Abstract
Collective movement simulations are challenging and important in many areas, including life science, mathematics, physics, information science and public safety. In this survey, we provide a comprehensive review of the state-of-the-art techniques for collective movement simulations. We start with a discussion on certain concepts to help beginners understand it more systematically. Then, we analyze the intelligence among different collective objects and the emphasis in different fields. Next, we classify existing collective movement simulation methods into four categories according to their effects, namely versatility, accuracy, dynamic adaptability, and assessment feedback capability. Furthermore, we introduce five applications of layout optimization, emergency control, dispatching, unmanned systems, and other derivative applications. Finally, we summarize possible future research directions.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
R. S. Parpinelli, H. S. Lopes. New inspirations in swarm intelligence: A survey. International Journal of Bio-inspired Computation, vol. 3, no. 1, pp. 1–16, 2011. DOI: https://doi.org/10.1504/ijbic.2011.038700.
T. Vicsek, A. Zafeiris. Collective motion. Physics Reports, vol. 517, no. 3–4, pp. 71–140, 2012. DOI: https://doi.org/10.1016/j.physrep.2012.03.004.
A. Strandburg-Peshkin, D. R. Farine, I. D. Couzin, M. C. Crofoot. Shared decision-making drives collective movement in wild baboons. Science, vol. 348, no. 6241, pp. 1358–1361, 2015. DOI: https://doi.org/10.1126/science.aaa5099.
A. John, A. Schadschneider, D. Chowdhury, K. Nishinari. Trafficlike collective movement of ants on trails: Absence of a jammed phase. Physical Review Letters, vol. 102, no. 10, Article number 108001, 2009. DOI: https://doi.org/10.1103/PhysRevLett.102.108001.
G. Andrienko, N. Andrienko, G. Fuchs, J. Wood. Revealing patterns and trends of mass mobility through spatial and temporal abstraction of origin-destination movement data. IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 9, pp. 2120–2136, 2017. DOI: https://doi.org/10.1109/TVCG.2016.2616404.
M. L. Xu, Y. P. Wu, Y. D. Ye, I. Farkas, H. Jiang, Z. G. Deng. Collective crowd formation transform with mutual information-based runtime feedback. Computer Graphics Forum, vol. 34, no. 1, pp. 60–73, 2015. DOI: https://doi.org/10.1111/cgf.12459.
I. Navarro, F. Matía. A framework for the collective movement of mobile robots based on distributed decisions. Robotics and Autonomous Systems, vol. 59, no. 10, pp. 685–697, 2011. DOI: https://doi.org/10.1016/j.robot.2011.05.001.
M. Del Mar Delgado, M. Miranda, S. J. Alvarez, E. Gurarie, W. F. Fagan, V. Penteriani, A. Di Virgilio, J. M. Morales. The importance of individual variation in the dynamics of animal collective movements. Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 373, no. 1746, Article number 20170008, 2018. DOI: https://doi.org/10.1098/rstb.2017.0008.
L. Gibelli, N. Bellomo. Crowd Dynamics, Volume 1: Theory, Models, and Safety Problems, Cham, Switzerland: Springer, 2018.
R. Eberhart, J. Kennedy. A new optimizer using particle swarm theory. In Proceedings of the 6th International Symposium on Micro Machine and Human Science, IEEE, Nagoya, Japan, pp. 39–43, 1995. DOI: https://doi.org/10.1109/MHS.1995.494215.
C. W. Reynolds. Flocks, herds and schools: A distributed behavioral model. In Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, ACM, Anaheim, USA, pp. 25–34, 1987. DOI: https://doi.org/10.1145/37401.37406.
X. Y. Tu, D. Terzopoulos. Artificial fishes: Physics, locomotion, perception, behavior. In Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, ACM, Orlando, USA, pp. 43–50, 1994. DOI: https://doi.org/10.1145/192161.192170.
D. Karaboga. An Idea Based on Honey Bee Swarm for Numerical Optimization, Technical Report TR06, Computer Engineering Department, Engineering Faculty, Erciyes University, Kayseri, Turkey, 2005.
M. Dorigo, V. Maniezzo, A. Colorni. Ant system: Optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics–Part B, vol. 26, no. 1, pp. 29–41, 1996. DOI: https://doi.org/10.1109/3477.484436.
Z. Han, A. L. Swindlehurst, K. J. R. Liu. Optimization of MANET connectivity via smart deployment/movement of unmanned air vehicles. IEEE Transactions on Vehicular Technology, vol. 58, no. 7, pp. 3533–3546, 2009. DOI: https://doi.org/10.1109/TVT.2009.2015953.
M. Rubenstein, A. Cornejo, R. Nagpal. Programmable self-assembly in a thousand-robot swarm. Science, vol. 345, no. 6198, pp. 795–799, 2014. DOI: https://doi.org/10.1126/science.1254295.
S. Mariani, F. Zambonelli. Degrees of autonomy in coordinating collectives of self-driving vehicles. In Proceedings of the 9th International Symposium on Leveraging Applications of Formal Methods, Springer, Rhodes, Greece, pp. 189–204, 2020. DOI: https://doi.org/10.1007/978-3-030-61470-6_12.
K. Guo, H. Guan. Modelling of driver/vehicle directional control system. Vehicle System Dynamics, vol. 22, no. 3–4, pp. 141–184, 1993. DOI: https://doi.org/10.1080/00423119308969025.
S. W. Yang, T. R. Li, X. Gong, B. Peng, J. Hu. A review on crowd simulation and modeling. Graphical Models, vol. 111, Article number 101081, 2020. DOI: https://doi.org/10.1016/j.gmod.2020.101081.
Q. W. Chao, H. K. Bi, W. Z. Li, T. L. Mao, Z. Q. Wang, M. C. Lin, Z. G. Deng. A survey on visual traffic simulation: Models, evaluations, and applications in autonomous driving. Computer Graphics Forum, vol. 39, no. 1, pp. 287–308, 2020. DOI: https://doi.org/10.1111/cgf.13803.
S. J. Chung, A. A. Paranjape, P. Dames, S. J. Shen, V. Kumar. A survey on aerial swarm robotics. IEEE Transactions on Robotics, vol. 34, no. 4, pp. 837–855, 2018. DOI: https://doi.org/10.1109/TRO.2018.2857475.
A. Chakraborty, A. K. Kar. Swarm Intelligence: A review of algorithms. Nature-inspired Computing and Optimization: Theory and Applications, S. Patnaik, X. S. Yang, K. Nakamatsu, Eds., Cham, Switzerland: Springer, pp. 475–494, 2017.
F. Martinez-Gil, M. Lozano, I. García-Fernández, F. Fernández. Modeling, evaluation, and scale on artificial pedestrians: A literature review. ACM Computing Surveys, vol. 50, no. 5, Article number 72, 2018. DOI: https://doi.org/10.1145/3117808.
M. L. Xu, H. Jiang, X. G. Jin, Z. G. Deng. Crowd simulation and its applications: Recent advances. Journal of Computer Science and Technology, vol. 29, no. 5, pp. 799–811, 2014. DOI: https://doi.org/10.1007/s11390-014-1469-y.
Y. Li, M. Y. Chen, Z. Dou, X. P. Zheng, Y. Cheng, A. Mebarki. A review of cellular automata models for crowd evacuation. Physica A: Statistical Mechanics and its Applications, vol. 526, Article number 120752, 2019. DOI: https://doi.org/10.1016/j.physa.2019.03.117.
C. Kownacki, D. Ołdziej. Flocking algorithm for fixed-wing unmanned aerial vehicles. In Proceedings of the 3rd Advances in Aerospace Guidance, Navigation and Control, Springer, Toulouse, French, pp. 415–431, 2015.
S. Z. Qu, M. Abouheaf, W. Gueaieb, D. Spinello. An adaptive fuzzy reinforcement learning cooperative approach for the autonomous control of flock systems. In Proceedings of IEEE International Conference on Robotics and Automation, Xi’an, China, pp. 8927–8933, 2021. DOI: https://doi.org/10.1109/ICRA48506.2021.9561204.
R. Olfati-Saber. Flocking for multi-agent dynamic systems: Algorithms and theory. IEEE Transactions on Automatic Control, vol. 51, no. 3, pp. 401–420, 2006. DOI: https://doi.org/10.1109/TAC.2005.864190.
M. M. Millonas. Swarms, phase transitions, and collective intelligence, [Online], Available: https://arxiv.org/abs/adap-org/9306002, 1993.
J. Kennedy, R. Eberhart. Particle swarm optimization. In Proceedings of ICNN’95-International Conference on Neural Networks, IEEE, Perth, Australia, pp. 1942–1948, 1995. DOI: https://doi.org/10.1109/ICNN.1995.488968.
E. Bonabeau, G. Theraulaz, M. Dorigo, G. Theraulaz. Swarm Intelligence: From Natural to Artificial Systems, New York, USA: Oxford University Press, 1999.
A. Y. Zomaya. Handbook of Nature-Inspired and Innovative Computing: Integrating Classical Models with Emerging Technologies, New York, USA: Springer, 2006.
C. Blum, D. Merkle. Swarm Intelligence: Introduction and Applications, Berlin, Germany: Springer, 2008.
L. F. Henderson. On the fluid mechanics of human crowd motion. Transportation Research, vol. 8, no. 6, pp. 509–515, 1974. DOI: https://doi.org/10.1016/0041-1647(74)90027-6.
M. Moussaid, S. Garnier, G. Theraulaz, D. Helbing. Collective information processing and pattern formation in swarms, flocks, and crowds. Topics in Cognitive Science, vol. 1, no. 3, pp. 469–497, 2009. DOI: https://doi.org/10.1111/1.1756-8765.2009.01028.x.
L. F. Henderson, D. J. Lyons. Sexual differences in human crowd motion. Nature, vol. 240, no. 5380, pp. 353–355, 1972. DOI: https://doi.org/10.1038/240353a0.
I. Karamouzas, M. Overmars. Simulating the local behaviour of small pedestrian groups. In Proceedings of the 17th ACM Symposium on Virtual Reality Software and Technology, ACM, Hong Kong, China, pp. 183–190, 2010. DOI: https://doi.org/10.1145/1889863.1889906.
M. Moussaïd, N. Perozo, S. Garnier, D. Helbing, G. Theraulaz. The walking behaviour of pedestrian social groups and its impact on crowd dynamics. PLoS One, vol. 5, no. 4, Article number e10047, 2010. DOI: https://doi.org/10.1371/journal.pone.0010047.
Z. Ren, P. Charalambous, J. Bruneau, Q. Peng, J. Pettré. Group modeling: A unified velocity-based approach. Computer Graphics Forum, vol. 36, no. 8, pp. 45–56, 2017. DOI: https://doi.org/10.1111/cgf.12993.
