Evacuation accidents have been frequent in recent years, and during evacuation we know that people do not always act selfishly. This study aims to investigate the effect of helping behavior on evacuation efficiency. Through the fuzzy logic reasoning method combined with the cellular automata (CA) model, we investigated the role of helping behavior on evacuation efficiency in different contexts and came up with the following results. Firstly, we found that helping behavior has a positive effect on evacuation efficiency to some extent and can significantly reduce evacuation time. At an average personnel density of approximately 1.38 persons per square meter, individuals involved in helping behavior are able to provide assistance and speed up the evacuation process. However, when the personnel density is too high, helping behavior may instead reduce evacuation efficiency. This may be attributable to the blockage of exits in high-density situations, resulting in a weakening of the synergistic effect of helping behavior. The findings elucidate the dual-edged nature of helping behavior in evacuations and emphasize the importance of strategic planning and crowd management to harness its potential benefits effectively.
HaghaniM.SarviM.Crowd Behaviour and Motion: Empirical Methods. Transportation Research Part B: Methodological, Vol. 107, 2018, pp. 253–294. https://doi.org/10.1016/j.trb.2017.06.017.
2.
DruryJ.CockingC.ReicherS.The Nature of Collective Resilience: Survivor Reactions to the 2005 London Bombings. International Journal of Mass Emergencies & Disasters, Vol. 27, No. 1, 2009, pp. 66–95. https://doi.org/10.1177/028072700902700104.
3.
BodeN. W.MillerJ.O’GormanR.CodlingE. A.Increased Costs Reduce Reciprocal Helping Behaviour of Humans in a Virtual Evacuation Experiment. Scientific Reports, Vol. 5, No. 1, 2015, p. 15896. https://doi.org/10.1038/srep15896.
4.
FuL.SongW.LoS.A Fuzzy-Theory-Based Behavioral Model for Studying Pedestrian Evacuation from a Single-Exit Room. Physics Letters A, Vol. 380, No. 34, 2016, pp. 2619–2627. https://doi.org/10.1016/j.physleta.2016.06.011.
5.
ZhangJ.ZhuJ.DangP.WuJ.ZhouY.LiW.FuL.GuoY.YouJ.An Improved Social Force Model (ISFM)-Based Crowd Evacuation Simulation Method in Virtual Reality with a Subway Fire as a Case Study. International Journal of Digital Earth, Vol. 16, No. 1, 2023, pp. 1186–1204. https://doi.org/10.1080/17538947.2023.2197261.
6.
LeeM.LeeJ.JunC.An Extended Floor Field Model Considering the Spread of Fire and Detour Behavior. Physica A: Statistical Mechanics and its Applications, Vol. 577, 2021, p. 126069. https://doi.org/10.1016/j.physa.2021.126069.
7.
GuoX.ChenJ.ZhengY.WeiJ.A Heterogeneous Lattice Gas Model for Simulating Pedestrian Evacuation. Physica A: Statistical Mechanics and its Applications, Vol. 391, No. 3, 2012, pp. 582–592. https://doi.org/10.1016/j.physa.2011.07.055.
8.
ChenM.HanD.Multi-Grid Model for Crowd’s Evacuation in Ships Based on Cellular Automata. Polish Maritime Research, Vol. 22, No. s1, 2015, pp. 75–81. https://doi.org/10.1515/pomr-2015-0036.
9.
KaserekaS.KasoroN.KyamakyaK.GoufoE.-F. D.ChokkiA. P.YengoM. V.Agent-Based Modelling and Simulation for Evacuation of People from a Building in Case of Fire. Procedia Computer Science, Vol. 130, 2018, pp. 10–17. https://doi.org/10.1016/j.procs.2018.04.006.
10.
YuanX.-T.TangT.-Q.ChenL.WangT.A fine grid cellular automaton model for pedestrian evacuation considering the effect of an obstacle. SIMULATION, Vol. 99, 2023, p. 957–968. https://doi.org/10.1177/00375497231161146.
11.
Von SiversI.TempletonA.KösterG.DruryJ.PhilippidesA.Humans Do Not Always Act Selfishly: Social Identity and Helping in Emergency Evacuation Simulation. Transportation Research Procedia, Vol. 2, 2014, pp. 585–593. https://doi.org/10.1016/j.trpro.2014.09.099.
12.
von SiversI.TempletonA.KünznerF.KösterG.DruryJ.PhilippidesA.NeckelT.BungartzH.-J.Modelling Social Identification and Helping in Evacuation Simulation. Safety Science, Vol. 89, 2016, pp. 288–300. https://doi.org/10.1016/j.ssci.2016.07.001.
13.
