A Cellular Automaton Model for Exit Selection Behavior Simulation during Evacuation Processes. (2018)

• Record Type: Journal Article
• Authors: Fu, Libi; Fang, Jie; Cao, Shuchao; Lo, Siuming
• Is Part Of: Procedia engineering. Volume 211(2018)
• Page Start: 169
• Page End: 175
• Publication Date: 2018
• Subjects: pedestrians -- exit selection -- model -- simulation -- evacuation; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Conference proceedings; Periodicals; 620.005
• DOI: 10.1016/j.proeng.2017.12.123
• Languages: English
• ISSNs: 1877-7058

Abstract

Abstract: Exit selection behavior is important for evacuation processes and safe facility design. Pedestrians' ability to select an exit route is affected by many factors, and will also impact on evacuation efficiency. It should not be neglected when modeling evacuation. In the case of asymmetric exits and distribution of pedestrians, an important issue is how to identify a pedestrian's target when performing simulations. However, this has not been well investigated, especially in discrete models. In this regard, we tend to investigate pedestrian exit choice behavior by integrating the least effort algorithm with a cellular automaton model. The distance to exits and crowd density around exits are involved. Simulations are conducted in a two-exit room. Evacuation time in scenarios where there are some special distributions of pedestrians is compared with that in scenarios where pedestrians are randomly distributed. The influence of the weighted value of the distance to exits or crowd density around exits on evacuation time is also studied. Useful suggestions are provided. The effect of locations of exits on evacuation time and the cumulative number of egress pedestrians are further investigated. Results demonstrate that two exits located in different walls, especially in symmetrical walls, are helpful in evacuation processes. This result is in line with that in other references. It is hoped that this work will be helpful in improving evacuation rules of discrete models in multi-exit situations.