# ESCAPE: Evacuation Strategy through Clustering and Autonomous Operation in Public Safety Systems
## 2 System Model and Overview
### Related Work  
Several centralized approaches have been proposed in the literature to deal with the evacuation-planning problem, where all information of the disaster area's status (e.g., available evacuation routes, number of evacuees, evacuation routes' congestion levels) is gathered to a central decision-making entity, i.e., an Emergency Control Center (ECC) [12]. The authors in [13] developed an evacuation service named MacroServ to support the transportation departments simulate an emergency evacuation by recommending to the evacuees the most preferable routes toward safe locations during a disaster. The authors adopted the Poisson and Weibull distributions to quantify various stochastic factors that influence their model, such as behavior of evacuees while driving, and rate of evacuees departing from an area. The problem of contraflow for evacuation is studied in [14], where a transportation network with edges of specific capacity and travel time is considered. The authors determined a reconfigured network with the ideal direction for each edge toward minimizing evacuation time by reallocating each edge's capacity. A similar problem was discussed in [15] where a multiple-objective optimization problem was formulated while considering evacuation priorities and setup time for the contraflow operation. Evacuation planning due to fire propagation was examined in [16], where the authors developed a probabilistic model to characterize how the disaster affects crowd behavior and, in turn, evacuation time.  
An analytic system-optimal dynamic traffic-assignment model with probabilistic demand and capacity constraints was proposed in [17] to capture the evacuation process, while stochastic programming was used as the solution method. Following a similar philosophy, a distributed-constraint optimization algorithm was introduced in [18] to optimize human evacuation timing by estimating their location. A centralized optimization problem was formulated in [19] aiming at minimizing total clearance time of the disaster area and the travel time of each evacuee under the constraints of avoiding traffic congestion and balancing traffic loads between different evacuation routes. Another system-oriented approach was discussed in [20], targeting evacuation traffic management at intersections of the disaster area by presenting a simulation-based framework to maximize evacuation efficiency with uncertain budget constraints of the Emergency Control Center. A heuristic algorithm was introduced in [21], which considered the disaster area as  
Figure 2: Autonomous evacuation-planning framework as a learning system.  
a transportation network and dynamically selected evacuation routes with a maximum flow rate to provide a high-velocity evacuee stream. A human-centric approach was followed in [22] by introducing a quality-of-service routing algorithm to accommodate the needs of different types of evacuees based on age, mobility, and level of resistance to hazards. In [23], the authors proposed a resource-allocation algorithm based on random neural networks to allocate the first responders to those evacuees whose health conditions have deteriorated beyond a certain level in the course of an evacuation.