Simulation-based passenger evacuation optimization in metro stations considering multi-objectives

• Main Authors: Guo, Kai, Zhang, Limao
• Other Authors: School of Civil and Environmental Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Civil engineering; Emergency Evacuation; Metro Stations
• Online Access: https://hdl.handle.net/10356/160750
• Institution: Nanyang Technological University

Evacuation is critical for safety management due to the highly overcrowded passengers in the metro stations. A simulation-based approach integrating Random Forest (RF) and Non-dominated Sorting Genetic Algorithm III (NSGA-III) is proposed to perform the evacuation evaluation and optimization at metro stations. A 3D model of the metro station is built to simulate the dynamic process of passenger evacuation in metro stations. A framework consisting of 9 influential factors and 3 objectives is developed to model the input-output relationship in passenger evacuation. An RF-based meta-model is used to construct the relationship between influential factors and objectives. At last, NSGA-III is applied to seeking the optimal solutions for the station renovation in order to achieve a safe evacuation. A station model simulating a real metro station in Singapore is constructed to test the effectiveness and applicability of the proposed approach. It is found that (1) A safe evacuation could be achieved for the station, but along with the increasing passenger volume and panic level, the requirement of evacuation objectives, the evacuation time and density, may not be met. Especially under the high passenger volume conditions, the passenger density could reach up to 6.2 unit/m2 (extremely dangerous); (2) An average improvement degree, 7.5%, can be achieved for the optimization of 20 test cases, and a maximum improvement degree, 22.5%, can be achieved for the evacuation optimization at metro stations; (3) It could be difficult to keep both of the evacuation time and density within the standards if one major exit is closed, even after the optimization. But a larger average improvement degree, 10.8%, can be achieved by the proposed optimization approach, which indicates the optimal solutions still could reduce the risk to a great extent. The novelty of this research lies in that (a) An RF algorithm is incorporated to build the meta-model that can properly represent the relationship between influential factors and objectives, despite the complexity and even conflicting between them; (b) Optimal measures for the evacuation improvement are provided from the MOO perspective by integrating NSGA-III. This hybrid approach can be used as a decision tool to assist regulatory authorities in developing effective emergency evacuation evaluation and optimization plans with adequate consideration of the complexity and multi-objective nature under evacuation events.