Abstract:
For a given transportation network, evacuation route planning identifies routes and route schedules to
minimize the time to evacuate the vulnerable population due to an imminent disaster. In this paper, we
present a capacity-constrained routing optimization approach to maximize the total evacuees for short
notice evacuation planning in case of both natural and/or man-made disasters. The methodology is
scaled to a highly congested urban city with a transportation network capacity well below daily peak
demands. As the evacuation route planning is computationally challenging, an evacuation scheduling
algorithm was adopted to expedite the solution process. The algorithm uses Dijkstras algorithm to find
the shortest path(s) and a modified greedy algorithm to assign maximum flows to selected paths given a
specific schedule per time interval. A case study using real population and transportation network data
was tested using the proposed methodology. The results show that the city is in grave danger due to the
high population density and disproportionate network capacity.