T. Vicsek, A. Czirók, E. Ben-Jacob, I. Cohen, O. Shochet. Novel type of phase transition in a system of self-driven particles. Physical Review Letters, vol. 75, no. 6, pp. 1226–1229, 1995. DOI: https://doi.org/10.1103/PhysRevLett.75.1226.
D. Helbing. Traffic and related self-driven many-particle systems. Reviews of Modern Physics, vol. 73, no. 4, pp. 1067–1141, 2001. DOI: https://doi.org/10.1103/RevModPhys.73.1067.
D. Helbing. Collective phenomena and states in traffic and self-driven many-particle systems. Computational Materials Science, vol. 30, no. 1–2, pp. 180–187, 2004. DOI: https://doi.org/10.1016/j.commatsci.2004.01.026.
D. Helbing, I. Farkas, T. Vicsek. Simulating dynamical features of escape panic. Nature, vol. 407, no. 6803, pp. 487–490, 2000. DOI: https://doi.org/10.1038/35035023.
R. S. Crutchfield. Conformity and character. American Psychologist, vol. 10, no. 5, pp. 191–198, 1955. DOI: https://doi.org/10.1037/h0040237.
X. T. Li, J. Zhang, M. H. Yin. Animal migration optimization: An optimization algorithm inspired by animal migration behavior. Neural Computing and Applications, vol. 24, no. 7, pp. 1867–1877, 2014. DOI: https://doi.org/10.1007/s00521-013-1433-8.
X. S. Yang. Nature-inspired Metaheuristic Algorithms, Frome, UK: Luniver Press, 2010.
S. Mirjalili, A. Lewis. The whale optimization algorithm. Advances in Engineering Software, vol. 95, pp. 51–67, 2016. DOI: https://doi.org/10.1016/j.advengsoft.2016.01.008.
C. W. Reynolds. Steering behaviors for autonomous characters. In Proceedings of Game Developers Conference, San Jose, USA, pp. 763–782, 1999.
D. Helbing, L. Buzna, A. Johansson, T. Werner. Self-organized pedestrian crowd dynamics: Experiments, simulations, and design solutions. Transportation Science, vol. 39, no. 1, pp. 1–24, 2005. DOI: https://doi.org/10.1287/trsc.1040.0108.
J. A. Sokoiowski, C. M. Banks. Principles of Modeling and Simulation: A Multidisciplinary Approach, Hoboken, USA: John Wiley & Sons, 2011.
P. A. Fishwick. Simulation Model Design and Execution: Building Digital Worlds, New Jersey, USA: Prentice Hall PTR, 1995.
J. Banks. Handbook of Simulation: Principles, Methodology, Advances, Applications, and Practice, New York, USA: John Wiley & Sons, 1998.
J. Kennedy, R. C. Eberhart, Y. H. She. Swarm Intelligence, San Francisco, USA: Morgan Kaufmann Publishers, 2001.
S. L. Lima, L. M. Dill. Behavioral decisions made under the risk of predation: A review and prospectus. Canadian Journal of Zoology, vol. 68, no. 4, pp. 619–640, 1990. DOI: https://doi.org/10.1139/z90-092.
J. Krause, G. D. Ruxton. Living in Groups, Oxford, USA: Oxford University Press, 2002.
G. Beauchamp. The effect of group size on mean food intake rate in birds. Biological Reviews, vol. 73, no. 4, pp. 449–472, 1998. DOI: https://doi.org/10.1111/j.1469-185X.1998.tb00179.x.
B. L. Partridge, T. J. Pitcher. The sensory basis of fish schools: Relative roles of lateral line and vision. Journal of Comparative Physiology, vol. 135, no. 4, pp. 315–325, 1980. DOI: https://doi.org/10.1007/BF00657647.
A. Huth, C. Wissel. The simulation of the movement of fish schools. Journal of Theoretical Biology, vol. 156, no. 3, pp. 365–385, 1992. DOI: https://doi.org/10.1016/S0022-5193(05)80681-2.
J. L. Deneubourg, J. M. Pasteels, J. C. Verhaeghe. Probabilistic behaviour in ants: A strategy of errors? Journal of Theoretical Biology, vol. 105, no. 2, pp. 259–271, 1983. DOI: https://doi.org/10.1016/S0022-5193(83)80007-1.
J. L. Deneubourg, S. Goss. Collective patterns and decision-making. Ethology Ecology & Evolution, vol. 1, no. 4, pp. 295–311, 1989. DOI: https://doi.org/10.1080/08927014.1989.9525500.
S. Goss, R. Beckers, J. L. Deneubourg, S. Aron, J. M. Pasteels. How trail laying and trail following can solve foraging problems for ant colonies. In Proceedings of Behavioural Mechanisms of Food Selection, Springer, Berlin Heidelberg Germany, pp. 661–678, 1990.
D. R. MacNulty, L. D. Mech, D. W. Smith. A proposed ethogram of large-carnivore predatory behavior, exemplified by the wolf. Journal of Mammalogy, vol. 88, no. 3, pp. 595–605, 2007. DOI: https://doi.org/10.1644/06-MAMM-A-119R1.1.
K. Von Frisch. The Dance Language and Orientation of Bees, Cambridge, UK: Harvard University Press, 1993.
J. C. Biesmeijer, T. D. Seeley. The use of waggle dance information by honey bees throughout their foraging careers. Behavioral Ecology and Sociobiology, vol. 59, no. 1, pp. 133–142, 2005. DOI: https://doi.org/10.1007/s00265-005-0019-6.
B. Granovskiy, T. Latty, M. Duncan, D. J. T. Sumpter, M. Beekman. How dancing honey bees keep track of changes: The role of inspector bees. Behavioral Ecology, vol. 23, no. 3, pp. 588–596, 2012. DOI: https://doi.org/10.1093/beheco/ars002.
H. C. Berg. Motile behavior of bacteria. Physics Today, vol. 53, no. 1, pp. 24–29, 2000. DOI: https://doi.org/10.1063/1.882934.
B. Khelifa, M. R. Laouar. A holonic intelligent decision support system for urban project planning by ant colony optimization algorithm. Applied Soft Computing, vol. 96, Article number 106621, 2020. DOI: https://doi.org/10.1016/j.asoc.2020.106621.
H. T. Zhao, C. S. Zhang, X. Y. Zheng, C. Zhang, B. Zhang. A decomposition-based many-objective ant colony optimization algorithm with adaptive solution construction and selection approaches. Swarm and Evolutionary Computation, vol. 68, Article number 100977, 2022. DOI: https://doi.org/10.1016/j.swevo.2021.100977.
Y. Wang, L. Wang, G. C. Chen, Z. Q. Cai, Y. Q. Zhou, L. N. Xing. An improved ant colony optimization algorithm to the periodic vehicle routing problem with time window and service choice. Swarm and Evolutionary Computation, vol. 55, Article number 100675, 2020. DOI: https://doi.org/10.1016/j.swevo.2020.100675.
Y. Tian, X. T. Zheng, X. Y. Zhang, Y. C. Jin. Efficient large-scale multiobjective optimization based on a competitive swarm optimizer. IEEE Transactions on Cybernetics, vol. 50, no. 8, pp. 3696–3708, 2020. DOI: https://doi.org/10.1109/TCYB.2019.2906383.
X. Y. Wang, B. R. Zhang, J. Wang, K. Zhang, Y. C. Jin. A cluster-based competitive particle swarm optimizer with a sparse truncation operator for multi-objective optimization. Swarm and Evolutionary Computation, vol. 71, Article number 101083, 2022. DOI: https://doi.org/10.1016/j.swevo.2022.101083.
H. Jiang, Z. G. Deng, M. L. Xu, X. J. He, T. L. Mao, Z. Q. Wang. An emotion evolution based model for collective behavior simulation. In Proceedings of ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, Montreal, Canada, Article number 10, 2018. DOI: https://doi.org/10.1145/3190834.3190844.
M. L. Xu, C. C. Li, P. Lv, W. Chen, D. Manocha, Z. G. Deng, B. Zhou. Crowd simulation model integrating “physiology-psychology-physics” factors. [Online], Available: https://arxiv.org/pdf/1801.00216v1.pdf, 2017.
X. Hong, G. J. Zhang, D. J. Lu, H. Liu, L. Zhu, M. L. Xu. Personalized crowd emotional contagion coupling the virtual and physical cyberspace. IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 3, pp. 1638–1652, 2022. DOI: https://doi.org/10.1109/TSMC.2020.3034395.
M. L. Xu, C. C. Li, P. Lv, W. Chen, Z. G. Deng, B. Zhou, D. Manocha. Emotion-based crowd simulation model based on physical strength consumption for emergency scenarios. IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 11, pp. 6977–6991, 2021. DOI: https://doi.org/10.1109/TITS.2020.3000607.
M. L. Xu, X. Z. Xie, P. Lv, J. W. Niu, H. Wang, C. C. Li, R. J. Zhu, Z. G. Deng, B. Zhou. Crowd behavior simulation with emotional contagion in unexpected multihazard situations. IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 3, pp. 1567–1581, 2019. DOI: https://doi.org/10.1109/TSMC.2019.2899047.
J. H. Wang, S. M. Lo, J. H. Sun, Q. S. Wang, H. L. Mu. Qualitative simulation of the panic spread in large-scale evacuation. Simulation, vol. 88, no. 12, pp. 1465–1474, 2012. DOI: https://doi.org/10.1177/0037549712456884.
N. R. Johnson. Panic at “The who concert stampede”: An empirical assessment. Social Problems, vol. 34, no. 4, pp. 362–373, 1987. DOI: https://doi.org/10.2307/800813.
G. J. Zhang, D. J. Lu, H. Liu. Strategies to utilize the positive emotional contagion optimally in crowd evacuation. IEEE Transactions on Affective Computing, vol. 11, no. 4, pp. 708–721, 2020. DOI: https://doi.org/10.1109/TAFFC.2018.2836462.
Y. Mao, S. W. Yang, Z. N. Li, Y. J. Li. Personality trait and group emotion contagion based crowd simulation for emergency evacuation. Multimedia Tools and Applications, vol. 79, no. 5, pp. 3077–3104, 2020. DOI: https://doi.org/10.1007/s11042-018-6069-3.
J. X. Xue, H. Yin, P. Lv, M. L. Xu, Y. F. Li. Crowd queuing simulation with an improved emotional contagion model. Science China Information Sciences, vol. 62, no. 4, Article number 44101, 2019. DOI: https://doi.org/10.1007/s11432-018-9654-6.
P. Lv, Z. J. Zhang, C. C. Li, Y. B. Guo, B. Zhou, M. L. Xu. Crowd behavior evolution with emotional contagion in political rallies. IEEE Transactions on Computational Social Systems, vol. 6, no. 2, pp. 377–386, 2019. DOI: https://doi.org/10.1109/TCSS.2018.2878461.
