A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 2 Issue 3
Jul.  2015

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 7.847, Top 10% (SCI Q1)
    CiteScore: 13.0, Top 5% (Q1)
    Google Scholar h5-index: 64, TOP 7
Turn off MathJax
Article Contents
Kecai Cao, Yangquan Chen, Dan Stuart and Dong Yue, "Cyber-physical Modeling and Control of Crowd of Pedestrians: A Review and New Framework," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 3, pp. 334-344, 2015.
Citation: Kecai Cao, Yangquan Chen, Dan Stuart and Dong Yue, "Cyber-physical Modeling and Control of Crowd of Pedestrians: A Review and New Framework," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 3, pp. 334-344, 2015.

Cyber-physical Modeling and Control of Crowd of Pedestrians: A Review and New Framework

Funds:

This work was supported by National Natural Science Foundation of China (61374055), Natural Science Foundation of Jiangsu Province (BK20131381), China Postdoctoral Science Foundation Funded Project (2013M541663), Jiangsu Planned Projects for Postdoctoral Research Funds (1202015C), Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry (BJ213022), and Scientific Research Foundation of Nanjing University of Posts and Telecommunications (NY214075, XJKY14004).

  • Recent advances in modeling and control of crowd of pedestrians are briefly surveyed in this paper. Possibilities of applying fractional calculus in the modeling of crowd of pedestrians have been shortly reviewed and discussed from different aspects such as descriptions of motion, interactions of long range and effects of memory. Control of the crowd of pedestrians have also been formulated using the framework of cyber-physical systems and been realized using networked Segways with onboard emergency response personnels to regulate the velocity and flux of the crowd. Platform for verification of the theoretical results are also provided in this paper.

     

  • loading
  • [1]
    Moussaid M, Helbing D, Theraulaz G. How simple rules determine pedestrian behavior and crowd disasters. Proceedings of the National Academy of Sciences of the United States of America, 2011, 108(17):6884-6888
    [2]
    Czirók A, Barabási A L, Vicsek T. Collective motion of self-propelled particles:kinetic phase transition in one dimension. Physical Review Letters, 1999, 82(1):209-212
    [3]
    Spieser K, Davison D E. Multi-agent stabilisation of the psychological dynamics of one-dimensional crowds. Automatica, 2009, 45(3):657-664
    [4]
    Couzin I D. Collective cognition in animal groups. Trends in Cognitive Sciences, 2009, 13(1):36-43
    [5]
    Couzin I D, Krause J, Franks N R, Levin S A. Effective leadership and decisionmaking in animal groups on the move. Nature, 2005, 433(7025):513-516
    [6]
    Alfio Q, Alessandro V. Analysis of a geometrical multiscale model based on the coupling of ODES and PDES for blood flow simulations. Multiscale Modeling and Simulation, 2003, 1(2):173-195
    [7]
    Haken H. Information and Self-Organization:A Macroscopic Approach to Complex Systems. Berlin:Springer-Verlag, 2006.
    [8]
    Daniel T, Musse S R. Crowd Simulation. Berlin:Springer-Verlag, 2007.
    [9]
    Peacock R D, Kuligowski E D, Averill J D. Pedestrian and Evacuation Dynamics. Berlin:Springer-Verlag, 2010.
    [10]
    Bellomo N. Modeling Complex Living Systems:A Kinetic Theory and Stochastic Game Approach. New York:Birkhauser Boston, 2008.
    [11]
    Kachroo P, Al-nasur S J, Wadoo S A, Shende A. Pedestrian Dynamics:Feedback Control of Crowd Evacuation. Berlin:Springer-Verlag, 2008.
    [12]
    Pelechano N, Allbeck J M, Badler N I. Virtual Crowds Methods, Simulation, and Control. New York:Morgan and Claypool Publishers, 2008.
    [13]
    Timmermans H. Pedestrian Behavior:Models, Data Collection and Applications. New York:Emerald Group Publishing Limited, 2009.
    [14]
    Kachroo P. Pedestrian Dynamics Mathematical Theory and Evacuation Control. New York:CRC Press, Taylor and Francis Group, 2009.
    [15]
    Barcelo J. Fundamentals of Traffic Simulation. Berlin:Springer Science and Business Media, 2010.
