Citation

Chen J, Yu J, Wen J, Zhang C, Yin Z, Wu J, Yao S. Int. J. Environ. Res. Public Health 2019; 16(23): e16234599.

Affiliation

Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China.

Copyright

(Copyright © 2019, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/ijerph16234599

PMID

31756961

Abstract

The timely and secure evacuation of an urban residential community is crucial to residents' safety when emergency events happen. This is different to evacuation of office spaces or schools, emergency evacuation in residential communities must consider the pre-evacuation time. The importance of estimating evacuation time components has been recognized for approximately 40 years. However, pre-evacuation time is rarely discussed in previous community-scale emergency evacuation studies. This paper proposes a new method that estimates the pre-evacuation time, which makes the evacuation simulation in urban residential communities more realistic. This method integrates the residents' pre-evacuation behavior data obtained by surveys to explore the influencing factors of pre-evacuation time and builds a predictive model to forecast pre-evacuation times based on the Random Forest algorithm. A sensitivity analysis is also conducted to find the critical parameters in evacuation simulations. The results of evacuation simulations in different scenarios can be compared to identify potential evacuation problems. A case study in Luoshanqicun Community, Pudong New District, Shanghai, China, was conducted to demonstrate the feasibility of the proposed method. The simulation results showed that the pre-evacuation times have significant impacts on the simulation procedure, including the total evacuation time, the congestion time and the congestion degree. This study can help to gain a deeper understanding of residents' behaviors under emergencies and improve emergency managements of urban communities.

Language: en

Keywords

Random Forest algorithm; evacuation simulation; pre-evacuation time; predictive model; residential community