Path planning for guided passengers during evacuation in subway station based on multi-objective optimization

Yang, Xiaoxia; Yang, Yi; Li, Yongxing; Yang, Xiaoli

doi:10.1016/j.apm.2022.07.024

Cited by 31 publications

(5 citation statements)

References 49 publications

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“…In a multitude of sectors within social production, there exists a series of multiobjective optimization problems (MOPs), such as job-shop scheduling [1] and the allocation of resources [2]. The objectives involved in these problems often exhibit mutual conflicts, leading typically to a spectrum of trade-off solutions rather than a singular solution that optimizes all objectives concurrently [3].…”

Section: Introductionmentioning

confidence: 99%

High-dimensional multi-objective evolutionary algorithm based on adaptive penalty parameters and improved association methods

Wang,

Xiang

et al. 2024

Preprint

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The balance between convergence and diversity is a crucial and challenging aspect of evolutionary multi-objective optimization. In order to balance them, this paper proposes a high-dimensional multi-objective evolutionary algorithm based on adaptive penalty parameters and an improved association method (MOEAAP). An adaptive penalty parameter is first proposed and utilized in non-dominated sorting based on reference points, allowing for the dynamic assignment of Pareto ranks to the population according to changes in iteration counts and objective dimensions. Additionally, an improved association method is employed to categorize candidate solutions into associated and non-associated solutions, while associated solutions are given preferential consideration. To verify the feasibility of the proposed algorithm, it is compared with several mainstream algorithms through simulation experiments, and is applied to multiple practical applications. Experimental results show the algorithm effectively balances convergence and diversity in high-dimensional, complex multi-objective optimization.

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Section: Introductionmentioning

confidence: 99%

High-dimensional multi-objective evolutionary algorithm based on adaptive penalty parameters and improved association methods

Wang,

Xiang

et al. 2024

Preprint

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“…For instance, Li and Guo [ 1 ] proposed a pedestrian boarding and alighting strategy to alleviate the congestion of bidirectional flow bottleneck caused by the boarding and alighting process on the platform. Yang et al [ 2 ] proposed to alleviate congestion in key areas of subway stations by adding guidance, and constructed the correlation between guidance and congestion through a multi-objective optimization model to provide the optimal guidance path. Wu et al [ 3 ] found that passenger flow line can be changed through railings, which can balance the utilization rate of each exit and relieve the congestion of exit bottlenecks.…”

Section: Introductionmentioning

confidence: 99%

Towards simulation optimization of subway station considering refined passenger behaviors

Wang,

Yuan,

Tong

et al. 2024

PLoS ONE

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The escalating passenger flow in subway systems presents significant challenges to station facilities during peak hours. Poorly designed station facilities can reduce passenger throughput efficiency and compromise passenger safety. This study conducts on-site investigations to extract refined parameters of passenger behaviors in security check and ticket checking areas. Using Beijing Subway Yizhuang Line Ciqunan Station as a case study, a microscopic simulation model is developed to replicate pedestrian flow within the subway station. By focusing on passenger demand and traffic organization, the layout of station facilities is regulated and optimized. After optimization, the passenger density in the security check and ticket inspection areas during the morning peak period decreased from 1.33 people/m2 to 1.00 people/m2; the longest queue length on the east side decreased from 15 people to 10 people, and the maximum queue length on the west side decreased from 7 people to 3 people. During peak hours, the dispersal time of passenger flow on the west side when entering the station decreased from 31.56 minutes to 30.04 minutes, and on the east side, it decreased from 36.12 minutes to 30.87 minutes. The optimization results effectively improved the efficiency of entering the station during peak hours.

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“…With the acceleration of urbanization, the urban population density is increasing, and more and more passengers are taking the subway to travel [1][2][3] . The contradiction between the safe and orderly operation of the subway and the growth of passenger travel demand is becoming more and more prominent.…”

Section: Introductionmentioning

confidence: 99%

“…Then combined with the camera to capture the passenger flow data in a unit of time in the station area, the passenger flow density data in the station is ultimately analyzed. (2) Passenger flow density data detection at the entry and exit of the station security checkpoint. This technology acquires the station's passenger flow data by installing infrared or laser detection equipment on both sides of the entry and exit doors of the station security checkpoint.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Metro Passenger Flow Monitoring and Guidance System

Liu

2024

HSET

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With the rapid development of big data, image analysis technology, and artificial intelligence, the related businesses of smart subways have been gradually realized. Facing the huge passenger flow during peak hours, how to guide passengers to relatively spacious compartments has become a hot issue of concern. This article utilizes big data processing and existing image analysis technology in combination with the original subway system to develop an intelligent subway passenger flow monitoring and guidance system. This system consists of three parts: multi-terminal collection, data analysis, and intuitive guidance. The system has the advantages of high reliability, low cost, and real-time guidance, which is expected to help improve passengers' ride experience and ensure the normal operation of the subway.

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Path planning for guided passengers during evacuation in subway station based on multi-objective optimization

Cited by 31 publications

References 49 publications

High-dimensional multi-objective evolutionary algorithm based on adaptive penalty parameters and improved association methods

High-dimensional multi-objective evolutionary algorithm based on adaptive penalty parameters and improved association methods

Towards simulation optimization of subway station considering refined passenger behaviors

Intelligent Metro Passenger Flow Monitoring and Guidance System

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