Routing Emergency Evacuees with Cognitive Packet Networks

Providing optimal and safe routes to evacuees in emergency situations requires fast and adaptive algorithms. The common approaches are often too slow to converge, too complex, or only focus on one aspect of the problem, e.g. finding the shortest path. This paper presents an adaptation of the Cognitive Packet Network (CPN) concept to emergency evacuation problems. Using Neural Networks, CPN is able to rapidly explore a network and allocate overhead in proportion to the perceived likelihood of finding an optimal path there. CPN is also flexible, as it can operate with any user-defined cost function, such as congestion, path length, safety, or even compound metrics. We compare CPN with optimal algorithms such as Dijkstra’s Shortest Path using a discrete-event emergency evacuation simulator. Our experiments show that CPN reaches the performance of optimal path-finding algorithms. The resulting side-effect of such smart or optimal algorithms is in the greater congestion that is encountered along the safer paths; therefore we indicate how the quality of service objective used by CPN can also be used to avoid congestion for further improvements in evacuee exit times.