C. C. Li, P. Lv, D. Manocha, H. Wang, Y. F. Li, B. Zhou, M. L. Xu. ACSEE: Antagonistic crowd simulation model with emotional contagion and evolutionary game theory. IEEE Transactions on Affective Computing, vol. 13, no. 2, pp. 729–745, 2022. DOI: https://doi.org/10.1109/TAFFC.2019.2954394.
S. Doctor, G. K. Venayagamoorthy, V. G. Gudise. Optimal PSO for collective robotic search applications. In Proceedings of Congress on Evolutionary Computation, IEEE, Portland, USA, pp. 1390–1395, 2004. DOI: https://doi.org/10.1109/CEC.2004.1331059.
J. H. Liang, C. H. Lee. Efficient collision-free path-planning of multiple mobile robots system using efficient artificial bee colony algorithm. Advances in Engineering Software, vol. 79, pp. 47–56, 2015. DOI: https://doi.org/10.1016/j.advengsoft.2014.09.006.
G. G. Wang, H. E. Chu, S. Mirjalili. Three-dimensional path planning for UCAV using an improved bat algorithm. Aerospace Science and Technology, vol. 49, pp. 231–238, 2016. DOI: https://doi.org/10.1016/j.ast.2015.11.040.
C. Z. Qu, W. D. Gai, M. Y. Zhong, J. Zhang. A novel reinforcement learning based grey wolf optimizer algorithm for unmanned aerial vehicles (UAVs) path planning. Applied Soft Computing, vol. 89, Article number 106099, 2020. DOI: https://doi.org/10.1016/j.asoc.2020.106099.
P. Pandey, A. Shukla, R. Tiwari. Three-dimensional path planning for unmanned aerial vehicles using glowworm swarm optimization algorithm. International Journal of System Assurance Engineering and Management, vol. 9, no. 4, pp. 836–852, 2018. DOI: https://doi.org/10.1007/s13198-017-0663-z.
P. K. Das, H. S. Behera, B. K. Panigrahi. A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning. Swarm and Evolutionary Computation, vol. 28, pp. 14–28, 2016. DOI: https://doi.org/10.1016/j.swevo.2015.10.011.
C. Z. Qu, W. D. Gai, J. Zhang, M. Y. Zhong. A novel hybrid grey wolf optimizer algorithm for unmanned aerial vehicle (UAV) path planning. Knowledge-Based Systems, vol. 194, Article number 105530, 2020. DOI: https://doi.org/10.1016/j.knosys.2020.105530.
W. J. He, X. G. Qi, L. F. Liu. A novel hybrid particle swarm optimization for multi-UAV cooperate path planning. Applied Intelligence, vol. 51, no. 10, pp. 7350–7364, 2021. DOI: https://doi.org/10.1007/s10489-020-02082-8.
H. Wang, X. Y. He, L. Y. Chen, J. R. Yin, L. Han, H. Liang, F. B. Zhu, R. J. Zhu, Z. M. Gao, M. L. Xu. Cognition-driven traffic simulation for unstructured road networks. Journal of Computer Science and Technology, vol. 35, no. 4, pp. 875–888, 2020. DOI: https://doi.org/10.1007/s11390-020-9598-y.
H. Wang, M. L. Xu, F. B. Zhu, Z. G. Deng, Y. F. Li, B. Zhou. Shadow traffic: A unified model for abnormal traffic behavior simulation. Computers & Graphics, vol. 70, pp. 235–241, 2018. DOI: https://doi.org/10.1016/j.cag.2017.07.004.
M. Land, J. Horwood. Which parts of the road guide steering? Nature, vol. 377, no. 6547, pp. 339–340, 1995. DOI: https://doi.org/10.1038/377339a0.
X. Y. Wang, J. Q. Wang, J. L. Zhang, X. J. Ban. Lane-changing model with dynamic consideration of driver’s propensity. International Journal of Modern Physics C, vol. 26, no. 2, Article number 1550015, 2015. DOI: https://doi.org/10.1142/S0129183115500151.
M. L. Xu, H. Wang, S. L. Chu, Y. Gan, X. H. Jiang, Y. F. Li, B. Zhou. Traffic simulation and visual verification in smog. ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 1, Article number 3, 2019. DOI: https://doi.org/10.1145/3200491.
C. Pêcher, C. Lemercier, J. M. Cellier. Emotions drive attention: Effects on driver’s behaviour. Safety Science, vol. 47, no. 9, pp. 1254–1259, 2009. DOI: https://doi.org/10.1016/j.ssci.2009.03.011.
O. Taubman-Ben-Ari. The effects of positive emotion priming on self-reported reckless driving. Accident Analysis & Prevention, vol. 45, pp. 718–725, 2012. DOI: https://doi.org/10.1016/j.aap.2011.09.039.
J. Y. Lu, X. F. Xie, R. G. Zhang. Focusing on appraisals: How and why anger and fear influence driving risk perception. Journal of Safety Research, vol. 45, pp. 65–73, 2013. DOI: https://doi.org/10.1016/j.jsr.2013.01.009.
X. E. Zhang. China in global landscape of life sciences. Bulletin of Chinese Academy of Sciences, vol. 37, no. 5, pp. 622–635, 2022. DOI: https://doi.org/10.16418/j.issn.1000-3045.20220426003. (in Chinese)
S. W. Li, O. Zeliger, L. Strahs, R. Báez-Mendoza, L. M. Johnson, A. M. Wojciechowski, Z. M. Williams. Frontal neurons driving competitive behaviour and ecology of social groups. Nature, vol. 603, no. 7902, pp. 661–666, 2022. DOI: https://doi.org/10.1038/s41586-021-04000-5.
N. Padilla-Coreano, K. Batra, M. Patarino, Z. X. Chen, R. R. Rock, R. H. Zhang, S. B. Hausmann, J. C. Weddington, R. Patel, Y. E. Zhang, H. S. Fang, S. Mishra, D. O. LeDuke, J. Revanna, H. Li, M. Borio, R. Pamintuan, A. Bal, L. R. Keyes, A. Libster, R. Wichmann, F. Mills, F. H. Taschbach, G. A. Matthews, J. P. Curley, I. R. Fiete, C. W. Lu, K. M. Tye. Cortical ensembles orchestrate social competition through hypothalamic outputs. Nature, vol. 603, no. 7902, pp. 667–671, 2022. DOI: https://doi.org/10.1038/s41586-022-04507-5.
C. Lyu, L. F. Abbott, G. Maimon. Building an allocentric travelling direction signal via vector computation. Nature, vol. 601, no. 7891, pp. 92–97, 2022. DOI: https://doi.org/10.1038/s41586-021-04067-0.
G. L. Bon. The Crowd: A Study of the Popular Mind, London, UK: Fischer, 1896.
D. Helbing, P. Molnár. Social force model for pedestrian dynamics. Physical Review E, vol. 51, no. 5, pp. 4282–4286, 1995. DOI: https://doi.org/10.1103/PhysRevE.51.4282.
R. L. Hughes. A continuum theory for the flow of pedestrians. Transportation Research Part B: Methodological, vol. 36, no. 6, pp. 507–535, 2002. DOI: https://doi.org/10.1016/S0191-2615(01)00015-7.
R. L. Hughes. The flow of human crowds. Annual Review of Fluid Mechanics, vol. 35, no. 1, pp. 169–182, 2003. DOI: https://doi.org/10.1146/annurev.fluid.35.101101.161136.
B. G. Silverman, M. Johns, J. Cornwell, K. O’Brien. Human behavior models for agents in simulators and games: Part I: Enabling science with PMFserv. Presence: Teleoperators and Virtual Environments, vol. 15, no. 2, pp. 139–162, 2006. DOI: https://doi.org/10.1162/pres.2006.15.2.139.
N. Bain, D. Bartolo. Dynamic response and hydrodynamics of polarized crowds. Science, vol. 363, no. 6422, pp. 46–49, 2019. DOI: https://doi.org/10.1126/science.aat9891.
A. Treuille, S. Cooper, Z. Popoviá. Continuum crowds. ACM Transactions on Graphics, vol. 25, no. 3, pp. 1160–1168, 2006. DOI: https://doi.org/10.1145/1141911.1142008.
H. Jiang, W. B. Xu, T. L. Mao, C. P. Li, S. H. Xia, Z. Q. Wang. Continuum crowd simulation in complex environments. Computers & Graphics, vol. 34, no. 5, pp. 537–544, 2010. DOI: https://doi.org/10.1016/j.cag.2010.05.013.
Q. W. Chao, P. F. Liu, Y. Han, Y. Y. Lin, C. N. Li, Q. G. Miao, X. G. Jin. A calibrated force-based model for mixed traffic simulation. IEEE Transactions on Visualization and Computer Graphics, to be published. DOI: https://doi.org/10.1109/TVCG.2021.3128286.
L. C. Jiao, L. Wang. A novel genetic algorithm based on immunity. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, vol. 30, no. 5, pp. 552–561, 2000. DOI: https://doi.org/10.1109/3468.867862.
G. Dhiman, M. Garg, A. Nagar, V. Kumar, M. Dehghani. A novel algorithm for global optimization: Rat swarm optimizer. Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 8, pp. 8457–8482, 2021. DOI: https://doi.org/10.1007/s12652-020-02580-0.
T. S. Shen. ESM: A building evacuation simulation model. Building and Environment, vol. 40, no. 5, pp. 671–680, 2005. DOI: https://doi.org/10.1016/j.buildenv.2004.08.029.
J. Tian. The physics and simulation of occupant traffic flow in the case of evacuating public building area. Transportation Research Part D: Transport and Environment, vol. 30, pp. 76–85, 2014. DOI: https://doi.org/10.1016/j.trd.2014.05.009.
N. Wagner, V. Agrawal. An agent-based simulation system for concert venue crowd evacuation modeling in the presence of a fire disaster. Expert Systems with Applications, vol. 41, no. 6, pp. 2807–2815, 2014. DOI: https://doi.org/10.1016/j.eswa.2013.10.013.
R. S. C. Lee, R. L. Hughes. Exploring trampling and crushing in a crowd. Journal of Transportation Engineering, vol. 131, no. 8, pp. 575–582, 2005. DOI: https://doi.org/10.1061/(ASCE)0733-947X(2005)131:8(575).
R. S. C. Lee, R. L. Hughes. Minimisation of the risk of trampling in a crowd. Mathematics and Computers in Simulation, vol. 74, no. 1, pp. 29–37, 2007. DOI: https://doi.org/10.1016/j.matcom.2006.06.029.
