# ESCAPE: Evacuation Strategy through Clustering and Autonomous Operation in Public Safety Systems
## 1 Introduction
### Contributions and Outline  
The key contributions of our research work that differentiate it from the rest of the literature body are summarized as follows:  
1. A novel paradigm for distributed and autonomous evacuation planning is introduced, allowing the evacuees to make the most beneficial actions for themselves by using the proposed ESCAPE service, which can run on their mobile devices. The ESCAPE service was developed based on the principles of the reinforcement learning and game theory, and consists of two decision-making layers.
2. At the first layer, the evacuees acting as stochastic learning automata [6; 7; 8] decide which evacuation route they want to join based on their past decisions while performing the current evacuation, and taking the (limited) available information from the disaster area through the ESCAPE service, e.g., evacuation rate per route, evacuees already on the route, and capacity of the route. The latter information can easily be available in a real implementation scenario through sensors deployed at the evacuation routes. Based on evacuees' decisions, a cluster of them is created at each evacuation route. It is highlighted that this human decision expresses the evacuation route where the evacuee desires to go, and if they finally evacuate through the specific evacuation route is determined by the second layer of their decision.
3. At the second layer, the evacuees of each cluster have to determine if they will finally go or not toward the initially selected evacuation route. Given that the evacuees usually exhibit aggressive and non-co-operative behavior under the risk of their lives, the decision-making process at each cluster of each evacuation route is formulated as a non-co-operative game among the evacuees. It is also highlighted that evacuees' decisions are interdependent. The theory of minority games [9; 10] was adopted, allowing evacuees to dynamically and in a distributed fashion choose if they will finally go through the evacuation route, while considering the route's available free space (Distributed Exponential learning algorithm for Minority Games (DEMG)). The formulated minority game is solved using an exponential learning algorithm.
4. A distributed and low-complexity evacuation planning algorithm, named ESCAPE, is introduced to implement both decision-making layers of the evacuees. ESCAPE complexity is provided that demonstrates the low time overhead of the proposed evacuation planning, and therefore could be adopted and considered for realistic implementation.
5. Detailed numerical and comparative results demonstrate that the proposed holistic framework concludes to a promising solution for realizing a distributed and autonomous evacuation planning that confronts with the needs and requirements of both the evacuees and the Emergency Control Center.  
This paper is organized as follows. In Section 2, the overall system model, the ESCAPE methodology, and the related work are described, while, in Section 3, our proposed reinforcement-learning-based evacuation route selection is presented. Section 4 introduces the non-co-operative minority game among evacuees to finally determine their evacuation route. The distributed and low-complexity ESCAPE algorithm, as well as its complexity, are presented in Section 5. Finally, a detailed numerical evaluation of our approach via modeling and simulation is demonstrated in Section 6, while Section 7 concludes the paper.