Research article

How do classroom-turnover times depend on lecture-hall size?


  • Received: 07 October 2022 Revised: 07 February 2023 Accepted: 11 February 2023 Published: 14 March 2023
  • Academic spaces in colleges and universities span classrooms for $ 10 $ students to lecture halls that hold over $ 600 $ people. During the break between consecutive classes, students from the first class must leave and the new class must find their desks, regardless of whether the room holds $ 10 $ or $ 600 $ people. Here we address the question of how the size of large lecture halls affects classroom-turnover times, focusing on non-emergency settings. By adapting the established social-force model, we treat students as individuals who interact and move through classrooms to reach their destinations. We find that social interactions and the separation time between consecutive classes strongly influence how long it takes entering students to reach their desks, and that these effects are more pronounced in larger lecture halls. While the median time that individual students must travel increases with decreased separation time, we find that shorter separation times lead to shorter classroom-turnover times overall. This suggests that the effects of scheduling gaps and lecture-hall size on classroom dynamics depends on the perspective—individual student or whole class—that one chooses to take.

    Citation: Joseph Benson, Mariya Bessonov, Korana Burke, Simone Cassani, Maria-Veronica Ciocanel, Daniel B. Cooney, Alexandria Volkening. How do classroom-turnover times depend on lecture-hall size?[J]. Mathematical Biosciences and Engineering, 2023, 20(5): 9179-9207. doi: 10.3934/mbe.2023403

    Related Papers:

  • Academic spaces in colleges and universities span classrooms for $ 10 $ students to lecture halls that hold over $ 600 $ people. During the break between consecutive classes, students from the first class must leave and the new class must find their desks, regardless of whether the room holds $ 10 $ or $ 600 $ people. Here we address the question of how the size of large lecture halls affects classroom-turnover times, focusing on non-emergency settings. By adapting the established social-force model, we treat students as individuals who interact and move through classrooms to reach their destinations. We find that social interactions and the separation time between consecutive classes strongly influence how long it takes entering students to reach their desks, and that these effects are more pronounced in larger lecture halls. While the median time that individual students must travel increases with decreased separation time, we find that shorter separation times lead to shorter classroom-turnover times overall. This suggests that the effects of scheduling gaps and lecture-hall size on classroom dynamics depends on the perspective—individual student or whole class—that one chooses to take.



    加载中


    [1] D. Helbing, L. Buzna, A. Johansson, T. Werner, Self-organized pedestrian crowd dynamics: Experiments, simulations, and design solutions, Transp. Sci., 39 (2005), 1–24. https://doi.org/10.1287/trsc.1040.0108 doi: 10.1287/trsc.1040.0108
    [2] N. Bellomo, D. Clarke, L. Gibelli, P. Townsend, B. J. Vreugdenhil, Human behaviours in evacuation crowd dynamics: From modelling to "big data" toward crisis management, Phys. Life Rev., 18 (2016), 1–21. https://doi.org/10.1016/j.plrev.2016.05.014 doi: 10.1016/j.plrev.2016.05.014