J. Ma, S. M. Lo, W. G. Song, W. L. Wang, J. Zhang, G. X. Liao. Modeling pedestrian space in complex building for efficient pedestrian traffic simulation. Automation in Construction, vol. 30, pp. 25–36, 2013. DOI: https://doi.org/10.1016/j.autcon.2012.11.032.
K. Castañeda, O. Sánchez, R. F. Herrera, E. Pellicer, H. Porras. BIM-based traffic analysis and simulation at road intersection design. Automation in Construction, vol. 131, Article number 103911, 2021. DOI: https://doi.org/10.1016/j.autcon.2021.103911.
S. P. Zhou, D. Chen, W. T. Cai, L. B. Luo, M. Y. H. Low, F. Tian, V. S. H. Tay, D. W. S. Ong. Crowd modeling and simulation technologies. ACM Transactions on Modeling and Computer Simulation, vol. 20, no. 4, Article number 20, 2010. DOI: https://doi.org/10.1145/1842722.1842725.
Y. Yuan, Z. K. Wu, M. Q. Zhou. Populating virtual environments with crowd patches and rule-based method. In Proceedings of the 11th ACM SIGGRAPH International Conference on Virtual-reality Continuum and its Applications in Industry, Singapore, pp. 239–246, 2012. DOI: https://doi.org/10.1145/2407516.2407574.
Q. W. Chao, Z. G. Deng, X. G. Jin. Vehicle-pedestrian interaction for mixed traffic simulation. Computer Animation and Virtual Worlds, vol. 26, no. 3–4, pp. 405–412, 2015. DOI: https://doi.org/10.1002/cav.1654.
T. Toffoli, N. Margolus. Cellular Automata Machines: A New Environment for Modeling. Cambridge, USA: MIT Press, 1987.
K. Nagel, M. Schreckenberg. A cellular automaton model for freeway traffic. Journal de Physique, vol. 2, no. 12, pp. 2221–2229, 1992. DOI: https://doi.org/10.1051/jp1:1992277.
K. Nagel, C. L. Barrett, M. Rickert. Parallel Traffic Micro-simulation by Cellular Automata and Application for Large Scale Transportation Modeling. Technical Report, Federal Highway Administration, Department of Transportation, USA. [Online], Available: https://rosap.nti.bts.gov/view/dot/57245, 1996.
M. Rickert, K. Nagel, M. Schreckenberg, A. Latour. Two lane traffic simulations using cellular automata. Physica A: Statistical Mechanics and its Applications, vol. 231, no. 4, pp. 534–550, 1996. DOI: https://doi.org/10.1016/0378-4371(95)00442-4.
V. J. Blue, J. L. Adler. Emergent fundamental pedestrian flows from cellular automata microsimulation. Transportation Research Record, vol. 1644, no. 1, pp. 29–36, 1998. DOI: https://doi.org/10.3141/1644-04.
V. J. Blue, J. L. Adler. Cellular automata microsimulation for modeling bi-directional pedestrian walkways. Transportation Research Part B: Methodological, vol. 35, no. 3, pp. 293–312, 2001. DOI: https://doi.org/10.1016/s0191-2615(99)00052-1.
C. Burstedde, K. Klauck, A. Schadschneider, J. Zittartz. Simulation of pedestrian dynamics using a two-dimensional cellular automaton. Physica A: Statistical Mechanics and Its Applications, vol. 295, no. 3–4, pp. 507–525, 2001. DOI: https://doi.org/10.1016/S0378-4371(01)00141-8.
R. F. Zhao, Y. Zhai, L. Qu, R. H. Wang, Y. Y. Huang, Q. Dong. A continuous floor field cellular automata model with interaction area for crowd evacuation. Physica A: Statistical Mechanics and Its Applications, vol. 575, Article number 126049, 2021. DOI: https://doi.org/10.1016/j.physa.2021.126049.
L. L. Lu, C. Y. Chan, J. Wang, W. Wang. A study of pedestrian group behaviors in crowd evacuation based on an extended floor field cellular automaton model. Transportation Research Part C: Emerging Technologies, vol. 81, pp. 317–329, 2017. DOI: https://doi.org/10.1016/j.trc.2016.08.018.
A. Kirchner, A. Schadschneider. Simulation of evacuation processes using a bionics-inspired cellular automaton model for pedestrian dynamics. Physica A: Statistical Mechanics and Its Applications, vol. 312, no. 1–2, pp. 260–276, 2002. DOI: https://doi.org/10.1016/s0378-4371(02)00857-9.
M. Muramatsu, T. Irie, T. Nagatani. Jamming transition in pedestrian counter flow. Physica A: Statistical Mechanics and Its Applications, vol. 267, no. 3–4, pp. 487–198, 1999. DOI: https://doi.org/10.1016/S0378-4371(99)00018-7.
M. Muramatsu, T. Nagatani. Jamming transition in two-dimensional pedestrian traffic. Physica A: Statistical Mechanics and its Applications, vol. 275, no. 1–2, pp. 281–291, 2000. DOI: https://doi.org/10.1016/S0378-4371(99)00447-1.
X. Ruan, J. Y. Zhou, H. Z. Tu, Z. R. Jin, X. F. Shi. An improved cellular automaton with axis information for microscopic traffic simulation. Transportation Research Part C: Emerging Technologies, vol. 78, pp. 63–77, 2017. DOI: https://doi.org/10.1016/j.trc.2017.02.023.
M. Y. Liu, J. Shi. A cellular automata traffic flow model combined with a BP neural network based microscopic lane changing decision model. Journal of Intelligent Transportation Systems, vol. 23, no. 4, pp. 309–318, 2019. DOI: https://doi.org/10.1080/15472450.2018.1462176.
J. H. Wang, W. Lv, Y. J. Jiang, S. S. Qin, J. W. Li. A multi-agent based cellular automata model for intersection traffic control simulation. Physica A: Statistical Mechanics and Its Applications, vol. 584, Article number 126356, 2021. DOI: https://doi.org/10.1016/j.physa.2021.126356.
A. Kirchner, H. Klüpfel, K. Nishinari, A. Schadschneider, M. Schreckenberg. Simulation of competitive egress behavior: Comparison with aircraft evacuation data. Physica A: Statistical Mechanics and its Applications, vol. 324, no. 3–4, pp. 689–697, 2003. DOI: https://doi.org/10.1016/s0378-4371(03)00076-1.
W. G. Weng, T. Chen, H. Y. Yuan, W. C. Fan. Cellular automaton simulation of pedestrian counter flow with different walk velocities. Physical Review E, vol. 74, no. 3, Article number 036102, 2006. DOI: https://doi.org/10.1103/PhysRevE.74.036102.
K. Yamamoto, S. Kokubo, K. Nishinari. Simulation for pedestrian dynamics by real-coded cellular automata (RCA). Physica A: Statistical Mechanics and its Applications, vol. 379, no. 2, pp. 654–660, 2007. DOI: https://doi.org/10.1016/j.physa.2007.02.040.
Y. W. Fu, J. H. Liang, Q. P. Liu, X. Q. Hu. Crowd simulation for evacuation behaviors based on multi-agent system and cellular automaton. In Proceedings of International Conference on Virtual Reality and Visualization, IEEE, Shenyang, China, pp. 103–109, 2014. DOI: https://doi.org/10.1109/ICVRV.2014.52.
T. I. Lakoba, D. J. Kaup, N. M. Finkelstein. Modifications of the helbing-molnar-farkas-vicsek social force model for pedestrian evolution. Simulation, vol. 81, no. 5, pp. 339–352, 2005. DOI: https://doi.org/10.1177/0037549705052772.
W. J. Yu, R. Chen, L. Y. Dong, S. Q. Dai. Centrifugal force model for pedestrian dynamics. Physical Review E, vol. 72, no. 2, Article number 026112, 2005. DOI: https://doi.org/10.1103/PhysRevE.72.026112.
M. Chraibi, U. Kemloh, A. Schadschneider, A. Seyfried. Force-based models of pedestrian dynamics. Networks and Heterogeneous Media, vol. 6, no. 3, pp. 425–442, 2011. DOI: https://doi.org/10.3934/nhm.2011.6.425.
M. Chraibi, A. Seyfried, A. Schadschneider. Generalized centrifugal-force model for pedestrian dynamics. Physical Review E, vol. 82, no. 4, Article number 046111, 2010. DOI: https://doi.org/10.1103/PhysRevE.82.046111.
F. Pascucci, N. Rinke, C. Schiermeyer, B. Friedrich, V. Berkhahn. Modeling of shared space with multi-modal traffic using a multi-layer social force approach. Transportation Research Procedia, vol. 10, pp. 316–326, 2015. DOI: https://doi.org/10.1016/j.trpro.2015.09.081.
L. Huang, J. P. Wu, F. You, Z. H. Lv, H. B. Song. Cyclist social force model at unsignalized intersections with heterogeneous traffic. IEEE Transactions on Industrial Informatics, vol. 13, no. 2, pp. 782–792, 2017. DOI: https://doi.org/10.1109/TII.2016.2597744.
Y. Q. Jiang, B. K. Chen, X. Li, Z. J. Ding. Dynamic navigation field in the social force model for pedestrian evacuation. Applied Mathematical Modelling, vol. 80, pp. 815–826, 2020. DOI: https://doi.org/10.1016/j.apm.2019.10.016.
N. Pelechano, J. M. Allbeck, N. I. Badler. Controlling individual agents in high-density crowd simulation. In Proceedings of ACM SIGGRAPH/Eurographics Symposium on Computer Animation, San Diego, USA, pp. 99–108, 2007.
W. H. Wu, M. Y. Chen, J. H. Li, B. L. Liu, X. P. Zheng. An extended social force model via pedestrian heterogeneity affecting the self-driven force. IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 7, pp. 7974–7986, 2022. DOI: https://doi.org/10.1109/T1TS.2021.3074914.
M. R. C. McDowell. Kinetic theory of vehicular traffic. Journal of the Operational Research Society, vol. 23, no. 4, pp. 599–600, 1972. DOI: https://doi.org/10.1057/jors.1972.98.
D. Helbing. A fluid dynamic model for the movement of pedestrians, [Online], Available: https://arxiv.org/abs/cond-mat/9805213, 1998.
M. J. Lighthill, G. B. Whitham. On kinematic waves II. A theory of traffic flow on long crowded roads. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 229, no. 1178, pp. 317–345, 1955. DOI: https://doi.org/10.1098/rspa.1955.0089.
S. Chenney. Flow tiles. In Proceedings of ACM SIGGRAPH/Eurographics symposium on Computer animation, Grenoble, France, pp. 233–242, 2004. DOI: https://doi.org/10.1145/1028523.1028553.
V. Coscia, C. Canavesio. First-order macroscopic modelling of human crowd dynamics. Mathematical Models and Methods in Applied Sciences, vol. 18, no. S1, pp. 1217–1247, 2008. DOI: https://doi.org/10.1142/S0218202508003017.
