Intelligent Evacuation Route Planning Algorithm Based on Maximum Flow

Li Liu, Huan Jin, Yangguang Liu and Xiaomin Zhang
Additional contact information
Li Liu: College of Digital Technology and Engineering, Ningbo University of Finance and Economics, Ningbo 315175, China
Huan Jin: Department of Computer Science, University of Nottingham Ningbo China, Ningbo 315100, China
Yangguang Liu: College of Finance and Information, Ningbo University of Finance and Economics, Ningbo 315175, China
Xiaomin Zhang: College of Digital Technology and Engineering, Ningbo University of Finance and Economics, Ningbo 315175, China

IJERPH, 2022, vol. 19, issue 13, 1-14

Abstract: This paper focuses on the problem of intelligent evacuation route planning for emergencies, including natural and human resource disasters and epidemic disasters, such as the COVID-19 pandemic. The goal of this study was to quickly generate an evacuation route for a community for victims to be evacuated to safe areas as soon as possible. The evacuation route planning problem needs to determine appropriate routes and allocate a specific number of victims to each route. This paper formulates the problem as a maximum flow problem and proposes a binary search algorithm based on a maximum flow algorithm, which is an intelligent optimization evacuation route planning algorithm for the community. Furthermore, the formulation is a nonlinear optimization problem because each route’s suggested evacuation time is a convex nonlinear function of the number of victims assigned to that route. Finally, numerical examples and Matlab simulations demonstrate not only the algorithm’s effectiveness, but also that the algorithm has low complexity and high precision. The study’s findings offer a practical solution for nonlinear models of evacuation route planning, which will be widely used in human society and robot path planning schemes.

Keywords: evacuation routing; network flow algorithm; artificial intelligence; route planning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/13/7865/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/13/7865/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:13:p:7865-:d:848685

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().