    [3] A. Schadschneider, M. Chraibi, A. Seyfried, A. Tordeux, J. Zhang, Pedestrian dynamics: From empirical results to modeling, in Modeling and Simulation in Science, Engineering and Technology, Springer-Verlag, New York, 2018, 63–102. https://doi.org/10.1007/978-3-030-05129-7_4
    [4] D. C. Duives, W. Daamen, S. P. Hoogendoorn, State-of-the-art crowd motion simulation models, Transp. Res. C: Emerg. Technol., 37 (2013), 193–209. https://doi.org/10.1016/j.trc.2013.02.005 doi: 10.1016/j.trc.2013.02.005
    [5] B. Zhan, D. N. Monekosso, P. Remagnino, S. A. Velastin, L.-Q. Xu, Crowd analysis: a survey, Mach. Vis. Appl., 19 (2008), 345–357. https://doi.org/10.1007/s00138-008-0132-4
    [6] E. Cristiani, B. Piccoli, A. Tosin, Multiscale Modeling of Pedestrian Dynamics, MS & A, Springer-Verlag, New York, 2014. https://doi.org/10.1007/978-3-319-06620-2
    [7] B. D. Hankin, R. A. Wright, Passenger flow in subways, J. Oper. Res. Soc., 9 (1958), 81–88. https://doi.org/10.2307/3006732
    [8] A. A. Bartlett, The Frank C. Walz lecture halls: A new concept in the design of lecture auditoria, Am. J. Phys., 41 (1973), 1233–1240. https://doi.org/10.1119/1.1987535 doi: 10.1119/1.1987535
    [9] V. Romero, W. D. Stone, J. D. Ford, COVID-19 indoor exposure levels: An analysis of foot traffic scenarios within an academic building, Transp. Res. Interdiscip. Perspect., 7 (2020), 100185. https://doi.org/10.1016/j.trip.2020.100185 doi: 10.1016/j.trip.2020.100185
    [10] S. Sajjadi, A. Hashemi, F. Ghanbarnejad, Social distancing in pedestrian dynamics and its effect on disease spreading, Phys. Rev. E, 104 (2021), 014313. https://doi.org/10.1103/PhysRevE.104.014313 doi: 10.1103/PhysRevE.104.014313
    [11] M. Xu, X. Xie, P. Lv, J. Niu, H. Wang, C. Li, et al., Crowd behavior simulation with emotional contagion in unexpected multihazard situations, IEEE Trans. Syst. Man Cybern.: Syst., 51 (2021), 1567–1581. https://doi.org/10.1109/TSMC.2019.2899047 doi: 10.1109/TSMC.2019.2899047
    [12] D. Helbing, I. Farkas, T. Vicsek, Simulating dynamical features of escape panic, Nature, 407 (2000), 487–490. https://doi.org/10.1038/35035023 doi: 10.1038/35035023
    [13] M. Haghani, M. Sarvi, Simulating dynamics of adaptive exit-choice changing in crowd evacuations: Model implementation and behavioural interpretations, Transp. Res. Part C Emerg. Technol., 103 (2019), 56–82.
    [14] H. R. L. Lee, A. Bhatia, J. Brynjarsdóttir, N. Abaid, A. Barbaro, S. Butail, Speed modulated social influence in evacuating pedestrian crowds, Collective Dyn., 5 (2020), 1–24. https://doi.org/10.17815/CD.2020.25 doi: 10.17815/CD.2020.25
    [15] Z. Li, W. Xu, Pedestrian evacuation within limited-space buildings based on different exit design schemes, Saf. Sci., 124 (2020), 104575.
    [16] E. Porter, S. H. Hamdar, W. Daamen, Pedestrian dynamics at transit stations: an integrated pedestrian flow modeling approach, Transp. A: Transp. Sci., 14 (2018), 468–483. https://doi.org/10.1080/23249935.2017.1378280 doi: 10.1080/23249935.2017.1378280
    [17] S. P. Hoogendoorn, P. H. L. Bovy, Pedestrian route-choice and activity scheduling theory and models, Transp. Res. B: Methodol., 38 (2004), 169–190. https://doi.org/10.1016/S0191-2615(03)00007-9 doi: 10.1016/S0191-2615(03)00007-9
    [18] N. W. F. Bode, E. Ronchi, Statistical model fitting and model selection in pedestrian dynamics research, Collective Dyn., 4 (2019), 1–32. https://doi.org/10.17815/CD.2019.20 doi: 10.17815/CD.2019.20
