Real-Time Deep Reinforcement Learning for Evacuation Under Emergencies

Zhou, Yujing; Yang, Yupeng; Liu, Dahai; Liu, Yongxin; Namilae, Sirish; Song, Houbing

i

Real-Time Deep Reinforcement Learning for Evacuation Under Emergencies

2024-01-01
By Zhou, Yujing ; Yang, Yupeng ; Liu, Dahai

File Language:

English

Details

Creators:

Zhou, Yujing ; Yang, Yupeng ; Liu, Dahai ; Liu, Yongxin ; Namilae, Sirish ; Song, Houbing
Corporate Creators:

Embry-Riddle Aeronautical University ; Center for Advanced Transportation Mobility, North Carolina A&T State University
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:
Publication/ Report Number:

CATM-2024-R1-ERAU
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Covered Final Report: 10/01/2021-12/31/2023
Corporate Publisher:

Center for Advanced Transportation Mobility, North Carolina A&T State University
Abstract:

This study adopted an A3C algorithm to simulate the evacuation process under different situations (e.g., multiple agents and different environmental conditions) and results were compared with Deep Q-Networks (DQN), to demonstrate the efficiency and effectiveness of A3C algorithm use in evacuation models. Results indicated that under static environments, A3C demonstrated superior adaptability and quicker response times. Furthermore, with an increasing number of agents, A3C showed better scalability and robustness when managing complex interactions and provided quick evacuations. These outcomes highlight A3C's advantage over traditional RL models under varying and challenging conditions. The report concludes with a discussion of the practical implications and benefits of these models. It emphasizes their potential in enhancing real-world evacuation planning and safety protocols.
Format:

PDF
Funding:

69A3551747125
Download URL:

https://rosap.ntl.bts.gov/view/dot/77393/dot_77393_DS1.pdf
File Type:

[PDF - 913.78 KB]
Collection(s):

University Transportation Centers
Main Document Checksum:

urn:sha-512:2e071e814eef507f46b1e467027bdf50fa99d9d6b62ed910e12a19146ffd7004ad64024472bc2b6d41da101b96ac899bb14d8018b1c03f6415ed4a39b957dee4

File Language:

English

ON THIS PAGE

Details

Found an issue?

Send us an email at:

publishinghd@cdc.gov

ROSA P serves as an archival repository of USDOT-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by USDOT or funded partners. As a repository, ROSA P retains documents in their original published format to ensure public access to scientific information.