic epidemic spreading scenarios at the global scale. BMC Infectious Diseases 11, 37 (02/02/2011 2011).

[90]

Dirck van Vliet. 1978. Improved shortest path algorithms for transport networks. Transportation Research 12, 1 (1978), 7--20.

[91]

Ryan Vogt, Ioanis Nikolaidis, and Pawel Gburzynski. 2012. A realistic outdoor urban pedestrian mobility model. Simulation Modelling Practice and Theory 26 (2012), 113--134.

[92]

Vladimir Vukadinović, Fabian Dreier, and Stefan Mangold. 2014. Impact of human mobility on wireless Ad Hoc networking in entertainment parks. Ad Hoc Networks 12 (2014), 17--34.

Digital Library

[93]

Vladimir Vukadinović, Ólafur Ragnar Helgason, and Gunnar Karlsson. 2009. A mobility model for pedestrian content distribution. In Proceedings of the 2nd International Conference on Simulation Tools and Techniques (Simutools). 1--8.

Digital Library

[94]

Duncan J. Watts. 1999. Networks, dynamics, and the small-world phenomenon. American Journal of Sociology 105 (1999), 493--527.

[95]

Stephan Winter and Zhang-Cai Yin. 2010. Directed movements in probabilistic time geography. International Journal of Geographical Information Science 24, 9 (2010), 1349--1365.

Digital Library

[96]

Stephan Winter and Zhang-Cai Yin. 2011. The elements of probabilistic time geography. GeoInformatica 15, 3 (2011), 417--434.

Digital Library

[97]

Jihwang Yeo, David Kotz, and Tristan Henderson. 2006. CRAWDAD: A community resource for archiving wireless data at Dartmouth. SIGCOMM Computer Communication Review 36, 2 (2006), 21--22.

Digital Library

[98]

Jungkeun Yoon, Mingyan Liu, and Brain Noble. 2003. Random waypoint considered harmful. In Proceedings of the 22nd Annual Joint Conference of the IEEE Computer and Communications (INFOCOM), Vol. 2. IEEE, 1312--1321.

[99]

Jungkeun Yoon, Brian D. Noble, Mingyan Liu, and Minkyong Kim. 2006. Building realistic mobility models from coarse-grained traces. In Proceedings of the 4th International Conference on Mobile Systems, Applications and Services (MobiSys). ACM, New York, NY, 177--190.

Digital Library

[100]

Desheng Zhang, Jun Huang, Ye Li, Fan Zhang, Chengzhong Xu, and Tian He. 2014. Exploring human mobility with multi-source data at extremely large metropolitan scales. In Proceedings of the International Conference on Mobile Computing and Networking (MobiCom). ACM, New York, NY, 201--212.

Digital Library

[101]

Chen Zhao and M. L. Sichitiu. 2010. N-Body: Social based mobility model for wireless Ad Hoc network research. In Proceedings of the 7th Annual IEEE Communications Society Conference on Sensor Mesh and Ad Hoc Communications and Networks (SECON). 1--9.

[102]

Qunwei Zheng, Xiaoyan Hong, and Jun Liu. 2006. An agenda based mobility model. In Proceedings of the 39th Annual Symposium on Simulation. IEEE Computer Society, 188--195.

Digital Library

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 48, Issue 3

February 2016

619 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/2856149

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering/University of Florida/Gainesville

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Publication History

Published: 22 December 2015

Accepted: 01 September 2015

Revised: 01 August 2015

Received: 01 April 2015

Published in CSUR Volume 48, Issue 3

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EC FP7 Marie Curie program
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European Union Seventh Framework Programme (EU FP7)

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Wang HZhang QWu YJin DWang XZhu LYu L(2024)Synthesizing Human Trajectories Based on Variational Point ProcessesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.331220936:4(1785-1799)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1109/TKDE.2023.3312209
马华赵东王新王甲华蓓童剑(2023)A novel crowdsensing system architecture model and its implementation methodsSCIENTIA SINICA Informationis10.1360/SSI-2022-015753:7(1262)Online publication date: 30-Jun-2023
https://doi.org/10.1360/SSI-2022-0157
Kapp AHansmeyer JMihaljević H(2023)Generative Models for Synthetic Urban Mobility Data: A Systematic Literature ReviewACM Computing Surveys10.1145/361022456:4(1-37)Online publication date: 10-Nov-2023
https://dl.acm.org/doi/10.1145/3610224
Kim JThakur GChristopher S(2023)A Design of Activity-Based Mobility InterventionProceedings of the 18th International Symposium on Spatial and Temporal Data10.1145/3609956.3609970(131-140)Online publication date: 23-Aug-2023
https://dl.acm.org/doi/10.1145/3609956.3609970
Chen WLiu XZhang JLam IHuang ZDong RWang XZhang T(2023)MIWA: Mixed-Initiative Web Automation for Better User Control and ConfidenceProceedings of the 36th Annual ACM Symposium on User Interface Software and Technology10.1145/3586183.3606720(1-15)Online publication date: 29-Oct-2023
https://dl.acm.org/doi/10.1145/3586183.3606720
Sasada TTaenaka YKadobayashi Y(2023)Oblivious Statistic Collection With Local Differential Privacy in Mutual DistrustIEEE Access10.1109/ACCESS.2023.325156011(21374-21386)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3251560
Wischhof LKilian MSchuhbäck SRupp MKöster G(2023)Mobility in pedestrian communication simulations: Impact of microscopic models and solutions for integrationComputer Communications10.1016/j.comcom.2023.09.029212(90-103)Online publication date: Dec-2023
https://doi.org/10.1016/j.comcom.2023.09.029
Huang D(2023)Generative model for human mobilityHandbook of Mobility Data Mining10.1016/B978-0-443-18424-6.00002-7(95-115)Online publication date: 2023
https://doi.org/10.1016/B978-0-443-18424-6.00002-7
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