    [19] D. Helbing, A. Johansson, Pedestrian, Crowd and Evacuation Dynamics, Springer, New York, 2009, 6476–6495. https://doi.org/10.1007/978-0-387-30440-3_382
    [20] A. Sieben, J. Schumann, A. Seyfried, Collective phenomena in crowds–Where pedestrian dynamics need social psychology, PLOS ONE, 12 (2017), e0177328. https://doi.org/10.1371/journal.pone.0177328 doi: 10.1371/journal.pone.0177328
    [21] M. Davidich, F. Geiss, H. G. Mayer, A. Pfaffinger, C. Royer, Waiting zones for realistic modelling of pedestrian dynamics: A case study using two major German railway stations as examples, Transp. Res. C: Emerg. Technol., 37 (2013), 210–222. https://doi.org/10.1016/j.trc.2013.02.016 doi: 10.1016/j.trc.2013.02.016
    [22] D. Nilsson, A. Johansson, Social influence during the initial phase of a fire evacuation–Analysis of evacuation experiments in a cinema theatre, Fire Saf. J., 44 (2009), 71–79. https://doi.org/10.1016/j.firesaf.2008.03.008 doi: 10.1016/j.firesaf.2008.03.008
    [23] M. Moussaïd, E. G. Guillot, M. Moreau, J. Fehrenbach, O. Chabiron, S. Lemercier, et al., Traffic instabilities in self-organized pedestrian crowds, PLOS Comput. Biol., 8 (2012), e1002442. https://doi.org/10.1371/journal.pcbi.1002442 doi: 10.1371/journal.pcbi.1002442
    [24] S. P. Hoogendoorn, W. Daamen, Pedestrian behavior at bottlenecks, Transp. Sci., 39 (2005), 147–159. http://www.jstor.org/stable/25769239
    [25] A. Seyfried, O. Passon, B. Steffen, M. Boltes, T. Rupprecht, W. Klingsch, New insights into pedestrian flow through bottlenecks, Transp. Sci., 43 (2009), 395–406. http://www.jstor.org/stable/25769460
    [26] Y. Feng, D. Duives, W. Daamen, S. Hoogendoorn, Data collection methods for studying pedestrian behaviour: A systematic review, Build. Environ., 187 (2021), 107329. https://doi.org/10.1016/j.buildenv.2020.107329 doi: 10.1016/j.buildenv.2020.107329
    [27] D. Helbing, Traffic and related self-driven many-particle systems, Rev. Mod. Phys., 73 (2001), 1067–1141. https://link.aps.org/doi/10.1103/RevModPhys.73.1067
    [28] J. A. Carrillo, S. Martin, M.-T. Wolfram, An improved version of the Hughes model for pedestrian flow, Math. Models Methods Appl. Sci., 26 (2016), 671–697. https://doi.org/10.1142/S0218202516500147 doi: 10.1142/S0218202516500147
    [29] R. L. Hughes, A continuum theory for the flow of pedestrians, Transp. Res. B: Methodol., 36 (2002), 507–535. https://doi.org/10.1016/S0191-2615(01)00015-7 doi: 10.1016/S0191-2615(01)00015-7
    [30] R. L. Hughes, The flow of human crowds, Annu. Rev. Fluid Mech., 35 (2003), 169–182. https://doi.org/10.1146/annurev.fluid.35.101101.161136
    [31] R. M. Colombo, M. Garavello, M. Lécureux-Mercier, A class of nonlocal models for pedestrian traffic, Math. Models Methods Appl. Sci., 22 (2012), 1150023. https://doi.org/10.1142/S0218202511500230 doi: 10.1142/S0218202511500230
    [32] A. L. Bertozzi, J. Rosado, M. B. Short, L. Wang, Contagion shocks in one dimension, J. Stat. Phys., 158 (2015), 647–664. https://doi.org/10.1007/s10955-014-1019-6 doi: 10.1007/s10955-014-1019-6
    [33] R. Bürger, P. Goatin, D. Inzunza, L. M. Villada, A non-local pedestrian flow model accounting for anisotropic interactions and domain boundaries, Math. Biosci. Eng., 17 (2020), 5883–5906. https://doi.org/10.3934/mbe.2020314. doi: 10.3934/mbe.2020314
    [34] N. Bellomo, A. Bellouquid, D. Knopoff, From the microscale to collective crowd dynamics, Multiscale Model. Simul., 11 (2013), 943–963. https://doi.org/10.1137/130904569 doi: 10.1137/130904569
