Self-calibrating Passenger Flow Simulation and Spatial Optimization for Public Building Based on GRU-SA

doi:10.16182/j.issn1004731x.joss.24-0704

Abstract

Abstract:

Real-time and precise passenger flow simulation provides critical data support for the optimal allocation of resources in public building facilities and the rational design of spatial layouts. This study proposed a self-calibrating passenger flow simulation and spatial optimization method for public buildings based on the GRU-simulated annealing algorithm. A simulation model incorporating spatial structures and flow lines was constructed using Anylogic. A self-calibrating passenger flow simulation method for public buildingswas designed based on the GRU-simulated annealing algorithm and applied to the outpatient department of a hospital in Shanghai for passenger flow simulation. The effectiveness of the method was validated by comparing the simulation results with actual data. Spatial layout optimization schemes were proposed to address passenger flow congestion issues observed during the simulation, and the optimization results were visualized. An optimization metric systemwas developed to assess the effectiveness of the optimization. Practical application demonstrates that this method can effectively identify bottlenecks in the spatial layout of public building facilities. After implementing targeted layout optimization schemes, queuing time is reduced by 24.14%; the service level of the facilities is significantly improved, and passenger density is decreased by 58.93%, thereby mitigating passenger gathering risks and reducing overall infection hazards.

Key words: public building, passenger flow simulation, spatial layout optimization, self-calibrating, simulated annealing, GRU neural network

CLC Number:

TP391.9

Xu Jinglin, Chen Qianru, Peng Yang, Yu Fangqiang. Self-calibrating Passenger Flow Simulation and Spatial Optimization for Public Building Based on GRU-SA[J]. Journal of System Simulation, 2025, 37(11): 2826-2838.

Figures/Tables 16

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Table 1

Monitoring data of passenger flow at entrance

出入口名	设备编号	客流数量 $N j$ /人	采集间隔时间 $t j$ /min	到达率 $λ j$
出入口1	3381	60	5	12
出入口2	1471	8	5	1.6

Table 1

Table 2

Business Process Data

业务环节区域	业务处理时长 $t K$	事件概率 $P K$
挂号窗口	5	(0,1)
缴费/取报告自助服务设施	5	(0,2)
取药窗口	5	(0,1)
抽血检查	16	(0,1)

Table 2

Table 3

Fig. 5

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Table 4

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编号	业务环节区域K	仿真客流密度	实际客流密度
1	抽血检查	0.157	0.230
2	挂号窗口	0.417	0.327
3	缴费/取报告自助服务设施	0.560	0.640
4	取药窗口	0.230	0.188

算法	客流密度最优误差	计算时长/s
SA	0.08	32.718
GRU-SA	0.07	9.3118