R. Narain, A. Golas, S. Curtis, M. C. Lin. Aggregate dynamics for dense crowd simulation. In Proceedings of ACM SIGGRAPH Asia, Yokohama, Japan, Article number 122, 2009. DOI: https://doi.org/10.1145/1661412.1618468.
N. Bellomo, S. Berrone, L. Gibelli, A. Pieri. First order models of human crowds with behavioral-social dynamics, [Online], Available: https://doi.org/10.48550/arXiv.1501.02828, 2015.
R. Mohan, G. Ramadurai. Heterogeneous traffic flow modelling using second-order macroscopic continuum model. Physics Letters A, vol. 381, no. 3, pp. 115–123, 2017. DOI: https://doi.org/10.1016/j.physleta.2016.10.042.
H. Y. Liang, J. Du, S. C. Wong. A Continuum model for pedestrian flow with explicit consideration of crowd force and panic effects. Transportation Research Part B: Methodological, vol. 149, pp. 100–117, 2021. DOI: https://doi.org/10.1016/j.trb.2021.05.006.
O. Khatib. Real-time obstacle avoidance for manipulators and mobile robots. The International Journal of Robotics Research, vol. 5, no. 1, pp. 90–98, 1986. DOI: https://doi.org/10.1177/027836498600500106.
Y. Q. Chen, T. X. Li. Collision avoidance of unmanned ships based on artificial potential field. In Proceedings of Chinese Automation Congress, IEEE, Jinan, China, pp. 4437–4440, 2017. DOI: https://doi.org/10.1109/CAC.2017.8243561.
H. I. Lin, M. F. Hsieh. Robotic arm path planning based on three-dimensional artificial potential field. In Proceedings of the 18th International Conference on Control, Automation and Systems, IEEE, PyeongChang, Republic of Korea, pp. 740–745, 2018.
J. Song, C. Hao, J. C. Su. Path planning for unmanned surface vehicle based on predictive artificial potential field. International Journal of Advanced Robotic Systems, vol. 17, no. 2, pp. 1–13, 2020. DOI: https://doi.org/10.1177/1729881420918461.
Z. Y. Chen, L. Ma, Z. J. Shao. Path planning for obstacle avoidance of manipulators based on improved artificial potential field. In Proceedings of Chinese Automation Congress, IEEE, Hangzhou, China, pp. 2991–2996, 2019. DOI: https://doi.org/10.1109/CAC48633.2019.8996467.
Y. B. Zheng, B. Li, D. Y. An, N. Li. A multi-agent path planning algorithm based on hierarchical reinforcement learning and artificial potential field. In Proceedings of the 11th International Conference on Natural Computation, IEEE, Zhangjiajie, China, pp. 363–369, 2015. DOI: https://doi.org/10.1109/ICNC.2015.7378017.
Y. Noguchi, T. Maki. Path planning method based on artificial potential field and reinforcement learning for intervention AUVs. In Proceedings of IEEE Underwater Technology, Kaohsiung, China, 2019. DOI: https://doi.org/10.1109/UT.2019.8734314.
H. X. Li, D. X. Gong, J. J. Yu. An obstacles avoidance method for serial manipulator based on reinforcement learning and artificial potential field. International Journal of Intelligent Robotics and Applications, vol. 5, no. 2, pp. 186–202, 2021. DOI: https://doi.org/10.1007/s41315-021-00172-5.
H. Wang, Y. F. He, Z. Wang, G. F. Li, Y. Q. Xiao, M. L. Xu. Data-driven online traffic reconstructions: Interactively optimizing in virtual reality. Computers & Graphics, vol. 105, pp. 85–93, 2022. DOI: https://doi.org/10.1016/j.cag.2022.03.012.
A. Lerner, Y. Chrysanthou, D. Lischinski. Crowds by example. Computer Graphics Forum, vol. 26, no. 3, pp. 655–664, 2007. DOI: https://doi.org/10.1111/j.1467-8659.2007.01089.x.
K. H. Lee, M. G. Choi, Q. Hong, J. Lee. Group behavior from video: A data-driven approach to crowd simulation. In Proceedings of ACM SIGGRAPH/Eurographics Symposium on Computer Animation, San Diego, USA, pp. 109–118, 2007.
A. Lerner, E. Fitusi, Y. Chrysanthou, D. Cohen-Or. Fitting behaviors to pedestrian simulations. In Proceedings of ACM SIGGRAPH/Eurographics Symposium on Computer Animation, New Orleans, USA, pp. 199–208, 2009. DOI: https://doi.org/10.1145/1599470.1599496.
X. Wang, R. Jiang, L. Li, Y. L. Lin, X. H. Zheng, F. Y. Wang. Capturing car-following behaviors by deep learning. IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 3, pp. 910–920, 2018. DOI: https://doi.org/10.1109/TITS.2017.2706963.
H. K. Bi, T. L. Mao, Z. Q. Wang, Z. G. Deng. A deep learning-based framework for intersectional traffic simulation and editing. IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 7, pp. 2335–2348, 2020. DOI: https://doi.org/10.1109/TVCG.2018.2889834.
X. Wei, W. Lu, L. L. Zhu, W. W. Xing. Learning motion rules from real data: Neural network for crowd simulation. Neurocomputing, vol. 310, pp. 125–134, 2018. DOI: https://doi.org/10.1016/j.neucom.2018.05.022.
Z. Z. Yao, G. J. Zhang, D. J. Lu, H. Liu. Learning crowd behavior from real data: A residual network method for crowd simulation. Neurocomputing, vol. 404, pp. 173–185, 2020. DOI: https://doi.org/10.1016/j.neucom.2020.04.141.
X. D. Zhao, L. Xia, J. Zhang, W. G. Song. Artificial neural network based modeling on unidirectional and bidirectional pedestrian flow at straight corridors. Physica A: Statistical Mechanics and Its Applications, vol. 547, Article number 123825, 2020. DOI: https://doi.org/10.1016/j.physa.2019.123825.
D. F. Xie, Z. Z. Fang, B. Jia, Z. B. He. A data-driven lane-changing model based on deep learning. Transportation Research Part C: Emerging Technologies, vol. 106, pp. 41–60, 2019. DOI: https://doi.org/10.1016/j.trc.2019.07.002.
H. K. Bi, T. L. Mao, Z. Q. Wang, Z. G. Deng. A data-driven model for lane-changing in traffic simulation. In Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Zurich, Switzerland, pp. 149–158, 2016.
X. Song, K. Chen, X. Li, J. H. Sun, B. C. Hou, Y. Cui, B. C. Zhang, G. Xiong, Z. L. Wang. Pedestrian trajectory prediction based on deep convolutional LSTM network. IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 6, pp. 3285–3302, 2021. DOI: https://doi.org/10.1109/TITS.2020.2981118.
X. D. Zhao, J. Zhang, W. G. Song. A Radar-Nearest-Neighbor based data-driven approach for crowd simulation. Transportation Research Part C: Emerging Technologies, vol. 129, Article number 103260, 2021. DOI: https://doi.org/10.1016/j.trc.2021.103260.
X. Song, D. L. Han, J. H. Sun, Z. H. Zhang. A data-driven neural network approach to simulate pedestrian movement. Physica A: Statistical Mechanics and its Applications, vol. 509, pp. 827–844, 2018. DOI: https://doi.org/10.1016/j.physa.2018.06.045.
K. Tkachuk, X. Song, I. Maltseva. Application of artificial neural networks for agent-based simulation of emergency evacuation from buildings for various purpose. IOP Conference Series: Materials Science and Engineering, vol. 365, no. 4, Article number 042064, 2018. DOI: https://doi.org/10.1088/1757-899X/365/4/042064.
N. Courty, T. Corpetti. Crowd motion capture. Computer Animation and Virtual Worlds, vol. 18, no. 4–5, pp. 361–370, 2007. DOI: https://doi.org/10.1002/cav.199.
S. R. Musse, C. R. Jung, J. C. Jr. Jacques, A. Braun. Using computer vision to simulate the motion of virtual agents. Computer Animation and Virtual Worlds, vol. 18, no. 2, pp. 83–93, 2007. DOI: https://doi.org/10.1002/cav.163.
J. H. Zhong, W. T. Cai, L. B. Luo, H. Y. Yin. Learning behavior patterns from video: A data-driven framework for agent-based crowd modeling. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, Istanbul, Turkey, pp. 801–809, 2015.
J. H. Zhong, W. T. Cai, M. Lees, L. B. Luo. Automatic model construction for the behavior of human crowds. Applied Soft Computing, vol. 56, pp. 368–378, 2017. DOI: https://doi.org/10.1016/j.asoc.2017.03.020.
S. Patil, J. Van Den Berg, S. Curtis, M. C. Lin, D. Manocha. Directing crowd simulations using navigation fields. IEEE Transactions on Visualization and Computer Graphics, vol. 17, no. 2, pp. 244–254, 2011. DOI: https://doi.org/10.1109/TVCG.2010.33.
M. Hu, S. Ali, M. Shah. Learning motion patterns in crowded scenes using motion flow field. In Proceedings of the 19th International Conference on Pattern Recognition, IEEE, Tampa, USA, 2008. DOI: https://doi.org/10.1109/ICPR.2008.4761183.
X. G. Jin, J. Y. Xu, C. C. L. Wang, S. S. Huang, J. Zhang. Interactive control of large-crowd navigation in virtual environments using vector fields. IEEE Computer Graphics and Applications, vol. 28, no. 6, pp. 37–46, 2008. DOI: https://doi.org/10.1109/MCG.2008.117.
S. Wu, H. Yang, S. B. Zheng, H. Su, Y. W. Fan, M. H. Yang. Crowd behavior analysis via curl and divergence of motion trajectories. International Journal of Computer Vision, vol. 123, no. 3, pp. 499–519, 2017. DOI: https://doi.org/10.1007/s11263-017-1005-y.
N. Nayan, S. S. Sahu, S. Kumar. Detecting anomalous crowd behavior using correlation analysis of optical flow. Signal, Image and Video Processing, vol. 13, no. 6, pp. 1233–1241, 2019. DOI: https://doi.org/10.1007/s11760-019-01474-9.
S. D. Khan. Congestion detection in pedestrian crowds using oscillation in motion trajectories. Engineering Applications of Artificial Intelligence, vol. 85, pp. 429–443, 2019. DOI: https://doi.org/10.1016/j.engappai.2019.07.009.
D. H. Lin, E. Grimson, J. Fisher. Learning visual flows: A Lie algebraic approach. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, pp. 747–754, 2009. DOI: https://doi.org/10.1109/CVPR.2009.5206660.
D. H. Lin, E. Grimson, J. Fisher. Modeling and estimating persistent motion with geometric flows. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, USA, pp. 1783–1790, 2010. DOI: https://doi.org/10.1109/CVPR.2010.5539848.