    [35] D. Kim, A. Quaini, Coupling kinetic theory approaches for pedestrian dynamics and disease contagion in a confined environment, Math. Models Methods Appl. Sci., 30 (2020), 1893–1915. https://doi.org/10.1142/S0218202520400126 doi: 10.1142/S0218202520400126
    [36] A. Festa, A. Tosin, M.-T. Wolfram, Kinetic description of collision avoidance in pedestrian crowds by sidestepping, Kinet. Relat. Models, 11 (2018), 491. https://doi.org/10.3934/krm.2018022 doi: 10.3934/krm.2018022
    [37] P. Degond, C. Appert-Rolland, J. Pettré, G. Theraulaz, Vision-based macroscopic pedestrian models, Kinet. Relat. Models, 6 (2013), 803–839. https://doi.org/10.3934/krm.2013.6.809 doi: 10.3934/krm.2013.6.809
    [38] L. F. Henderson, The statistics of crowd fluids, Nature, 229 (1971), 381–383. https://doi.org/10.1038/229381a0 doi: 10.1038/229381a0
    [39] C. Burstedde, K. Klauck, A. Schadschneider, J. Zittartz, Simulation of pedestrian dynamics using a two-dimensional cellular automaton, Physica A, 295 (2001), 507–525. https://doi.org/10.1016/S0378-4371(01)00141-8 doi: 10.1016/S0378-4371(01)00141-8
    [40] A. Varas, M. Cornejo, D. Mainemer, B. Toledo, J. Rogan, V. Muñoz, et al., Cellular automaton model for evacuation process with obstacles, Physica A, 382 (2007), 631–642. https://doi.org/10.1016/j.ssci.2010.09.006 doi: 10.1016/j.ssci.2010.09.006
    [41] J. Hu, L. You, H. Zhang, J. Wei, Y. Guo, Study on queueing behavior in pedestrian evacuation by extended cellular automata model, Physica A, 489 (2018), 112–127. https://doi.org/10.1016/j.physa.2017.07.004 doi: 10.1016/j.physa.2017.07.004
    [42] A. Kirchner, A. Schadschneider, Simulation of evacuation processes using a bionics-inspired cellular automaton model for pedestrian dynamics, Physica A, 312 (2002), 260–276. https://doi.org/10.1016/S0378-4371(02)00857-9 doi: 10.1016/S0378-4371(02)00857-9
    [43] V. J. Blue, J. L. Adler, Cellular automata microsimulation for modeling bi-directional pedestrian walkways, Transp. Res. B: Methodol., 35 (2001), 293–312. https://doi.org/10.1016/S0191-2615(99)00052-1 doi: 10.1016/S0191-2615(99)00052-1
    [44] Q. F. Gao, Y. Z. Tao, Y. F. Wei, C. Wu, L. Y. Dong, Simulation-based optimization of inner layout of a theater considering the effect of pedestrians, Chin. Phys. B, 29 (2020), https://doi.org/10.1088/1674-1056/ab6c44
    [45] A. Kirchner, H. Klüpfel, K. Nishinari, A. Schadschneider, M. Schreckenberg, Simulation of competitive egress behavior: Comparison with aircraft evacuation data, Physica A, 324 (2003), 689–697. https://doi.org/10.1016/S0378-4371(03)00076-1 doi: 10.1016/S0378-4371(03)00076-1
    [46] D. Helbing, M. Isobe, T. Nagatani, K. Takimoto, Lattice gas simulation of experimentally studied evacuation dynamics, Phys. Rev. E, 67 (2003), 067101. https://doi.org/10.1103/PhysRevE.67.067101 doi: 10.1103/PhysRevE.67.067101
    [47] Y. Tajima, T. Nagatani, Scaling behavior of crowd flow outside a hall, Physica A, 292 (2001), 545–554. https://doi.org/10.1016/S0378-4371(00)00630-0 doi: 10.1016/S0378-4371(00)00630-0
    [48] H. Kuang, X. Li, T. Song, S. Dai, Analysis of pedestrian dynamics in counter flow via an extended lattice gas model, Phys. Rev. E, 78 (2008), 066117. https://doi.org/10.1103/PhysRevE.78.066117 doi: 10.1103/PhysRevE.78.066117
    [49] D. Helbing, P. Molnár, Social force model for pedestrian dynamics, Phys. Rev. E, 51 (1995), 4282. https://doi.org/10.1103/physreve.51.4282 doi: 10.1103/physreve.51.4282
    [50] F. Zanlungo, T. Ikeda, T. Kanda, Social force model with explicit collision prediction, EPL, 93 (2011), 68005. https://doi.org/10.1209/0295-5075/93/68005 doi: 10.1209/0295-5075/93/68005
