The behavior of stair/escalator/elevator selection of passengers at the subway platform could directly affect the travel efficiency and even the service level of the station. This paper proposes a stair/escalator/elevator selection model of passengers at the subway platform, where the cellular automata model is used to simulate passenger movement, and the fuzzy logic theory is used to describe selection behavior. The distance to the stair/escalator/elevator and the density of passengers in front of the facility are selected as the main influence factors. The rationality and effectiveness of the selection model are verified by comparing the field data with simulation results of passengers choosing different stairs/escalators/elevators. The relationship between passenger quantity, passenger speed, escalator quantity, escalator location, and the traffic rate is analyzed based on simulation results, which can provide a theoretical reference for the facility layout optimization at the platform floor of the subway station.
LiHWangYJiangJ, et al. Metro station evacuation safety assessment considering emergency response. Simulation2022; 98: 919–931.
2.
WuYXuJJiaL, et al. Estimation of emergency evacuation capacity for subway stations. J Transp Safe Sec2018; 10: 586–601.
3.
DingYYangLWengF, et al. Investigation of combined stairs elevators evacuation strategies for high rise buildings based on simulation. Simul Model Pract Theor2015; 53: 60–73.
4.
YangXZhangRLiY, et al. Passenger evacuation path planning in subway station under multiple fires based on multiobjective robust optimization. IEEE T Intel Transp Syst2022; 23: 21915–21931.
5.
YangXYangXWangQ, et al. Guide optimization in pedestrian emergency evacuation. Appl Math Comput2020; 365: 124711.
6.
CaoSZhangJSongW, et al. The stepping behavior analysis of pedestrians from different age groups via a single-file experiment. J Stat Mech: Theor Exp2018; 2018: 033402.
7.
CaoSWangPYaoM, et al. Dynamic analysis of pedestrian movement in single-file experiment under limited visibility. Commun Nonl Sci Num Simul2019; 69: 329–342.
8.
Mohd IbrahimAVenkatIDe WildeP, et al. The role of crowd behavior and cooperation strategies during evacuation. Simulation2022; 98: 737–751.
9.
HendersonLF. On the fluid mechanics of human crowd motion. Transp Res1974; 8: 509–515.
10.
LøvåsGG. Modeling and simulation of pedestrian traffic flow. Transp Res Part B: Methodol1994; 28: 429–443.
11.
LimGJZangenehSBaharnematiMR, et al. A capacitated network flow optimization approach for short notice evacuation planning. Eur J Oper Res2012; 223: 234–245.
12.
KuangHLiXSongT, et al. Analysis of pedestrian dynamics in counter flow via an extended lattice gas model. Phys Rev E2008; 78: 066117.
13.
KuangHLiXWeiY, et al. Effect of following strength on pedestrian counter flow. Chin Phys B2010; 19: 070517.
14.
LiXGuoFKuangH, et al. Effect of psychological tension on pedestrian counter flow via an extended cost potential field cellular automaton model. Phys A: Stat Mech Appl2017; 487: 47–57.
15.
LiXGengZKuangH, et al. Effect of dangerous source on evacuation dynamics in pedestrian counter flow. Phys A: Stat Mech Appl2019; 533: 122047.
16.
OkazakiS. A study of pedestrian movement in architectural space. Trans Archit Inst Jpn1979; 285: 137–147.
17.
WangXLiuTXuM. Bus bunching simulation based on the lattice Boltzmann model of traffic flow. Simulation. Epub ahead of print 3August2022. DOI: 10.1177/00375497221112915.
18.
YangXYangXXueS, et al. The effect of waiting area design at the metro platform on passengers’ alighting and boarding behaviors. Appl Math Comput2019; 358: 177–193.
19.
YangXYangXWangQ. Pedestrian evacuation under guides in a multiple-exit room via the fuzzy logic method. Commun Nonl Sci Num Simul2020; 83: 105138.
20.
YangXJiangHKangY, et al. Passenger management strategy and evacuation in subway station under covid-19. Chin Phys B2021; 31: 078901.
21.
YangXYangYLiY, et al. Path planning for guided passengers during evacuation in subway station based on multi-objective optimization. Appl Math Model2022; 111: 777–801.
22.
TajimaYNagataniT. Scaling behavior of crowd flow outside a hall. Phys A: Stat Mech Appl2001; 292: 545–554.
23.
SongPGaoYXueY, et al. Human behavior modeling for evacuation from classroom using cellular automata. IEEE Access2019; 7: 98694–98701.
24.
YangXYangXPanF, et al. The effect of passenger attributes on alighting and boarding efficiency based on social force model. Phys A: Stat Mech Appl2021; 565: 125566.
25.
YangXYangXLiY, et al. Obstacle avoidance in the improved social force model based on ant colony optimization during pedestrian evacuation. Phys A: Stat Mech Appl2021; 583: 126256.
26.
YangXZhangRPanF, et al. Stochastic user equilibrium path planning for crowd evacuation at subway station based on social force model. Phys A: Stat Mech Appl2022; 594: 127033.
27.
HuJYouLZhangH, et al. Study on queueing behavior in pedestrian evacuation by extended cellular automata model. Phys A: Stat Mech Appl2018; 489: 112–127.