    [16]
    Berlonghi A E. Understanding and planning for different spectator crowds. Safety Science, 1995, 18(4):239-247
    [17]
    Helbing D, Buzna L, Johansson A, Werner T. Self-organized pedestrian crowd dynamics:experiments, simulations, and design solutions. Transportation Science, 2005, 39(1):1-24
    [18]
    Bellomo N, Bianca C, Coscia V. On the modeling of crowd dynamics:an overview and research perspectives. SeMA Journal, 2013, 54(1):25-46
    [19]
    Cristiani E, Piccoli B, Tosin A. Multiscale modeling of granular flows with application to crowd dynamics. Multiscale Modeling and Simulation, 2011, 9(1):155-182
    [20]
    Christian D. Applicable thermostatted models to crowd dynamics:comment on "thermostatted kinetic equations as models for complex systems in physics and life sciences" by Carlo Bianca. Physics of Life Reviews, 2012, 9(4):410-412
    [21]
    Bogdan P, Marculescu R. A fractional calculus approach to modeling fractal dynamic games. In:Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference. Orlando, FL:IEEE, 2011. 255-260
    [22]
    Stuart D, Christensen K, Chen A, Cao K C, Zeng C B, Chen Y Q. A framework for modeling and managing mass pedestrian evacuations involving individuals with disabilities:networked segways as mobile sensors and actuators. In:Proceedings of the 2013 ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. 2013. DETC2013-12652
    [23]
    Helbing D, Farkas I, Vicsek T. Simulating dynamical features of escape panic. Nature, 2000, 407(6803):487-490
    [24]
    Löhner R. On the modeling of pedestrian motion. Applied Mathematical Modelling, 2010, 34(2):366-382
    [25]
    Dirk H, Schweitzer F, Keltsch J, Molnar P. Active walker model for the formation of human and animal trail systems. Physical Review E, 1998, 56(3):2527-2539
    [26]
    Orsogna M R D, Chuang Y L, Bertozzi A L, Chayes L S. Self-propelled particles with soft-core interactions patterns, stability, and collapse. Physical Review Letters, 2006, 96(10):104302:1-4
    [27]
    Song Y Q, Gong J H, Niu L, Li Y, Jiang Y R, Zhang W L, Cui T J. A grid-based spatial data model for the simulation and analysis of individual behaviours in micro-spatial environments. Simulation Modelling Practice and Theory, 2013, 38:58-68
    [28]
    Rodriguez S O. Roadmap-Based Techniques for Modeling Group Behaviors in Multi-Agent Systems[Ph. D. dissertation], Texas A and M University, USA, 2012.
    [29]
    Wang B C, Zhang J F. Mean field games for large-population multiagent systems with Markov jump parameters. SIAM Journal on Control and Optimization, 2012, 50(4):2308-2334
    [30]
    Li T, Zhang J F. Asymptotically optimal decentralized control for large population stochastic multiagent systems. IEEE Transactions on Automatic Control, 2008, 53(7):1643-1660
    [31]
    Giese M A, Mukovskiy A, Park A N, Omlor L, Slotine J J E. Real-time synthesis of body movements based on learned primitives. Visual motion analysis. In:Proceedings of the 2009 International Dagstuhl Seminar Statistical and Geometrical Approaches to Visual Motion Analysis. Berlin:Springer-Verlag, 2009, 5604:107-127
    [32]
    Mukovskiy A, Slotine J J E, Giese M A. Dynamically stable control of articulated crowds. Journal of Computational Science, 2013, 4(4):304-310
    [33]
    Burchan B O, Lien J M, Amato N M. Better group behaviors using rule-based roadmaps. In:Proceedings of the 2004 Springer Tracts in Advanced Robotics. Berlin:Springer-Verlag, 2004. 95-112
    [34]
    Zhang H M. New perspectives on continuum traffic flow models. Networks and Spatial Economics, 2001, 1(1-2):9-33
    [35]
    Colombo R M, Goatin P, Rosini M D. A macroscopic model for pedestrian flows in panic situations. International Series Mathematical Sciences and Applications, 2010, 32:255-272
    [36]
    Hughes R L. A continuum theory for the flow of pedestrians. Transportation Research Part B:Methodological, 2002, 36(6):507-535
    [37]
    Jiang Y Q, Zhang P, Wong S C, Liu R X. A higher-order macroscopic model for pedestrian flows. Physica A:Statistical Mechanics and Its Applications, 2010, 389(21):4623-4635
    [38]
    Xiong M Z, Cai W T, Zhou S P, Low M Y H, Tian F, Chen D, Ong D W Z, Hamilton B D. A case study of multi-resolution modeling for crowd simulation. In:Proceedings of the 2009 Simulation Multiconference. San Diego, California, USA:IEEE, 2009.