Y. W. Fan, H. Yang, S. B. Zheng, H. Su. Geometric motion flow (GMF): A new feature for traffic surveillance. In Proceedings of the 6th International Conference on Image and Graphics, IEEE, Hefei, China, pp. 726–730, 2011. DOI: https://doi.org/10.1109/ICIG.2011.56.
C. Rudloff, D. Bauer, T. Matyus, S. Seer. Mind the gap: Boarding and alighting processes using the social force paradigm calibrated on experimental data. In Proceedings of the 14th International IEEE Conference on Intelligent Transportation Systems, Washington DC, USA, pp. 353–358, 2011. DOI: https://doi.org/10.1109/ITSC.2011.6083077.
S. Seer, N. Brändle, C. Ratti. Kinects and human kinetics: A new approach for studying crowd behavior, [Online], Available: https://arxiv.org/abs/1210.2838, 2012.
M. Tang, H. F. Jia. An approach for calibration and validation of the social force pedestrian model. In Proceedings of International Conference on Transportation, Mechanical, and Electrical Engineering, IEEE, Changchun, China, pp. 2026–2031, 2011. DOI: https://doi.org/10.1109/TMEE.2011.6199614.
M. Ko, T. Kim, K. Sohn. Calibrating a social-force-based pedestrian walking model based on maximum likelihood estimation. Transportation, vol. 40, no. 1, pp. 91–107, 2013. DOI: https://doi.org/10.1007/s11116-012-9411-z.
S. Seer, C. Rudloff, T. Matyus, N. Brändle. Validating social force based models with comprehensive real world motion data. Transportation Research Procedia, vol. 2, pp. 724–732, 2014. DOI: https://doi.org/10.1016/j.trpro.2014.09.080.
M. X. Liu, W. L. Zeng, P. Chen, X. Y. Wu. A microscopic simulation model for pedestrian-pedestrian and pedestrian-vehicle interactions at crosswalks. PLoS One, vol. 12, no. 7, Article number e0180992, 2017. DOI: https://doi.org/10.1371/journal.pone.0180992.
R. Lovreglio, C. Dias, X. Song, L. Ballerini. Towards microscopic calibration of pedestrian simulation models using open trajectory datasets: The case study of the edinburgh informatics forum. In Proceedings of International Conference on Traffic and Granular Flow, Springer, Washington DC, USA, pp. 215–223, 2017. DOI: https://doi.org/10.1007/978-3-030-11440-4_25.
S. Lemercier, A. Jelic, R. Kulpa, J. Hua, J. Fehrenbach, P. Degond, C. Appert-Rolland, S. Donikian, J. Pettré. Realistic following behaviors for crowd simulation. Computer Graphics Forum, vol. 31, no. 2, pp. 489–498, 2012. DOI: https://doi.org/10.1111/j.1467-8659.2012.03028.x.
B. Anvari, M. G. H. Bell, A. Sivakumar, W. Y. Ochieng. Modelling shared space users via rule-based social force model. Transportation Research Part C: Emerging Technologies, vol. 51, pp. 83–103, 2015. DOI: https://doi.org/10.1016/j.trc.2014.10.012.
W. L. Zeng, P. Chen, G. Z. Yu, Y. P. Wang. Specification and calibration of a microscopic model for pedestrian dynamic simulation at signalized intersections: A hybrid approach. Transportation Research Part C: Emerging Technologies, vol. 80, pp. 37–70, 2017. DOI: https://doi.org/10.1016/j.trc.2017.04.009.
M. Hussein, T. Sayed. Validation of an agent-based microscopic pedestrian simulation model in a crowded pedestrian walking environment. Transportation Planning and Technology, vol. 42, no. 1, pp. 1–22, 2019. DOI: https://doi.org/10.1080/03081060.2018.1541279.
N. Bode. Parameter calibration in crowd simulation models using approximate Bayesian computation, [Online], Available: https://arxiv.org/abs/2001.10330, 2020.
P. F. Liu, Q. W. Chao, H. W. Huang, Q. Y. Wang, Z. Y. Zhao, Q. Peng, M. K. Yip, E. S. Liu, X. G. Jin. Velocity-based dynamic crowd simulation by data-driven optimization. The Visual Computer, vol. 38, no. 9, pp. 3499–3512, 2022. DOI: https://doi.org/10.1007/s00371-022-02556-5.
L. Torrey. Crowd simulation via multi-agent reinforcement learning. In Proceedings of the 6th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, Stanford, USA, pp. 89–94, 2010.
Y. Kato, K. Kamiyama, K. Morioka. Autonomous robot navigation system with learning based on deep Q-network and topological maps. In Proceedings of IEEE/SICE International Symposium on System Integration, IEEE, Taipei, China, pp. 1040–1046, 2017. DOI: https://doi.org/10.1109/SII.2017.8279360.
S. M. Feng, H. L. Ren, X. R. Wang, P. Ben-Tzvi. Mobile robot obstacle avoidance based on deep reinforcement learning. In Proceedings of ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, Anaheim, USA, 2019. DOI: https://doi.org/10.1115/DETC2019-97536.
J. W. Zhang, J. T. Springenberg, J. Boedecker, W. Burgard. Deep reinforcement learning with successor features for navigation across similar environments. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Vancouver, Canada, pp. 2371–2378, 2017. DOI: https://doi.org/10.1109/IROS.2017.8206049.
S. H. Hsu, S. H. Chan, P. T. Wu, K. Xiao, L. C. Fu. Distributed deep reinforcement learning based indoor visual navigation. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Madrid, Spain, pp. 2532–2537, 2018. DOI: https://doi.org/10.1109/IROS.2018.8594352.
C. Wang, J. Wang, X. D. Zhang, X. Zhang. Autonomous navigation of UAV in large-scale unknown complex environment with deep reinforcement learning. In Proceedings of IEEE Global Conference on Signal and Information Processing, Montreal, Canada, pp. 858–862, 2017. DOI: https://doi.org/10.1109/GlobalSIP.2017.8309082.
C. Wang, J. Wang, Y. Shen, X. D. Zhang. Autonomous navigation of UAVs in large-scale complex environments: A deep reinforcement learning approach. IEEE Transactions on Vehicular Technology, vol. 68, no. 3, pp. 2124–2136, 2019. DOI: https://doi.org/10.1109/TVT.2018.2890773.
C. Wang, J. Wang, J. J. Wang, X. D. Zhang. Deep-reinforcement-learning-based autonomous UAV navigation with sparse rewards. IEEE Internet of Things Journal, vol. 7, no.7, pp. 6180–6190, 2020. DOI: https://doi.org/10.1109/JIOT.2020.2973193.
D. Xu, X. Huang, Z. L. Li, X. Li. Local motion simulation using deep reinforcement learning. Transactions in GIS, vol. 24, no. 3, pp. 756–779, 2020. DOI: https://doi.org/10.1111/tgis.12620.
D. Xu, X. Huang, J. Mango, X. Li, Z. L. Li. Simulating multi-exit evacuation using deep reinforcement learning. Transactions in GIS, vol. 25, no. 3, pp. 1542–1564, 2021. DOI: 10.1111/tgis.2738.
R. J. Zhu, S. N. Wu, L. L. Li, P. Lv, M. L. Xu. Context-aware multiagent broad reinforcement learning for mixed pedestrian-vehicle adaptive traffic light control. IEEE Internet of Things Journal, vol. 9, no. 20, pp. 19694–19705, 2022. DOI: https://doi.org/10.1109/JIOT.2022.3167029.
Z. Zhang, D. J. Lu, J. L. Y. Li, P. S. Liu, G. J. Zhang. Crowd evacuation simulation using hierarchical deep reinforcement learning. In Proceedings of the 24th IEEE International Conference on Computer Supported Cooperative Work in Design, Dalian, China, pp. 563–568, 2021. DOI: https://doi.org/10.1109/CSCWD49262.2021.9437632.
D. Zhang, W. H. Li, J. H. Gong, L. Huang, G. Y. Zhang, S. Shen, J. T. Liu, H. N. Ma. HDRLM3D: A deep reinforcement learning-based model with human-like perceptron and policy for crowd evacuation in 3D environments. ISPRS International Journal of Geo-information, vol. 11, no.4, Article number 255, 2022. DOI: https://doi.org/10.3390/ijgi11040255.
A. D. Dragan, S. S. Srinivasa. A policy-blending formalism for shared control. The International Journal of Robotics Research, vol. 32, no. 7, pp. 790–805, 2013. DOI: https://doi.org/10.1177/0278364913490324.
A. Broad, T. Murphey, B. Argall. Operation and imitation under safety-aware shared control. In Proceedings of the 13th International Workshop on the Algorithmic Foundations of Robotics, Springer, Mérida, Mexico, pp. 905–920, 2018. DOI: https://doi.org/10.1007/978-3-030-44051-0_52.
R. C. Simpson, S. P. Levine. Automatic adaptation in the NavChair assistive wheelchair navigation system. IEEE Transactions on Rehabilitation Engineering, vol. 7, no. 4, pp. 452–463, 1999. DOI: https://doi.org/10.1109/86.808949.
B. J. F. Driessen, T. K. T. Kate, F. Liefhebber, A. H. G. Versluis, J. A. Van Woerden. Collaborative control of the manus manipulator. Universal Access in the Information Society, vol. 4, no. 2, pp. 165–173, 2005. DOI: https://doi.org/10.1007/s10209-005-0121-6.
Y. Xing, C. Lv, D. P. Cao, P. Hang. Toward human-vehicle collaboration: Review and perspectives on human-centered collaborative automated driving. Transportation Research Part C: Emerging Technologies, vol. 128, Article number 103199, 2021. DOI: https://doi.org/10.1016/j.trc.2021.103199.
N. Jansen, M. Cubuktepe, U. Topcu. Synthesis of shared control protocols with provable safety and performance guarantees. In Proceedings of American Control Conference, IEEE, Seattle, USA, pp. 1866–1873, 2017. DOI: https://doi.org/10.23919/ACC.2017.7963224.
D. Gopinath, S. Jain, B. D. Argall. Human-in-the-loop optimization of shared autonomy in assistive robotics. IEEE Robotics and Automation Letters, vol. 2, no. 1, pp. 247–254, 2017. DOI: https://doi.org/10.1109/LRA.2016.2593928.
Y. Oh, S. W. Wu, M. Toussaint, J. Mainprice. Natural gradient shared control. In Proceedings of the 29th IEEE International Conference on Robot and Human Interactive Communication, Naples, Italy, pp. 1223–1229, 2020. DOI: https://doi.org/10.1109/RO-MAN47096.2020.9223465.