KwakJ.LeesM. H.CaiW.OngM. E. H.Modeling Helping Behavior in Emergency Evacuations Using Volunteer’s Dilemma Game. In Computational Science – ICCS 2020 (V. V.KrzhizhanovskayaZávodszkyG.LeesM. H.DongarraJ. J.SlootP. M. A.BrissosS.TeixeiraJ., eds.), Springer International Publishing, Cham, 2020, pp. 513–523.
14.
KwakJ.LeesM. H.CaiW.PourghaderiA. R.OngM. E. H.Influence of Committed Volunteers on Helping Behavior in Emergency Evacuations. Journal of Physics: Complexity, Vol. 3, No. 1, 2022, p. 015005. https://doi.org/10.1088/2632-072X/ac48a1.
15.
ZhengX.ChengY.Modeling Cooperative and Competitive Behaviors in Emergency Evacuation: A Game-Theoretical Approach. Computers & Mathematics with Applications, Vol. 62, No. 12, 2011, pp. 4627–4634. https://doi.org/10.1016/j.camwa.2011.10.048.
16.
MaY.-P.ZhangH.Simulation Study on Cooperation Behaviors and Crowd Dynamics in Pedestrian Evacuation. Chinese Physics B, Vol. 29, No. 3, 2020, p. 038901. https://doi.org/10.1088/1674-1056/ab6b14.
17.
YuR.MaoQ.LvJ.An Extended Model for Crowd Evacuation Considering Rescue Behavior. Physica A: Statistical Mechanics and its Applications, Vol. 605, 2022, p. 127989. https://doi.org/10.1016/j.physa.2022.127989.
18.
MehraP.Context-Aware Computing: Beyond Search and Location-Based Services. IEEE Internet Computing, Vol. 16, No. 2, 2012, pp. 12–16. https://doi.org/10.1109/MIC.2012.31.
19.
ZouB.LuC.MaoS.LiY.Effect of Pedestrian Judgement on Evacuation Efficiency Considering Hesitation. Physica A: Statistical Mechanics and its Applications, Vol. 547, 2020, p. 122943. https://doi.org/10.1016/j.physa.2019.122943.
20.
ManleyM.KimY. S.ChristensenK.ChenA.Modeling Emergency Evacuation of Individuals with Disabilities in a Densely Populated Airport. Transportation Research Record: Journal of the Transportation Research Board, 2011. 2206: 32–38. https://doi.org/10.3141/2206-05.
21.
WangG.ChenT.ZhengH.WangJ.HuX.DengK.TaoZ.LuoN.Heterogeneous Crowd Dynamics Considering the Impact of Personality Traits under a Fire Emergency: A Questionnaire & Simulation-Based Approach. Physica A: Statistical Mechanics and its Applications, Vol. 610, 2023, p. 128411. https://doi.org/10.1016/j.physa.2022.128411.
22.
ElzieT.FrydenlundE.CollinsA. J.RobinsonR. M.Panic That Spreads Sociobehavioral Contagion in Pedestrian Evacuations. Transportation Research Record: Journal of the Transportation Research Board, 2016. 2586: 1–8. https://doi.org/10.3141/2586-01.
23.
LiF.ZhangY.MaY.ZhangH.Modelling Multi-Exit Large-Venue Pedestrian Evacuation with Dual-Strategy Adaptive Particle Swarm Optimization. IEEE Access, Vol. 8, 2020, pp. 114554–114569. https://doi.org/10.1109/ACCESS.2020.3003082.
24.
ChenM.WuK.ZhangH.HanD.GuoM.A Ship Evacuation Model Considering the Interaction between Pedestrians Based on Cellular Automata. Ocean Engineering, Vol. 281, 2023, p. 114644. https://doi.org/10.1016/j.oceaneng.2023.114644.
25.
GavriilidisP.GerakakisI.GeorgoudasI. G.TrunfioG. A.SirakoulisG. Ch.A Fuzzy Logic Inspired Cellular Automata Based Model for Simulating Crowd Evacuation Processes. In Parallel Processing and Applied Mathematics (R.WyrzykowskiDongarraJ.DeelmanE.KarczewskiK., eds.), Springer International Publishing, Cham, 2018, pp. 436–445.
26.
BisgambigliaP.-A.InnocentiE.GonsolinP.-R.A New Way to Use Fuzzy Inference Systems in Activity-Based Cellular Modeling Simulations. Presented at the 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples, Italy, 2017.
27.
YangX.YangX.WangQ.Pedestrian Evacuation under Guides in a Multiple-Exit Room via the Fuzzy Logic Method. Communications in Nonlinear Science and Numerical Simulation, Vol. 83, 2020, p. 105138. https://doi.org/10.1016/j.cnsns.2019.105138.