    [51] M. Li, Y. Zhao, L. He, W. Chen, X. Xu, The parameter calibration and optimization of social force model for the real-life 2013 Ya'an earthquake evacuation in China, Saf. Sci., 79 (2015), 243–253. https://doi.org/10.1016/j.ssci.2015.06.018 doi: 10.1016/j.ssci.2015.06.018
    [52] S. Seer, C. Rudloff, T. Matyus, N. Brändle, Validating social force based models with comprehensive real world motion data, Transp. Res. Proc., 2 (2014), 724–732. https://doi.org/10.1016/j.trpro.2014.09.080 doi: 10.1016/j.trpro.2014.09.080
    [53] A. Johansson, D. Helbing, P. K. Shukla, Specification of the social force pedestrian model by evolutionary adjustment to video tracking data, Adv. Complex Syst., 10 (2007), 271–288. https://doi.org/10.1142/S0219525907001355 doi: 10.1142/S0219525907001355
    [54] M. Ko, T. Kim, K. Sohn, Calibrating a social-force-based pedestrian walking model based on maximum likelihood estimation, Transportation, 40 (2013), 91–107. https://doi.org/10.1007/s11116-012-9411-z doi: 10.1007/s11116-012-9411-z
    [55] A. Kneidl, D. Hartmann, A. Borrmann, A hybrid multi-scale approach for simulation of pedestrian dynamics, Transp. Res. C: Emerg. Technol., 37 (2013), 223–237. https://doi.org/10.1016/j.trc.2013.03.005 doi: 10.1016/j.trc.2013.03.005
    [56] C. Delcea, L. A. Cotfas, Increasing awareness in classroom evacuation situations using agent-based modeling, Physica A, 523 (2019), 1400–1418. https://doi.org/10.1016/j.physa.2019.04.137 doi: 10.1016/j.physa.2019.04.137
    [57] R. Liu, D. Jiang, L. Shi, Agent-based simulation of alternative classroom evacuation scenarios, Front. Archit. Res., 5 (2016), 111–125. https://doi.org/10.1016/j.foar.2015.12.002 doi: 10.1016/j.foar.2015.12.002
    [58] A. Lachapelle, M. T. Wolfram, On a mean field game approach modeling congestion and aversion in pedestrian crowds, Transp. Res. B: Methodol., 45 (2011), 1572–1589. https://doi.org/10.1016/j.trb.2011.07.011 doi: 10.1016/j.trb.2011.07.011
    [59] C. Dogbé, Modeling crowd dynamics by the mean-field limit approach, Math. Comput. Model., 52 (2010), 1506–1520. https://doi.org/10.1016/j.mcm.2010.06.012 doi: 10.1016/j.mcm.2010.06.012
    [60] X. Zheng, Y. Cheng, Modeling cooperative and competitive behaviors in emergency evacuation: A game-theoretical approach, Comput. Math. Appl., 62 (2011), 4627–4634. https://doi.org/10.1016/j.camwa.2011.10.048 doi: 10.1016/j.camwa.2011.10.048
    [61] M. Burger, M. Di Francesco, P. A. Markowich, M.-T. Wolfram, On a mean field game optimal control approach modeling fast exit scenarios in human crowds, in 52nd IEEE Conference on Decision and Control, 2013, 3128–3133. https://doi.org/10.1109/CDC.2013.6760360
    [62] E. Cartee, A. Vladimirsky, Anisotropic challenges in pedestrian flow modeling, Commun. Math. Sci., 16 (2018), 1067–1093. https://dx.doi.org/10.4310/CMS.2018.v16.n4.a7 doi: 10.4310/CMS.2018.v16.n4.a7
    [63] Y. Achdou, J. M. Lasry, Mean field games for modeling crowd motion, in Contributions to Partial Differential Equations and Applications, Springer, Cham, 2019, 17–42. https://doi.org/10.1007/978-3-319-78325-3_4
    [64] R. Bailo, J. A. Carrillo, P. Degond, Pedestrian Models Based on Rational Behaviour, 259–292, Springer, Cham, 2018. https://doi.org/10.1007/978-3-030-05129-7_9
    [65] L. Fu, J. Luo, M. Deng, L. Kong, H. Kuang, Simulation of evacuation processes in a large classroom using an improved cellular automaton model for pedestrian dynamics, Procedia Eng., 31 (2012), 1066–1071. https://doi.org/10.1016/j.proeng.2012.01.1143 doi: 10.1016/j.proeng.2012.01.1143
