Optimal Installation Location of Escape Route Signs at T-Type Intersections

Young-Hoon Bae,Jong-Yeong Son,Ryun-Seok Oh,Hye-Kyoung Lee,Yoon-Ha Lee,Won-Hwa Hong,Jun-Ho Choi

Sustainability（2021）SCI 4区

Kyungpook Natl Univ | Natl Disaster Management Res Inst | Pukyong Natl Univ | Changshin Univ

Cited 3|Views4

Abstract

This study analyzed the decision-making times (DMTs) of participants at T-type indoor intersections according to the horizontal/vertical installation locations and the arrow directions of escape route signs. A total of 120 university students participated in the study. We analyzed the DMTs and following rates (FRs) required for the participants to observe the visual stimuli of the signs installed in front of the T-type indoor intersections and then properly select a path according to the arrow direction of the signs. The results are as follows: (1) the participants exhibited shorter DMTs for the right arrow direction of the signs, (2) the Simon effect occurred when the horizontal installation location of the signs was more than 60 cm away from the center of the T-type indoor intersection on both sides, (3) the DMTs of participants increased when the vertical installation location of the signs was low. Finally, we proposed an optimal installation location of the signs to support the shortest DMTs at T-type indoor intersections. It is expected that the results of this study will provide a database of DMTs, based on the locations of the signs during emergency evacuations, and will be utilized to improve the installation guidelines and regulations of signs.

Translated text

Key words

escape route signs,decision-making times,virtual stimuli,T-type intersection,optimal installation location

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper