TY  - JOUR
CY  - Hanoi
VL  - 5357 L
Y1  - 2008///
AV  - none
A1  - Rahman, A.
A1  - Mahmood, A.K.
A1  - Schneider, E.
SP  - 357
KW  - Agents; Artificial intelligence; Behavioral research; Human engineering; Intelligent agents; Optimization; Sensitivity analysis; Tall buildings
KW  -  Ant colony optimization; Cognitive behavior; Evacuation planning; Multi-agent simulation; Prometheus methodology
KW  -  Multi agent systems
EP  - 369
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-58449113033&doi=10.1007%2f978-3-540-89674-6_40&partnerID=40&md5=a25a2b38111285d1bd2e8c6def5e5a5d
ID  - scholars411
N2  - Human factors play a significant part in the time taken to evacuate due to an emergency. An agent-based simulation, using the Prometheus methodology (SEEP 1.5), has been developed to study the complex behavior of human (the 'agents') in high-rise building evacuations. In the case of hostel evacuations, simulation results show that pre-evacuation phase takes 60.4 of Total Evacuation Time (TET). The movement phase (including queuing time) only takes 39.6 of TET. From sensitivity analysis, it can be shown that a reduction in TET by 41.2 can be achieved by improving the recognition phase. Emergency exit signs have been used as smart agents. Modified Ant Colony Optimization (ACO) was used to determine the feasibility of the evacuation routes. Both wayfinding methods, the 'familiarity of environment', which is the most natural method, and the ACO method have been simulated and comparisons were made. In scenario 1, where there were no obstacles, both methods achieved the same TET. However, in scenario 2, where an obstacle was present, the TET for the ACO wayfinding method was 21.6 shorter than the one for the 'familiarity' wayfinding method. © 2008 Springer Berlin Heidelberg.
TI  - Using agent-based simulation of human behavior to reduce evacuation time
N1  - cited By 7; Conference of 11th Pacific Rim International Conference on Multi-Agents, PRIMA 2008 ; Conference Date: 15 December 2008 Through 16 December 2008; Conference Code:75114
SN  - 03029743
JF  - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ER  -