Adaptive evacuation systems use behavioral strategies to improve evacuation performance in complex environments. However, the optimization of these strategies has not been extensively studied in adaptive evacuation systems. In this paper, we study the best practices for optimizing evacuation plans using behavioral strategies.
To evaluate the effectiveness of such strategies, we conducted experiments in a real evacuation scenario. We tested the effectiveness of our approach against a standard evacuation scenario and an evacuation scenario incorporating external flows. We also tested the system’s ability to outperform the performance of an existing evacuation system. In the latter scenario, our approach outperformed the existing system in terms of time and evacuation efficiency. The results indicate that our approach can achieve superior performance even in scenarios with high utilization rates.
We used a DEM-based simulation to construct the simulation model. In this simulation model, we introduced a pseudo-radius to represent the personal space of the evacuees and a pseudo-spring to regulate the interaction among evacuees during the evacuation process. Moreover, we performed a sensitivity analysis to detect any potential anomalies.
The results indicated that the OPTIMAL configuration was the optimal choice. This configuration achieved a better balance between exit gates. Moreover, the number of decision changes increased exponentially with distance. In this context, the OPTIMAL configuration was also the most effective. Specifically, the total evacuation time was slightly shorter than the median, which is the best performance measure.
In the context of an adaptive evacuation system, it is important to understand how pedestrians interact with the system and what the optimal way to optimize their decisions is. For this reason, we performed a study on pedestrian behavior and its effects on evacuation performance. Moreover, we explored the effectiveness of exit-choice-changing strategies, such as the use of guide signs.
Our study showed that the optimal heuristic rule is a simple proportional swapping process between cells. This configuration is suitable for realistic scenarios with high pedestrian utilization rates. The optimal heuristic rule is a product of the optimization process described above. In addition, the OPTIMAL configuration produced a better balance between exit gates and increased the number of decision changes. Moreover, the optimal heuristic rule also produced a better overall time performance.