    [39]
    Xiong M Z, Lees M, Cai W T, Zhou S P, Low M Y H. Hybrid modelling of crowd simulation. Procedia Computer Science, 2010, 1(1):57-65
    [40]
    Arlotti L, Bellomo N, Lachowicz M. Kinetic equations modelling population dynamics. Transport Theory and Statistical Physics, 2000, 29(1-2):125-139
    [41]
    Bellomo N, Delitala M, Coscia V. On the mathematical theory of vehicular traffic flow I. fluid dynamic and kinetic modelling. Mathematical Models and Methods in Applied Sciences, 2002, 12(12):1801-1843
    [42]
    Dirk H. A fluid dynamic model for the movement of pedestrians. Complex Systems, 1992, 6:391-415
    [43]
    Hoogendoorn S P, Bovy P H L. Gas-kinetic modeling and simulation of pedestrian flows. Journal of the Transportation Research Board, 2007, 1710(1):28-36
    [44]
    De Lillo S, Salvatori M C, Bellomo N. Mathematical tools of the kinetic theory of active particles with some reasoning on the modelling progression and heterogeneity. Mathematical and Computer Modelling, 2007, 45(5-6):564-578
    [45]
    Bellomo N, Bellouquid A. On the modeling of crowd dynamics looking at the beautiful shapes of swarms. Networks and Heterogeneous Media, 2011, 6(3):383-399
    [46]
    Delitala M. Nonlinear models of vehicular traffic flow-new frameworks of the mathematical kinetic theory. Comptes Rendus Mecanique, 2003, 331(12):817-822
    [47]
    Arlotti L, De Angelis E, Fermo L, Lachowicz M, Bellomo N. On a class of integro-differential equations modeling complex systems with nonlinear interactions. Applied Mathematics Letters, 2012, 25(3):490-495
    [48]
    Hilfer R. Applications of Fractional Calculus in Physics. New York:World Scientific Publishing Company, 2000.
    [49]
    Zaslavsky G M. Chaos, fractional kinetics, and anomalous transport. Physics Reports, 2002, 371(6):461-580
    [50]
    Sokolov I M. Distributed-order fractional kinetics. Acta Physica Polonica B, 2004, 35(4):1323-1341
    [51]
    Cao K C, Zeng C B, Stuart D, Chen Y Q. Fractional order dynamic modeling of crowd pedestrians. In:Proceedings of the 5th Symposium on Fractional Differentiation and Its Applications. Nanjing, China:Hohai University, 2012.
    [52]
    Wang L F, Yang X J, Baleanu D, Cattani C, Zhao Y. Fractal dynamical model of vehicular traffic flow within the local fractional conservation laws. Abstract and Applied Analysis, 2014, 35:1-5
    [53]
    Bogdan P, Marculescu R. Towards a science of cyber-physical systems design. In:Proceedings of the 2011 IEEE/ACM International Conference on Cyber-Physical Systems. Chicago, IL:IEEE, 2011. 99-108
    [54]
    Romanovas M, Klingbeil L, Trachtler1 M, Manoli Y. Fractional langevin models for human motion tracking in recursive Bayesian estimation algorithms. In:Proceedings of the 2009 Conference Mathematical Modelling and Analysis. Daugavpils, Latvia:MMA, 2009. 27-30
    [55]
    Romanovas M, Traechtler M, Klingbeil L, Manoli Y. On fractional models for human motion tracking. Journal of Vibration and Control, 2013, 20(7):986-997
    [56]
    Romanovas M, Klingbeil L, Traechtler M, Manoli Y. On fractional models for human motion tracking. In:Proceedings of the 5th Symposium on Fractional Differentiation and Its Applications. Nanjing, China:Hohai University, 2012.