28.
ZhengYLiXGJiaB, et al. Simulation of pedestrians’ evacuation dynamics with underground flood spreading based on cellular automaton. Simul Model Pract Theor2019; 94: 149–161.
29.
LiYYangXWangZ, et al. Lane-design for mixed pedestrian flow in t-shaped passage. Phys A: Stat Mech Appl2021; 589: 126593.
30.
SinghMKRamachandra RaoK. Selection of open or closed boundaries in a cellular automata model for heterogeneous non-lane-based traffic. Simulation2022; 98: 773–788.
31.
XieCTangTZhangB, et al. Experiment, model, and simulation of the pedestrian flow around a training school classroom during the after-class period. Simulation2022; 98: 63–82.
32.
ChenJMaJLoS. Event-driven modeling of elevator assisted evacuation in ultra high-rise buildings. Simul Model Pract Theor2017; 74: 99–116.
33.
ChenZZhangJLiD. Smoke control–discussion of switching elevator to evacuation elevator in high-rise building. Proc Eng2011; 11: 40–44.
34.
ZengWMiwaTTashiroM, et al. Exploring travelers’ route choice behavior from gps trajectories: a path size logit model with sampling of alternatives. In: Proceedings of the international choice modelling conference, 2017, http://www.icmconference.org.uk/index.php/icmc/ICMC2017/paper/view/1247
35.
LiTWuFPengL, et al. Study on passengers’ escalator-stair selection mode in subway station. J Transp Eng Inform2018; 16: 57–63.
36.
JiXZhangJRanB. A study on pedestrian choice between stairway and escalator in the transfer station based on floor field cellular automata. Phys A: Stat Mech Appl2013; 392: 5089–5100.
ZhuangYSchadschneiderAChengH, et al. Estimating escalator vs stairs choice behavior in the presence of entry railing: a field study. KSCE J Civil Eng2018; 22: 5203–5214.
39.
LiYJiaHLiJ, et al. Pedestrian choice behavior analysis and simulation of vertical walking facilities in transfer station. Chin Phys B2016; 25: 108901.
40.
MaJWangMLiL. A kinetic theory model of human crowds accounting for visual attention. Simulation2022; 98: 1039–1056.
41.
LiXGuoFKuangH, et al. An extended cost potential field cellular automaton model for pedestrian evacuation considering the restriction of visual field. Phys A: Stat Mech Appl2019; 515: 47–56.
42.
YangXZhangRLiY, et al. Fuzzy-theory-based pedestrian dynamics models for studying the waiting passenger distribution at the subway platform. Tunnel Undergr Space Technol2022; 129: 104680.
43.
ZhuN. Pedestrian evacuation with asymmetrical exit based on an improved cellular automata. In: Proceedings of the 2018 2nd international conference on applied mathematics, modelling and statistics application (AMMSA 2018), 2018, pp. 254–257, https://www.atlantis-press.com/proceedings/ammsa-18/25898308
44.
GaoFYanYChenZ, et al. Effect of density control in partially observable asymmetric-exit evacuation under guidance: strategic suggestion under time delay. Appl Math Comput2022; 418: 126838.
45.
RenHYanYGaoF. Variable guiding strategies in multi-exits evacuation: Pursuing balanced pedestrian densities. Appl Math Comput2021; 397: 125965.
46.
LiuTYangXWangQ, et al. A fuzzy-theory-based cellular automata model for pedestrian evacuation from a multiple-exit room. IEEE Access2020; 8: 106334–106345.
47.
BursteddeCKlauckKSchadschneiderA, et al. Simulation of pedestrian dynamics using a two-dimensional cellular automaton. Phys A: Stat Mech Appl2001; 295: 507–525.
48.
YangXWangQLiuT. A fuzzy-theory-based social force model for studying the impact of alighting area width on alighting and boarding behaviors. IEEE Access2020; 8: 28595–28606.
49.
TaherifarNHamedmoghadamHSreeS, et al. A macroscopic approach for calibration and validation of a modified social force model for bidirectional pedestrian streams. Transp A: Transp Sci2019; 15: 1637–1661.
50.
YangXDongHYaoX. Passenger distribution modelling at the subway platform based on ant colony optimization algorithm. Simul Model Pract Theor2017; 77: 228–244.
51.
ShiXXueSFelicianiC, et al. Verifying the applicability of a pedestrian simulation model to reproduce the effect of exit design on egress flow under normal and emergency conditions. Phys A: Stat Mech Appl2021; 562: 125347.
52.
XueSShiXJiangR, et al. Incentive-based experiments to characterize pedestrians evacuation behaviors under limited visibility. Safe Sci2021; 133: 105013.
53.
LiDHanB. Behavioral effect on pedestrian evacuation simulation using cellular automata. Safe Sci2015; 80: 41–55.
54.
WuYRongJLiuX, et al. Passengers distribution in urban rail transit platform before vehicle arrival. Beijing Gongye Daxue Xuebao (J Beijing Univ Technol)2012; 38: 875–879.
55.
HoogendoornSPBovyPH. Pedestrian route-choice and activity scheduling theory and models. Transp Res Part B: Methodol2004; 38: 169–190.
