Capacity Constrained Routing Algorithms for Evacuation Route Planning

Abstract

Evacuation route planning identifies paths in a given transportation network to minimize the time needed to move vulnerable populations to safe destinations. Evacuation route planning is critical for numerous important applications like disaster emergency management and homeland defense preparation. It is computationally challenging because the number of evacuees often far exceeds the capacity, (ie.) the number of people that can move along the road segments in a unit time. Linear Programming(LP) based methods using time expanded networks can take hours to days of computation for metropolitan sized problems. In this paper, we propose a new approach, namely a capacity constrained routing planner which models capacity as a time series and generalizes shortest path algorithms to incorporate capacity constraints. We characterize the design space for reducing the computational cost. Analytical cost model and experiment results show that the proposed algorithm is faster than the LP based algorithms and requires less memory. Experimental evaluation using various network configurations also shows that the proposed algorithm produces solutions that are comparable to those produced by LP based algorithms while significantly reducing the computational cost.