    [57]
    Couzin I D, Krause J, James R, Ruxton G D, Franks N R. Collective memory and spatial sorting in animal groups. Journal of Theoretical Biology, 2002, 218(1):1-11
    [58]
    Mogilner A, Edelstein-Keshet L, Bent L, Spiros A. Mutual interactions, potentials, and individual distance in a social aggregation. Journal of Mathematical Biology, 2003, 47(4):353-389
    [59]
    John J, Tu Y H. Long-range order in a two-dimensional dynamical XY model:how birds fly together. Physical Review Letters, 1995, 75:4326-4329
    [60]
    Zaslavsky G M, Edelman M, Tarasov V E. Dynamics of the chain of forced oscillators with long-range interaction:from synchronization to chaos. Chaos:An Interdisciplinary Journal of Nonlinear Science, 2007, 17(4):043124
    [61]
    Hongler M O, Filliger R, Gallay O. Local versus nonlocal barycentric interactions in 1d agent dynamics. Mathematical Biosciences and Engineering, 2014, 11(2):303-315
    [62]
    Ishiwata R, Sugiyama Y. Relationships between power-law long-range interactions and fractional mechanics. Physica A, 2012, 391(23):5827-5838
    [63]
    Zhang H M. A mathematical theory of traffic hysteresis. Transportation Research Part B:Methodological, 1999, 33(1):1-23
    [64]
    Tarasov V E, Zaslavsky G M. Fokker-Planck equation with fractional coordinate derivatives. Physica A:Statistical Mechanics and Its Applications, 2008, 387(26):6505-6512
    [65]
    Colombo R M, Lécureux-Mercier M. Nonlocal crowd dynamics models for several populations. Acta Mathematica Scientia, 2012, 32(1):177-196
    [66]
    Wadoo S A, Kachroo P. Feedback control design and stability analysis of one dimensional evacuation system. In:Proceedings of the 2006 Intelligent Transportation Systems Conference. Toronto, Ont.:IEEE, 2006. 618-623
    [67]
    Wadoo S A, Kachroo P. Feedback control design and stability analysis of two dimensional evacuation system. In:Proceedings of the 2006 Intelligent Transportation Systems Conference. Toronto, Ont.:IEEE, 2006. 1108-1113
    [68]
    Wadoo S A, Al-Nasur S, Kachroo P. Feedback control of macroscopic crowd dynamic models. In:Proceedings of the 2008 American Control Conference. Seattle, WA:IEEE, 2008. 2558-2563
    [69]
    Wadoo S A, Kachroo P. Feedback control of crowd evacuation in one dimension. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(1):182-193
    [70]
    Dong H R, Yang X X, Chen Y, Wang Q L. Pedestrian evacuation in two-dimension via state feedback control. In:Proceedings of the 2013 American Control Conference. Washington, DC:IEEE, 2013. 302-306
    [71]
    Shende A, Singh M P, Kachroo P. Optimization-based feedback control for pedestrian evacuation from an exit corridor. IEEE Transactions on Intelligent Transportation Systems, 2011, 12(4):1167-1176
    [72]
    Shende A, Singh M P, Kachroo P. Optimal feedback flow rates for pedestrian evacuation in a network of corridors. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(3):1053-1066
    [73]
    Alvarez L, Roberto H, Perry L. Traffic flow control in automated highway systems. Control Engineering Practice, 1999, 7(9):1071-1078
    [74]
    Yang X X, Dong H R, Chen Y, Wang Q L. Pedestrian evacuation in two-dimension via robust feedback control. In:Proceedings of the 10th IEEE International Conference on Control and Automation. Hangzhou, China:IEEE, 2013. 1087-1091
    [75]
    Wadoo S A. Sliding mode control of crowd dynamics. IEEE Transactions on Control Systems Technology, 2013, 21(3):1008-1015
    [76]
    Jadbabaie A, Lin J, Morse A S. Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Transactions on Automatic Control, 2003, 48(6):988-1001
    [77]
    Dyer J R G, Johansson A, Helbing D, Couzin I D, Krause J. Leadership, consensus decision making and collective behaviour in humans. Philosophical Transactions of the Royal Society B, 2009, 364(1518):781-789
    [78]
    Dyer J R G, Ioannou C C, Morrell L J, Croft D P, Couzin I D, Waters D A, Krause J. Consensus decision making in human crowds. Animal Behaviour, 2008, 75(2):461-470
    [79]
    Aubé F, Shield R. Modeling the effect of leadership on crowd flow dynamics. In:Proceedings of the 6th International Conference on Cellular Automata for Research and Industry. Amsterdam, the Netherlands:Springer, 2004, 3305:601-611
    [80]
    Kihong P, Walter W. Self-similar Network Traffic and Performance Evaluation. New York:John Wiley and Sons, Inc., 2000.
