Analysis of Fire Characteristics and Emergency Evacuation Influencing Factors in Urban Railway Transportation

DOI

10.2991/978-94-6463-514-0_84How to use a DOI?

Keywords

component; urban railway; fire risk; emergency evacuation; random forest

Abstract

This paper addresses the characteristics of urban rail transit fires and proposes a predictive model combining evacuation simulation and random forests for rapid forecasting of evacuation targets. Firstly, the characteristics of urban rail transit fires and key factors affecting evacuation are analyzed. Secondly, a three-dimensional model for crowd evacuation simulation is constructed. Finally, a predictive model based on random forests is developed, with an analysis of the importance of predictive variables. The random forest approach can effectively deal with complex nonlinear relationships and large amounts of data, and improve the prediction accuracy and robustness of the model. The results indicate that this method can rapidly predict emergency evacuation scenarios in urban rail transit fires, achieving an accuracy of 95%.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zhiming Zhang
AU  - Hui Dou
AU  - Qingqing Wang
AU  - Yong Wang
AU  - Xiangfei Yang
AU  - Qianwan Yu
PY  - 2024
DA  - 2024/09/28
TI  - Analysis of Fire Characteristics and Emergency Evacuation Influencing Factors in Urban Railway Transportation
BT  - Proceedings of the 2024 7th International Symposium on Traffic Transportation and Civil Architecture (ISTTCA 2024)
PB  - Atlantis Press
SP  - 874
EP  - 881
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-514-0_84
DO  - 10.2991/978-94-6463-514-0_84
ID  - Zhang2024
ER  -