Simulation study of decision-making behavior in urban flood drainage and rescue operations based on ABM

To reduce the damage caused by urban waterlogging disasters and improve the efficiency of urban drainage emergency rescue operations, this study approaches from the perspective of human behavior strategies in traffic. Based on Agent-Based Modeling (ABM), it simulates the urban waterlogging traffic environment and drainage rescue behaviors, taking the Liangshui River Basin in Beijing as a case study. The simulation analyzes the impact of different traffic route selection decisions within 16 hours of waterlogging occurrence on the drainage rescue process, revealing the mechanism of its influence on rescue efficiency. The research results indicate that the path selection decision-making behavior for drainage rescue can effectively enhance the efficiency of rescue operations. As traffic congestion increases, avoiding congested routes according to behavior decision-making can significantly improve rescue efficiency in the model, increasing it by 12.8%. Furthermore, for nearby drainage rescue operations, decisions to avoid congested routes are superior to those based on the shortest path, whereas the opposite holds true for longer distances. The findings of this study are expected to provide substantial support for enhancing urban resilience against waterlogging disasters.