Simulation-based multi-objective optimization towards proactive evacuation planning at metro stations

Guo, Kai; Zhang, Limao; Wu, Maozhi

doi:10.1016/j.engappai.2023.105858

Cited by 7 publications

(5 citation statements)

References 38 publications

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“…R 2 , MSE (Mean Square Error), RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and MAPE (Mean Absolute Percentage Error) are used as assessment indices for the accuracy of the algorithm to predict the test set data. The formula is shown in Equations ( 11)-( 15) [58]:…”

Section: Determination Of Main Independent Variablesmentioning

confidence: 99%

Fire Egress System Optimization of High-Rise Teaching Building Based on Simulation and Machine Learning

Zhou

Zhang

et al. 2023

Fire

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A fire egress system is one of the most critical aspects of fire emergency evacuation, which is the cornerstone technology of building fire safety. The high-rise teaching buildings on campus, where vast crowds of people gather, need to be qualified for rapid evacuation in the event of a fire especially. Conventional teaching building egress system design places more emphasis on individual elements (e.g., stairwells, evacuation doors, and evacuation walkways) rather than on their co-regulation as a whole. Furthermore, there are not enough holistic and effective optimal design strategies, which is because most of the existing studies rely on experiments or simulations and often suffer from a lack of sufficient data to fully reveal the interactions of individual variables. In this study, the co-effectiveness of stairwells, walkways, and room doors in reducing total evacuation time was investigated by simulation and machine learning. We selected a typical high-rise teaching building as an example and integrated two simulation software, Pyrosim and Pathfinder, to compare the available safe evacuation time (ASET) and required safe evacuation time (RSET). Then, a framework consisting of five factors—stair flight width (SFW), stairwell door width (SDW), corridor width (CW), room door width (RDW), and location of the downward stair flight (LDSF)—was established for the optimization through statistical analysis of big data obtained by the preferred machine learning algorithm. Results indicate that (1) By modifying just one factor (SFW), the total evacuation time (TET) can be reduced by at most 12.1%, with the mortality rate dropping from 26.5% to 9.5%; (2) although ASET could not be achieved either, among 4000 cases of multi-factor combinations, a maximum TET improvement degree, 29.5%, can be achieved for the evacuation optimization compared to baseline model, with a consequent reduction in mortality to 0.15%; (3) it shows that the emphasis of the egress system optimization is on the geometric features of the evacuation stairwell; furthermore, the multi-factor combination approaches have better compromised evacuation performances than the single-factor controlled schemes. The research results can be applied as rational design strategies to mitigate fire evacuation issues in high-rise teaching buildings and, in addition, the methodology suggested in this paper would be suitable to other building types.

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Section: Determination Of Main Independent Variablesmentioning

confidence: 99%

Fire Egress System Optimization of High-Rise Teaching Building Based on Simulation and Machine Learning

Zhou

Zhang

et al. 2023

Fire

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“…Wang and Hou (2021) proposed a CF-based book recommendation method, considering the book's interest as an important measure, including factors like search frequency, borrowing time, borrowing frequency, borrowing interval, and renewal frequency. Guo et al (2023) proposed a recommendation method based on multi-factor random walk (MFRW), where MFRW calculated the current user's comprehensive trust value toward other users based on common friends, enhancing recommendation accuracy. Yin et al (2022) proposed a multimodal recommendation model that employed a dual attention mechanism to quantify investor preferences, used deep networks to learn project features, and combined CF mechanisms to model both aspects.…”

Section: Single Domain Book Recommendationmentioning

confidence: 99%

“…To verify that knowledge transfer learning can improve the recommendation performance of the proposed model, it was compared with two single-domain recommendation models, MF (Ruchitha, 2021) and MFRW (Guo et al, 2023), using only the Movie and Book datasets. The experiment used MAE and MSE as evaluation indicators.…”

Section: Effectiveness Of Knowledge Transfermentioning

confidence: 99%

A Cross-Domain Recommender System for Literary Books Using Multi-Head Self-Attention Interaction and Knowledge Transfer Learning

Cui,

Duan,

Zhang

et al. 2023

International Journal of Data Warehousing and Mining

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Existing book recommendation methods often overlook the rich information contained in the comment text, which can limit their effectiveness. Therefore, a cross-domain recommender system for literary books that leverages multi-head self-attention interaction and knowledge transfer learning is proposed. Firstly, the BERT model is employed to obtain word vectors, and CNN is used to extract user and project features. Then, higher-level features are captured through the fusion of multi-head self-attention and addition pooling. Finally, knowledge transfer learning is introduced to conduct joint modeling between different domains by simultaneously extracting domain-specific features and shared features between domains. On the Amazon dataset, the proposed model achieved MAE and MSE of 0.801 and 1.058 in the “movie-book” recommendation task and 0.787 and 0.805 in the “music-book” recommendation task, respectively. This performance is significantly superior to other advanced recommendation models. Moreover, the proposed model also has good universality on the Chinese dataset.

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“…Chen et al developed a cellular automata model considering the role of social forces during pedestrian evacuation inside a building, analyzed the effects of social forces on evacuation time, and found that aggregative attraction among crowds prolongs the evacuation time during the evacuation process [4] . Guo proposed a hybrid method combining building information modeling (BIM) and cellular automata modeling to achieve the combination of evacuation event simulation and active evacuation management that can minimize the evacuation time and completely clear the congested area [5] . Yuan proposed a cellular automata model with a high degree of discretization, set up a triangular obstacle floor field for simulating the emergency evacuation process in a room with obstacles, and studied the relationship between the pedestrian trajectory and obstacle [6] .…”

Section: Introductionmentioning

confidence: 99%

The Effect of Herding Behavior on the Efficiency of Pedestrian Evacuation in Subway Stations

Bao

2024

SSR

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In the emergency evacuation of pedestrians in subway stations, most people do not know the correct evacuation routes and methods, and tend to follow others blindly, resulting in herding behavior. To study the process of pedestrian evacuation with herding behavior in subway stations, a cellular automata pedestrian evacuation model is established to simulate the pedestrian evacuation with herding behavior in subway stations, and the effects of the weight coefficients of the parameters in the model on the overall evacuation time and the distribution of people’s positions are investigated to quantify the effects of herding on the efficiency of the pedestrian evacuation and the movement behavior. The simulation results show that moderate (kC < 40) crowd behavior can play a beneficial role in reducing reaction time and guiding the direction of evacuation; on the contrary, excessive crowd behavior (kC > 40) will lead to an increase in evacuation time and a decrease in evacuation efficiency. The conclusions presented in this paper can help to improve the efficiency of pedestrian evacuation in emergencies and provide a theoretical basis and practical guidance for the management of safety evacuation in subway stations.

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Simulation-based multi-objective optimization towards proactive evacuation planning at metro stations

Cited by 7 publications

References 38 publications

Fire Egress System Optimization of High-Rise Teaching Building Based on Simulation and Machine Learning

Fire Egress System Optimization of High-Rise Teaching Building Based on Simulation and Machine Learning

A Cross-Domain Recommender System for Literary Books Using Multi-Head Self-Attention Interaction and Knowledge Transfer Learning

The Effect of Herding Behavior on the Efficiency of Pedestrian Evacuation in Subway Stations

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