W. X. Xu, J. Huang, Y. J. Wang, C. J. Tao, L. Cheng. Reinforcement learning-based shared control for walking-aid robot and its experimental verification. Advanced Robotics, vol. 29, no. 22, pp. 1463–1481, 2015. DOI: https://doi.org/10.1080/01691864.2015.1070748.
S. Reddy, A. D. Dragan, S. Levine. Shared autonomy via deep reinforcement learning. In Proceedings of the 14th Robotics: Science and Systems, Pittsburgh, USA, 2018.
C. B. Schaff, M. R. Walter. Residual policy learning for shared autonomy. In Proceedings of the 14th Robotics: Science and Systems, Corvalis, USA, 2020.
Y. Oh, M. Toussaint, J. Mainprice. Learning to arbitrate human and robot control using disagreement between sub-policies. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Prague, Czech Republic, pp. 5305–5311, 2021. DOI: https://doi.org/10.1109/IROS51168.2021.9636049.
K. Hauser. Recognition, prediction, and planning for assisted teleoperation of freeform tasks. Autonomous Robots, vol. 35, no. 4, pp. 241–254, 2013. DOI: https://doi.org/10.1007/s10514-013-9350-3.
S. Javdani, H. Admoni, S. Pellegrinelli, S. S. Srinivasa, J. A. Bagnell. Shared autonomy via hindsight optimization for teleoperation and teaming. The International Journal of Robotics Research, vol. 37, no. 7, pp. 717–742, 2018. DOI: https://doi.org/10.1177/0278364918776060.
S. Noh. Decision-making framework for autonomous driving at road intersections: Safeguarding against collision, overly conservative behavior, and violation vehicles. IEEE Transactions on Industrial Electronics, vol. 66, no. 4, pp. 3275–3286, 2019. DOI: https://doi.org/10.1109/TIE.2018.2840530.
C. Xu, W. Z. Zhao, C. Y. Wang. An integrated threat assessment algorithm for decision-making of autonomous driving vehicles. IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 6, pp. 2510–2521, 2020. DOI: https://doi.org/10.1109/TITS.2019.2919865.
J. H. Kim, D. S. Kum. Threat prediction algorithm based on local path candidates and surrounding vehicle trajectory predictions for automated driving vehicles. In Proceedings of IEEE Intelligent Vehicles Symposium, IEEE, Seoul, Republic of Korea, pp. 1220–1225, 2015. DOI: https://doi.org/10.1109/IVS.2015.7225849.
M. Bošnak, I. Škrjanc. Efficient time-to-collision estimation for a braking supervision system with LIDAR. In Proceedings of the 3rd IEEE International Conference on Cybernetics, Exeter, UK, 2017. DOI: https://doi.org/10.1109/CYBConf.2017.7985775.
S. Noh, K. An. Decision-making framework for automated driving in highway environments. IEEE Transactions on Intelligent Transportation Systems, vol. 19, no. 1, pp. 58–71, 2018. DOI: https://doi.org/10.1109/TITS.2017.2691346.
M. Y. Yu, R. Vasudevan, M. Johnson-Roberson. Occlusion-aware risk assessment for autonomous driving in urban environments. IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 2235–2241, 2019. DOI: https://doi.org/10.1109/LRA.2019.2900453.
D. Shin, B. Kim, K. Yi, A. Carvalho, F. Borrelli. Human-centered risk assessment of an automated vehicle using vehicular wireless communication. IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 2, pp. 667–681, 2019. DOI: https://doi.org/10.1109/TITS.2018.2823744.
G. F. Li, Y. F. Yang, T. R. Zhang, X. D. Qu, D. P. Cao, B. Cheng, K. Q. Li. Risk assessment based collision avoidance decision-making for autonomous vehicles in multi-scenarios. Transportation Research Part C: Emerging Technologies, vol. 122, Article number 102820, 2021. DOI: https://doi.org/10.1016/j.trc.2020.102820.
D. Shen, Y. B. Chen, L. X. Li, S. Chien. Collision-free path planning for automated vehicles risk assessment via predictive occupancy map. In Proceedings of IEEE Intelligent Vehicles Symposium, Las Vegas, USA, pp. 985–991, 2020. DOI: https://doi.org/10.1109/IV47402.2020.9304720.
D. Deveaux, T. Higuchi, S. Uçar, C. H. Wang, J. Härri, O. Altintas. Extraction of risk knowledge from time to collision variation in roundabouts. In Proceedings of IEEE International Intelligent Transportation Systems Conference, Indianapolis, USA, pp. 3665–3672, 2021. DOI: https://doi.org/10.1109/ITSC48978.2021.9564570.
J. Y. Zhu, Y. L. Ma, Y. N. Lou. Multi-vehicle interaction safety of connected automated vehicles in merging area: A real-time risk assessment approach. Accident Analysis & Prevention, vol. 166, Article number 106546, 2022. DOI: https://doi.org/10.1016/j.aap.2021.106546.
X. Li, X. P. Liu. Embedding sustainable development strategies in agent-based models for use as a planning tool. International Journal of Geographical Information Science, vol. 22, no. 1, pp. 21–45, 2008. DOI: https://doi.org/10.1080/13658810701228686.
G. D. P. A. Aschwanden, S. Haegler, F. Bosché, L. Van Gool, G. Schmitt. Empiric design evaluation in urban planning. Automation in Construction, vol. 20, no. 3, pp. 299–310, 2011. DOI: https://doi.org/10.1016/j.autcon.2010.10.007.
C. D. T. Mathew, P. R. Knob, S. R. Musse, D. G. Aliaga. Urban walkability design using virtual population simulation. Computer Graphics Forum, vol. 38, no. 1, pp. 455–469, 2019. DOI: https://doi.org/10.1111/cgf.13585.
T. Feng, L. F. Yu, S. K. Yeung, K. K. Yin, K. Zhou. Crowd-driven mid-scale layout design. ACM Transactions on Graphics, vol. 35, no. 4, Article number 132, 2016. DOI: https://doi.org/10.1145/2897824.2925894.
N. Khamis, H. Selamat, F. S. Ismail, O. F. Lutfy. Optimal exit configuration of factory layout for a safer emergency evacuation using crowd simulation model and multi-objective artificial bee colony optimization. International Journal of Integrated Engineering, vol. 11, no. 4, pp. 183–191, 2019.
I. Garcia-Dorado, D. G. Aliaga, S. V. Ukkusuri. Designing large-scale interactive traffic animations for urban modeling. Computer Graphics Forum, vol. 33, no. 2, pp. 411–420, 2014. DOI: https://doi.org/10.1111/cgf.12329.
B. Haworth, M. Usman, G. Berseth, M. Khayatkhoei, M. Kapadia, P. Faloutsos. CODE: Crowd-optimized design of environments. Computer Animation and Virtual Worlds, vol. 28, no. 6, Article number e1749, 2017. DOI: https://doi.org/10.1002/cav.1749.
N. Chakraborty, B. Haworth, M. Usman, G. Berseth, P. Faloutsos, M. Kapadia. Crowd sourced co-design of floor plans using simulation guided games. In Proceedings of the 10th International Conference on Motion in Games, ACM, Barcelona, Spain, Article number 1, 2017. DOI: https://doi.org/10.1145/3136457.3136463.
M. L. Xu, Y. P. Wu, P. Lv, H. Jiang, M. X. Luo, Y. D. Ye. miSFM: On combination of Mutual Information and Social Force Model towards simulating crowd evacuation. Neurocomputing, vol. 168, pp. 529–537, 2015. DOI: https://doi.org/10.1016/j.neucom.2015.05.074.
J. Tsai, N. Fridman, E. Bowring, M. Brown, S. Epstein, G. A. Kaminka, S. Marsella, A. Ogden, I. Rika, A. Sheel, M. E. Taylor, X. Z. Wang, A. Zilka, M. Tambe. ESCAPES: Evacuation simulation with children, authorities, parents, emotions, and social comparison. In Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems, Taipei, China, pp. 457–464, 2011.
L. Tan, M. Y. Hu, H. Lin. Agent-based simulation of building evacuation: Combining human behavior with predictable spatial accessibility in a fire emergency. Information Sciences, vol. 295, pp. 53–66, 2015. DOI: https://doi.org/10.1016/j.ins.2014.09.029.
S. K. Wong, Y. S. Wang, P. K. Tang, T. Y. Tsai. Optimized evacuation route based on crowd simulation. Computational Visual Media, vol. 3, no. 3, pp. 243–261, 2017. DOI: https://doi.org/10.1007/s41095-017-0081-9.
Y. Zang, Q. Mei, S. X. Liu. Evacuation simulation of a high-rise teaching building considering the influence of obstacles. Simulation Modelling Practice and Theory, vol. 112, Article number 102354, 2021. DOI: https://doi.org/10.1016/j.simpat.2021.102354.
X. Z. Lu, Z. B. Yang, G. P. Cimellaro, Z. Xu. Pedestrian evacuation simulation under the scenario with earthquake-induced falling debris. Safety Science, vol. 114, pp. 61–71, 2019. DOI: https://doi.org/10.1016/j.ssci.2018.12.028.
Z. Q. Wang, G. F. Jia. A novel agent-based model for tsunami evacuation simulation and risk assessment. Natural Hazards, vol. 105, no. 2, pp. 2045–2071, 2021. DOI: https://doi.org/10.1007/s11069-020-04389-8.
P. Lv, Q. Zhang, B. Y. Xu, R. Feng, C. C. Li, J. X. Xue, B. Zhou, M. L. Xu. Agent-based campus novel Coronavirus infection and control simulation. IEEE Transactions on Computational Social Systems, vol. 9, no. 3, pp. 688–699, 2022. DOI: https://doi.org/10.1109/TCSS.2021.3114504.
A. A. Tako, S. Robinson. The application of discrete event simulation and system dynamics in the logistics and supply chain context. Decision Support Systems, vol. 52, no. 4, pp. 802–815, 2012. DOI: https://doi.org/10.1016/j.dss.2011.11.015.
Y. Zhang, R. Su, K. Z. Gao, Y. C. Zhang. A pedestrian hopping model and traffic light scheduling for pedestrian-vehicle mixed-flow networks, [Online], Available: https://arxiv.org/abs/1705.05251, 2017.
B. Li, W. F. Li. Modeling and simulation of container terminal logistics systems using Harvard architecture and agent-based computing. In Proceedings of Winter Simulation Conference, IEEE, Baltimore, USA, pp. 3396–3410, 2010. DOI: https://doi.org/10.1109/WSC.2010.5679030.
V. Elia, M. G. Gnoni, F. Tornese. Improving logistic efficiency of WEEE collection through dynamic scheduling using simulation modeling. Waste Management, vol. 72, pp. 78–86, 2018. DOI: https://doi.org/10.1016/j.wasman.2017.11.016.
