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RETRACTED ARTICLE: Decision response of subway evacuation signs based on brain component features
- Source :
- Neural Computing and Applications. 34:6705-6719
- Publication Year :
- 2021
- Publisher :
- Springer Science and Business Media LLC, 2021.
-
Abstract
- Due to safety issues when passengers get on and off the subway and spend a lot of time on the subway, this makes subway station signs very important. Moreover, in case of fire and other dangerous situations and emergency evacuation, the guiding signs must be able to guide passengers to leave the station and dangerous areas efficiently and orderly, so as to protect the personal and property safety of passengers. The purpose of this study was to analyze the decision response of subway evacuation signs using the characteristics of the brain components. In this study, subway model is constructed. When you perform simulation using software, you need to fine tune the parameters to get the best simulation effect. A questionnaire survey was made on the components of the subway sign. The results show that the number of people who think that the standard font of the blackboard logo is the most representative of the emergency exit, accounting for 78.2% of the total number of people, taking the image as the first choice accounted for 52.9% of the total number of people, and the green sulfur powder logo as the first choice accounted for 69.8% of the total number. This study makes an important contribution to the research of subway traffic safety problems.
- Subjects :
- Subway station
Computer science
Questionnaire
Logo
030204 cardiovascular system & hematology
Transport engineering
03 medical and health sciences
0302 clinical medicine
Artificial Intelligence
Component (UML)
Emergency evacuation
Computational Science and Engineering
030217 neurology & neurosurgery
Software
Emergency exit
Subjects
Details
- ISSN :
- 14333058 and 09410643
- Volume :
- 34
- Database :
- OpenAIRE
- Journal :
- Neural Computing and Applications
- Accession number :
- edsair.doi...........5e6e9846ed55ba249db25e07df50e173