    [66] J. Zhang, W. Song, X. Xu, Experiment and multi-grid modeling of evacuation from a classroom, Physica A, 387 (2008), 5901–5909. https://doi.org/10.1016/j.physa.2008.06.030 doi: 10.1016/j.physa.2008.06.030
    [67] K. Takimoto, T. Nagatani, Spatio-temporal distribution of escape time in evacuation process, Physica A, 320 (2003), 611–621. https://doi.org/10.1016/S0378-4371(02)01540-6 doi: 10.1016/S0378-4371(02)01540-6
    [68] A. Garcimartín, I. Zuriguel, J. M. Pastor, C. Martín-Gómez, D. R. Parisi, Experimental evidence of the "faster is slower" effect, Transp. Res. Proc., 2 (2014), 760–767. https://doi.org/10.1016/j.trpro.2014.09.085 doi: 10.1016/j.trpro.2014.09.085
    [69] M. Moussaïd, D. Helbing, G. Theraulaz, How simple rules determine pedestrian behavior and crowd disasters, Proc. Natl. Acad. Sci. USA, 108 (2011), 6884–6888. https://doi.org/10.1073/pnas.1016507108 doi: 10.1073/pnas.1016507108
    [70] J. Zhang, W. Klingsch, A. Schadschneider, A. Seyfried, Ordering in bidirectional pedestrian flows and its influence on the fundamental diagram, J. Stat. Mech. Theory Exp., 2012 (2012), P02002. https://doi.org/10.1088/1742-5468/2012/02/P02002 doi: 10.1088/1742-5468/2012/02/P02002
    [71] Dateline Staff, New lecture hall on the (California Ave.) block, 2019, Last accessed: 27-01-2022. Available from: https://www.ucdavis.edu/news/new-lecture-hall-block
    [72] UC Davis Office of the University Registrar, General Assignment Classroom Guide: 194 Rock Hall, Last accessed: 27-01-2022. Available from: https://registrar-apps.ucdavis.edu/rooms/room2.cfm?RoomType = GeneralAssignment & ID = 1
    [73] I. Fink and Associates Inc, The Ohio State University Instructional Space Feasibility Study Final Report, 2009, Last accessed: 27-01-2022. Available from: https://registrar.osu.edu/scheduling/spacestudyfinalreport.pdf
    [74] J. Benson, M. Bessonov, K. Burke, S. Cassani, M. V. Ciocanel, D. B. Cooney, et al., Code associated with "How do classroom-turnover times depend on lecture-hall size?", 2022. Available from: https://gitlab.com/modeling-pedestrian-dynamics/lecture-hall-dynamics/-/tree/main/
    [75] N. Waldau, P. Gattermann, H. Knoflacher, M. Schreckenberg, Pedestrian and evacuation dynamics 2005, Springer, Berlin, 2007. https://doi.org/10.1007/978-3-540-47064-9
    [76] K. Alden, M. Read, J. Timmis, P. S. Andrews, H. Veiga-Fernandes, M. Coles, Spartan: A comprehensive tool for understanding uncertainty in simulations of biological systems, PLOS Comput. Biol., 9 (2013), e1002916. https://doi.org/10.1371/journal.pcbi.1002916 doi: 10.1371/journal.pcbi.1002916
    [77] J. Cosgrove, J. Butler, K. Alden, M. Read, V. Kumar, L. Cucurull-Sanchez, et al., Agent-based modeling in systems pharmacology, CPT: Pharmacometrics Syst. Pharmacol., 4 (2015), 615–629. https://doi.org/10.1002/psp4.12018 doi: 10.1002/psp4.12018
    [78] M. Read, P. S. Andrews, J. Timmis, V. Kumar, Techniques for grounding agent-based simulations in the real domain: A case study in experimental autoimmune encephalomyelitis, Math. Comput. Model. Dyn. Syst., 18 (2012), 67–86. ttps://doi.org/10.1080/13873954.2011.601419 doi: 10.1080/13873954.2011.601419
    [79] A. Vargha, H. D. Delaney, A critique and improvement of the CL common language effect size statistics of McGraw and Wong, J. Educ. Behav. Stat., 25 (2000), 101–132. https://doi.org/10.3102/10769986025002101 doi: 10.3102/10769986025002101
