A fast and multifactor evacuation method considering cumulative fatality rate based on deep reinforcement learning for urban toxic gas leakage-Reference-Cited by-同舟云学术

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A fast and multifactor evacuation method considering cumulative fatality rate based on deep reinforcement learning for urban toxic gas leakage

Published:2024-04 Issue: Volume:103 Page:105255
ISSN:2210-6707
Container-title:Sustainable Cities and Society
language:en
Short-container-title:Sustainable Cities and Society

Author:

Shao Xuqiang,Yang Haokang,Liu Zhijian,Li Mingyu,He Junzhou,Huang Jiancai,Hu Chenxing

Funder

Ministry of Education of the People's Republic of China

Hebei Provincial Natural Science Foundation

National Natural Science Foundation of China National Outstanding Youth Science Fund Project

Publisher

Elsevier BV

Reference47 articles.

1. Cellular automaton model for social forces interaction in building evacuation for sustainable society;Chen;Sustainable Cities and Society,2020

2. On-site evaluation of pedestrian-level air quality at a U-type street canyon in an ancient city;Cui;Journal of Wind Engineering and Industrial Aerodynamics,2017

3. Optimal design of building openings to reduce the risk of indoor respiratory epidemic infections;Dong;Building Simulation,2022

4. Study on influence of viaduct and noise barriers on the particulate matter dispersion in street canyons by CFD modeling;Hao;Atmospheric Pollution Research,2019

5. Social distancing detection with deep learning model;Hou,2020

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A double‐layer crowd evacuation simulation method based on deep reinforcement learning;Computer Animation and Virtual Worlds;2024-05

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