28.
LiuT.YangX.WangQ.ZhouM.XiaS.A Fuzzy-Theory-Based Cellular Automata Model for Pedestrian Evacuation from a Multiple-Exit Room. IEEE access, Vol. 8, 2020, pp. 106334–106345. https://doi.org/10.1109/ACCESS.2020.3000606.
29.
XueZ.FanX.JinQ.JianH.LiuJ.A Fuzzy Logic-Based Model for Heterogeneous Pedestrian Dynamical Behaviors. Fuzzy Systems and Data Mining III (FSDM 2017), IOS PRESS, Natl Dong Hwa Univ, Hualien, TAIWAN, 299, 2017, pp. 238–244.
30.
GuopingG. A. O.ChangshengG.A Helping Behavior-Considering Cellular Automaton Model for Evacuation from a Building. China Safety Science Journal, Vol. 28, No. 1, 2018, p. 56. https://doi.org/10.16265/j.cnki.issn1003-3033.2018.01.010.
31.
WangG.ChenT.HuX.ZhengH.JiangW.Wall-Following Searching or Area Coverage Searching? Simulation Study of the Panic Evacuation Considering the Guidance of a Single Rescuer. Physica A: Statistical Mechanics and its Applications, Vol. 603, 2022, p. 127638. https://doi.org/10.1016/j.physa.2022.127638.
32.
ChenC.SunH.LeiP.ZhaoD.ShiC.An Extended Model for Crowd Evacuation Considering Pedestrian Panic in Artificial Attack. Physica A: Statistical Mechanics and its Applications, Vol. 571, 2021, p. 125833. https://doi.org/10.1016/j.physa.2021.125833.
33.
DengK.LiM.WangG.HuX.ZhangY.ZhengH.TianK.ChenT.Experimental Study on Panic during Simulated Fire Evacuation Using Psycho-and Physiological Metrics. International Journal of Environmental Research and Public Health, Vol. 19, No. 11, 2022, p. 6905. https://doi.org/10.3390/ijerph19116905.
34.
LiC.LiuS.XuG.CenX.Influence of Driver’s Yielding Behavior on Pedestrian-Vehicle Conflicts at a Two-Lane Roundabout Using Fuzzy Cellular Automata. Journal of Central South University, Vol. 29, No. 1, 2022, pp. 346–358. https://doi.org/10.1007/s11771-022-4927-7.
35.
ZhouM.DongH.WangF.-Y.WangQ.YangX.Modeling and Simulation of Pedestrian Dynamical Behavior Based on a Fuzzy Logic Approach. Information Sciences, Vol. 360, 2016, pp. 112–130. https://doi.org/10.1016/j.ins.2016.04.018.
36.
Dell’OrcoM.MarinelliM.OttomanelliM.Simulation of Crowd Dynamics in Panic Situations Using a Fuzzy Logic-Based Behavioural Model. In Computer-based Modelling and Optimization in Transportation (J. F.de SousaRossiR., eds.), Springer International Publishing, Cham, pp. 237–250.
37.
WangY.ChengL.LiS.Study on the Influence of Subway Passengers’ Non-Adaptive Behavior Based on Knowledge-Attitude-Practice Theory. Presented at the 2019 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), 2019.
38.
LiJ.YangL.QuY.SextonG.An Extended Takagi–Sugeno–Kang Inference System (TSK+) with Fuzzy Interpolation and Its Rule Base Generation. Soft Computing, Vol. 22, No. 10, 2018, pp. 3155–3170. https://doi.org/10.1007/s00500-017-2925-8.
39.
LiJ.LiuW.ZhangF.LiT.WangR.A Ship Fire Escape Speed Correction Method Considering the Influence of Crowd Interaction. Mathematics, Vol. 10, No. 15, 2022, p. 2749. https://doi.org/10.3390/math10152749.
40.
ArangoJ.MontufarJ.Walking Speed of Older Pedestrians Who Use Canes or Walkers for Mobility. Transportation Research Record: Journal of the Transportation Research Board, 2008. 2073: 79–85. https://doi.org/10.3141/2073-09.
41.
MaoQ.LiY.YuR.Simulation Study of Pedestrians Evacuation Considering Aggressive Behavior. Transportation Research Record: Journal of the Transportation Research Board, 2023. https://doi.org/10.1177/03611981231207843.
42.
RenX.XuH.ZhangJ.SongW.JiangK.A Speed-Based Model for Collaborative Movement Simulation of the Elderly Assisted by the Young. Transportmetrica A: Transport Science, 2023. https://doi.org/10.1080/23249935.2023.2182141.