    [81]
    Marculescu R, Bogdan P. Cyberphysical systems workload modeling and design optimization. IEEE Design and Test of Computers, 2011, 28(4):78-87
    [82]
    Li T, Zhang J F. Asymptotically optimal decentralized control for interacted ARX multi-agent systems. In:Proceedings of the 6th IEEE International Conference on Control and Automation. Guangzhou, China:IEEE, 2007. 1296-1301
    [83]
    Huang M Y. Large-population LQG games involving a major player:the nash certainty equivalence principle. SIAM Journal on Control and Optimization, 2010, 48(5):3318-3353
    [84]
    Cao Y G, Yu W W, Ren W, Chen G R. An overview of recent progress in the study of distributed multi-agent coordination. IEEE Transactions on Industrial Informatics, 2013, 9(1):427-438
    [85]
    Min H B, Liu Y, Wang S C, Sun F C. An overview on coordination control problem of multi-agent system. Acta Automatica Sinica, 2012, 38(10):1557-1570
    [86]
    Nourian M, Malhame R P, Huang M, Caines P E. Mean field (NCE) formulation of estimation based leader-follower collective dynamics. International Journal of Robotics and Automation, 2011, 26(1):120-129
    [87]
    Wang B C, Zhang J F. Distributed control of multi-agent systems with random parameters and a major agent. Automatica, 2012, 48(9):2093-2106
    [88]
    Huang M Y, Caines P E, Malhame R P. Social optima in mean field LQG control:centralized and decentralized strategies. IEEE Transactions on Automatic Control, 2012, 57(7):1736-1751
    [89]
    Nourian M, Caines P E, Malhame R P, Huang M Y. Nash, social and centralized solutions to consensus problems via mean field control theory. IEEE Transactions on Automatic Control, 2013, 58(3):639-653
    [90]
    Nourian M, Caines P E, Malhame R P, Huang M Y. Mean field LQG control in leaderfollower stochastic multi-agent systems likelihood ratio based adaptation. IEEE Transactions on Automatic Control, 2012, 57(11):2801-2816
    [91]
    Lasry J M, Lions P L. Mean field games. Japanese Journal of Mathematics, 2007, 2(1):229-260
    [92]
    Dogbé C. Modeling crowd dynamics by the mean-field limit approach. Mathematical and Computer Modelling, 2010, 52(9-10):1506-1520
    [93]
    Lachapelle A, Wolfram M T. On a mean field game approach modeling congestion and aversion in pedestrian crowds. Transportation Research Part B:Methodological, 2011, 45(10):1572-1589
    [94]
    Helbing D, Molnár P. Social force model for pedestrian dynamics. Physical Review E, 1995, 51:4282-4286
    [95]
    Liang J S, Chen Y Q. Diff-mas2-user's Manual. Technical Report, Center for Self-Organizing and Intelligent Systems (CSOIS), Department of Electrical and Computer Engineering, College of Engineering, Utah State University, USA, 2004.
    [96]
    He J P, Cheng P, Shi L, Chen J M. SATS:secure average-consensusbased time synchronization in wireless sensor networks. IEEE Transactions on Signal Processing, 2013, 61(24):6387-6400
    [97]
    Liu H, Cao X H, He J P, Cheng P. Distributed identification of the most critical node for average consensus. In:Proceedings of the 19th International Federation of Automatic Control World Congress. Cape Town, South Africa:IFAC, 2014. 1843-1848
    [98]
    Zhao C C, He J P, Cheng P, Chen J M. Secure consensus against message manipulation attacks in synchronous networks. In:Proceedings of the 19th International Federation of Automatic Control World Congress. Cape Town, South Africa:IFAC, 2014. 1182-1187

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (1084) PDF downloads(5) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return