    [80] S. Cassani, S. D. Olson, A hybrid model of cartilage regeneration capturing the interactions between cellular dynamics and porosity, Bull. Math. Biol., 82 (2020), 1–32. https://doi.org/10.1007/s11538-020-00695-1 doi: 10.1007/s11538-020-00695-1
    [81] CollegeData.com, 1st Financial Bank USA, 2022, Last accessed: 27-01-2022. Available from: https://waf.collegedata.com/college-search
    [82] M. Chraibi, M. Freialdenhoven, A. Schadschneider, A. Seyfried, Modeling the desired direction in a force-based model for pedestrian dynamics, in Traffic and Granular Flow '11, Springer, Berlin, 2013,263–275. https://doi.org/10.1007/978-3-642-39669-4_25
    [83] L. Ma, B. Chen, L. Chen, X. Xu, S. Liu, X. Liu, Data driven analysis of the desired speed in ordinary differential equation based pedestrian simulation models, Physica A, 608 (2022), 128241. https://doi.org/10.1016/j.physa.2022.128241 doi: 10.1016/j.physa.2022.128241
    [84] F. Zanlungo, C. Feliciani, Z. Yücel, K. Nishinari, T. Kanda, Analysis and modelling of macroscopic and microscopic dynamics of a pedestrian cross-flow, preprint, arXiv: 2112.12304.
    [85] D. Wolinski, S. J. Guy, A. H. Olivier, M. Lin, D. Manocha, J. Pettré, Parameter estimation and comparative evaluation of crowd simulations, Comput. Graph Forum., 33 (2014), 303–312, https://doi.org/10.1111/cgf.12328. doi: 10.1111/cgf.12328
    [86] Q. Xu, M. Chraibi, A. Seyfried, Anticipation in a velocity-based model for pedestrian dynamics, Transp. Res. Part C Emerg. Technol., 133 (2021), 103464. https://doi.org/10.1016/j.trc.2021.103464 doi: 10.1016/j.trc.2021.103464
    [87] W. J. Yu, R. Chen, L. Y. Dong, S. Q. Dai, Centrifugal force model for pedestrian dynamics, Phys. Rev. E, 72 (2005), 026112. https://doi.org/10.1103/PhysRevE.72.026112 doi: 10.1103/PhysRevE.72.026112
    [88] A. Rapoport, W. E. Stein, J. E. Parco, D. A. Seale, Equilibrium play in single-server queues with endogenously determined arrival times, J. Econ. Behav. Organ., 55 (2004), 67–91. https://doi.org/10.1016/j.jebo.2003.07.003 doi: 10.1016/j.jebo.2003.07.003
    [89] S. Juneja, N. Shimkin, The concert queueing game: strategic arrivals with waiting and tardiness costs, Queueing Syst., 74 (2013), 369–402. https://doi.org/10.1007/s11134-012-9329-3 doi: 10.1007/s11134-012-9329-3
    [90] D. Levinson, Micro-foundations of congestion and pricing: A game theory perspective, Transp. Res. A: Policy Pract., 39 (2005), 691–704. https://doi.org/10.1016/j.tra.2005.02.021 doi: 10.1016/j.tra.2005.02.021
    [91] A. Ziegelmeyer, F. Koessler, K. B. My and L. Denant-Boèmont, Road traffic congestion and public information: An experimental investigation, J. Transp. Econ. Policy, 42 (2008), 43–82.
    [92] O. Guéant, J. M. Lasry, P. L. Lions, Mean field games and applications, in Paris-Princeton Lectures on Mathematical Finance 2010, Springer, Berlin, 2011,205–266. https://doi.org/10.1007/978-3-642-14660-2_3
    [93] H. Harapan, N. Itoh, A. Yufika, W. Winardi, S. Keam, H. Te, et al., Coronavirus disease 2019 (COVID-19): A literature review, J. Infect. Public Health, 13 (2020), 667–673. https://doi.org/10.1016/j.jiph.2020.03.019 doi: 10.1016/j.jiph.2020.03.019
  • mbe-20-05-403 supplementary.pdf
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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

Metrics

Article views(1366) PDF downloads(74) Cited by(0)

Article outline

Figures and Tables

Figures(8)  /  Tables(2)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog