An efficient dynamic route optimization for urban flooding evacuation based on Cellular Automata

Flooding has been one of the major issues that have seriously hampered the developments of the urban systems, as well as threatened the urban water environments. To mitigate the impacts of floods, it is highly important to identify effective evacuation plans with the aid of computer-based methods. However, effectively identifying the dynamic evacuation route based on the evolution of flooding status is difficult and challenging. To this end, this study develops a Cellular Automata-based Dynamic Route Optimization (CADRO) algorithm to identify the dynamic flood evacuation route (FER), where the hydrodynamics, topography and human response time are incorporated. The proposed CADRO is applied in a suburb of Yangzhou City, China, and results demonstrate the people trapped rate, the mean and maximum length of FERs are shortened by nearly 32.70%, 34.04% and 7.90%, respectively compared with the traditional A* algorithm used for evacuation route optimization. One important feature of the proposed CADRO is that it can identify the dynamical variation of terrain connectivity within the flooding process, thereby offering the optimal FERs. In addition, the impacts of the human response time to the FERs are investigated in this study. It is anticipated that the proposed CADRO can be greatly beneficial to the urban flooding risk management.

Automated summary

Flooding is a major issue affecting urban systems and water environments. The study developed a dynamic route optimization algorithm called CADRO to identify flood evacuation routes, which showed significant improvements in efficiency compared to traditional methods. The algorithm considers hydrodynamics, topography, and human response time, and is expected to greatly benefit urban flooding risk management.