I. H. Zohdy, H. A. Rakha. Intersection management via vehicle connectivity: The intersection cooperative adaptive cruise control system concept. Journal of Intelligent Transportation Systems, vol. 20, no. 1, pp. 17–32, 2016. DOI: https://doi.org/10.1080/15472450.2014.889918.
A. S. Kamal, J. I. Imura, T. Hayakawa, A. Ohata, K. Aihara. A vehicle-intersection coordination scheme for smooth flows of traffic without using traffic lights. IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 3, pp. 1136–1147, 2015. DOI: https://doi.org/10.1109/TITS.2014.2354380.
E. Moradi-Pari, H. N. Mahjoub, H. Kazemi, Y. P. Fallah, A. Tahmasbi-Sarvestani. Utilizing model-based communication and control for cooperative automated vehicle applications. IEEE Transactions on Intelligent Vehicles, vol. 2, no. 1, pp. 38–51, 2017. DOI: https://doi.org/10.1109/TIV.2017.2708605.
Y. Zhang, Y. C. Zhang, R. Su. Pedestrian-safety-aware traffic light control strategy for urban traffic congestion alleviation. IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 1, pp. 178–193, 2021. DOI: https://doi.org/10.1109/TITS.2019.2955752.
M. K. Heinrich, S. Von Mammen, D. N. Hofstadler, M. Wahby, P. Zahadat, T. Skrzypczak, M. D. Soorati, R. Krela, W. Kwiatkowski, T. Schmickl, P. Ayres, K. Stoy, H. Hamann. Constructing living buildings: A review of relevant technologies for a novel application of biohybrid robotics. Journal of the Royal Society Interface, vol. 16, no. 156, Article number 20190238, 2019. DOI: https://doi.org/10.1098/rsif.2019.0238.
X. Lan, Y. Liu, Z. Zhao. Cooperative control for swarming systems based on reinforcement learning in unknown dynamic environment. Neurocomputing, vol. 410, no. 3, pp. 10–418, 2020. DOI: https://doi.org/10.1016/j.neucom.2020.06.038.
P. K. Das, H. S. Behera, P. K. Jena, B. K. Panigrahi. An intelligent multi-robot path planning in a dynamic environment using improved gravitational search algorithm. International Journal of Automation and Computing, vol. 18, no. 6, pp. 1032–1044, 2021. DOI: https://doi.org/10.1007/s11633-016-1019-x.
Y. Zhao, Y. Ma. Songlin Hu. USV formation and path-following control via deep reinforcement learning with random braking. IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 12, pp. 5468–5478, 2021. DOI: https://doi.org/10.1109/TNNLS.2021.3068762.
K. Jin, J. Wang, H. Wang, X. Liang, Y. Guo, M. Wang, H. Yi. Soft formation control for unmanned surface vehicles under environmental disturbance using multitask reinforcement learning. Ocean Engineering, vol. 260, Article number 12035, 2022. DOI: https://doi.org/10.1016/j.oceaneng.2022.112035.
K. M. Shi, X. Y. Zhang, S. Xia. Multiple swarm fruit fly optimization algorithm based path planning method for multi-UAVs. Applied Sciences, vol. 10, no. 8, Article number 2822, 2020. DOI: https://doi.org/10.3390/app10082822.
F. A. De Alcantara Andrade, A. R. Hovenburg, L. N. De Lima, C. D. Rodin, T. A. Johansen, R. Storvold, C. A. M. Correia, D. B. Haddad. Autonomous unmanned aerial vehicles in search and rescue missions using real-time cooperative model predictive control. Sensors, vol. 19, no. 19, Article number 4067, 2019. DOI: https://doi.org/10.3390/s19194067.
P. Yao, H. L. Wang, H. X. Ji. Multi-UAVs tracking target in urban environment by model predictive control and improved grey wolf optimizer. Aerospace Science and Technology, vol. 55, pp. 131–143, 2016. DOI: https://doi.org/10.1016/j.ast.2016.05.016.
J. F. Wu, H. L. Wang, N. Li, P. Yao, Y. Huang, Z. K. Su, Y. Yu. Distributed trajectory optimization for multiple solar-powered UAVs target tracking in urban environment by adaptive grasshopper optimization algorithm. Aerospace Science and Technology, vol. 70, pp. 497–510, 2017. DOI: https://doi.org/10.1016/j.ast.2017.08.037.
X. F. Wang, H. Zhao, T. Han, H. Zhou, C. Li. A grey wolf optimizer using Gaussian estimation of distribution and its application in the multi-UAV multi-target urban tracking problem. Applied Soft Computing, vol. 78, pp. 240–260, 2019. DOI: https://doi.org/10.1016/j.asoc.2019.02.037.
R. N. Mohd, J. Geraghty. Genetic algorithm performance with different selection strategies in solving TSP. In Proceedings of the World Congress on Engineering, International Association of Engineers, London, UK, 2011.
S. T. Roberts. Commercial content moderation: Digital laborers’ dirty work. The Intersectional Internet: Race, Sex, Class and Culture Online, S. U. Noble, B. M. Tynes, Eds., Pieterlen, Switzerland: Peter Lang Publishing, 2016.
A. Ponsich, A. L. Jaimes, C. A. C. Coello. A survey on multiobjective evolutionary algorithms for the solution of the portfolio optimization problem and other finance and economics applications. IEEE Transactions on Evolutionary Computation, vol. 17, no. 3, pp. 321–344, 2013. DOI: https://doi.org/10.1109/TEVC.2012.2196800.
M. R. AlRashidi, M. E. El-Hawary. A survey of particle swarm optimization applications in electric power systems. IEEE Transactions on Evolutionary Computation, vol. 13, no. 4, pp. 913–918, 2009. DOI: https://doi.org/10.1109/TEVC.2006.880326.
A. Ouaarab, B. Ahiod, X. S. Yang. Discrete cuckoo search algorithm for the travelling salesman problem. Neural Computing and Applications, vol. 24, no. 7–8, pp. 1659–1669, 2014. DOI: https://doi.org/10.1007/s00521-013-1402-2.
E. Osaba, X. S. Yang, F. Diaz, P. Lopez-Garcia, R. Carballedo. An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems. Engineering Applications of Artificial Intelligence, vol. 48, pp. 59–71, 2016. DOI: https://doi.org/10.1016/j.engappai.2015.10.006.
Y. Huang, X. N. Shen, X. You. A discrete shuffled frogleaping algorithm based on heuristic information for traveling salesman problem. Applied Soft Computing, vol. 102, Article number 107085, 2021. DOI: https://doi.org/10.1016/j.asoc.2021.107085.
E. Summers, R. Punzalan. Bots, seeds and people: Web archives as infrastructure. In Proceedings of ACM Conference on Computer Supported Cooperative Work and Social Computing, Portland, USA, pp. 821–834, 2017. DOI: https://doi.org/10.1145/2998181.2998345.
S. Seidel, N. Berente, A. Lindberg, K. Lyytinen, J. V. Nickerson. Autonomous tools and design: A triple-loop approach to human-machine learning. Communications of the ACM, vol. 62, no. 1, pp. 50–57, 2019. DOI: https://doi.org/10.1145/3210753.
W. Shi, W. N. Chen, Y. Lin, T. L. Gu, S. Kwong, J. Zhang. An adaptive estimation of distribution algorithm for multipolicy insurance investment planning. IEEE Transactions on Evolutionary Computation, vol. 23, no. 1, pp. 1–14, 2019. DOI: https://doi.org/10.1109/TEVC.2017.2782571.
M. Asafuddoula, T. Ray, R. Sarker. A decomposition-based evolutionary algorithm for many objective optimization. IEEE Transactions on Evolutionary Computation, vol. 19, no. 3, pp. 445–460, 2015. DOI: https://doi.org/10.1109/TEVC.2014.2339823.
H. D. Wang, Y. C. Jin, J. Doherty. Committee-based active learning for surrogate-assisted particle swarm optimization of expensive problems. IEEE Transactions on Cybernetics, vol. 47, no. 9, pp. 2664–2677, 2017. DOI: https://doi.org/10.1109/TCYB.2017.2710978.
C. D. Fjell, H. Jenssen, W. A. Cheung, R. E. W. Hancock, A. Cherkasov. Optimization of antibacterial peptides by genetic algorithms and cheminformatics. Chemical Biology & Drug Design, vol. 77, no. 1, pp. 48–56, 2011. DOI: https://doi.org/10.1111/j.1747-0285.2010.01044.x.
Acknowledgements
This work was supported and funded by National Natural Science Foundation of China (Nos. 62072415 and 62036010), the National Key Research and Development Program of China (No. 2021YFB3301504), the Natural Science Foundation of Henan Province, China (No. 202300410496), the China Postdoctoral Science Foundation (No. 2019MM662530), the Special Project for COVID-19 Prevention and Control Emergency Tackling of Henan Science and Technology Department, China (No. 201100312000), the National Defense Basic Scientific Research, China (No. JCKY2020XXXB028), and the Social Simulator (Zhengzhou) Major Science and Technology Infrastructure Construction Strategy Research, China (No. 2021HENZDA03).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
The authors declared that they have no conflicts of interest to this work.
Additional information
Colored figures are available in the online version at https://link.springer.com/journal/11633
Hua Wang received the Ph. D. degree in computer science from Institute of Computing Technology, Chinese Academy of Sciences, China in 2015. She is currently an associate professor of Zhengzhou University, China.
Her research interests include traffic animation and environment modeling.
Xing-Yu Guo received the B.Sc. degree in software engineering from the College of Data Science, Taiyuan University of Technology, China in 2021. She is currently a Ph. D. degree candidate in software engineering at School of Computer and Artificial Intelligence, Zhengzhou University, China.
Her research interest is collective movement simulation evaluation.
Hao Tao received the Ph. D. degree in control science and engineering from Xi’an Jiaotong University, China in 2015. He is currently a senior engineer of China Ship Development and Design Center, China.
His research interests include swarm intelligence and digital twin.
Ming-Liang Xu received the Ph.D. degree in computer science and technology from State Key Laboratory of CAD and CG, Zhejiang University, China in 2012. He is currently a professor with School of Computer and Artificial Intelligence, Zhengzhou University, China. He has authored more than 60 journal articles and conference papers in these areas, including ACM Transactions on Graphics, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology, ACM Special Interest Group on Computer Graphics, Asia ACM Multimedia Conference, and IEEE International Conference on Computer Vision.
His research interests include computer graphics, multimedia and artificial intelligence.
Rights and permissions
About this article
Cite this article
Wang, H., Guo, XY., Tao, H. et al. Collective Movement Simulation: Methods and Applications. Mach. Intell. Res. 21, 452–480 (2024). https://doi.org/10.1007/s11633-022-1405-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11633-022-1405-5