Evacuation Planning Algorithms Professor Shashi Shekhar

1. Evacuation Planning Algorithms Professor Shashi Shekhar Dept. of Computer Science, University of Minnesota Participants: Q. Lu, S. Kim February 2004 Sponsors: AHPCRC, Army Research Lab. CTS, MnDOT, Federal Highway Authority

2. Why evacuation planning? Hurricane Andrew Florida and Louisiana, 1992 • No effective evacuation planning • Traffic congestions on all highways • Great confusions and chaos ( National Weather Services) "We packed up Morgan City residents to evacuate in the a.m. on the day that Andrew hit coastal Louisiana, but in early afternoon the majority came back home. The traffic was so bad that they couldn't get through Lafayette." - Morgan City, Louisiana Mayor Tim Mott ( http://i49south.com/hurricane.htm ) ( www.washingtonpost.com)

3. Homeland Defense and Evacuation Planning Motivation: • Preparation of response to a chem-bio attack or natural disaster • Plan evacuation routes and schedules after plume simulation of toxic agents • Guide affected population and public officials • Real-time or Non real-time planning Base Map Weather Data Plume Dispersion Demographics Information Transportation Networks ( Images from www.fortune.com )

4. Given Transportation network with capacity constraints Initial number of people to be evacuated and their initial locations Evacuation destinations Output Routes to be taken and scheduling of people on each route Objective Minimize total time needed for evacuation Minimize computational overhead Constraints Capacity constraints: evacuation plan meets capacity of the network Task 1 - Problem Statement

5. Task 1 - A Real Scenario (1): Minnesota Nuclear Power Plant Evacuation Route Planning (Monticello) Monticello Power Plant Affected Cities Evacuation Destination AHPCRC

6. Task 1 -A Real Scenario (1): Handcrafted Existing Evacuation Routes Destination Monticello Power Plant

7. Task 2 - Related Works Linear Programming Approach - Optimal solution for evacuation plan - e.g. EVACNET (U. of Florida), Hoppe and Tardos (Cornell University). Limitation: - High computational complexity - Cannot apply to large transportation networks Capacity-ignorant Approach - Simple shortest path computation - e.g. EXIT89(National Fire Protection Association) Limitation: - Do not consider capacity constraints - Very poor solution quality

8. Task 4 - A Real Scenario: Monticello Power Plant Evacuation Transportation Network: Nodes: 115 Edges: 153 Sources: 14 cities within 20 miles range of Monticello Destination: 1 (Osseo City) • Population : Census 2000,total population by city(Source: US Census Bureau) • City Population • Albertville city 3,621 • Annandale city 2,684 • Becker city 2,673 • Big Lake city 6,063 • Buffalo city 10,097 • Clear Lake city 266 • Clearwater city 858 • Elk River city 16,447 • Hasty City 550 • Maple Lake city 1,633 • Monticello city 7,868 • Rogers city 3,588 • St. Michael city 9,099 • Zimmerman city 2,851 • Total 68,298

9. Task 4 - A Real Scenario(Monticello): Result Routes (a) Old plan uses orange routes, e.g. State Highway 95, going far away from destination, increase total evacuation time. (b) Old plan puts high load on highways I-94 and US-10. Our plan uses more roads to reduce congestion. Source cities Destination Monticello Power Plant Routes used only by old plan Routes used only by result plan of capacity constrained routing Routes used by both plans (c) Congestion is likely in old plan near evacuation destination due to capacity constraints. Our plan has richer routes near destination to reduce congestion and total evacuation time.

10. Task 4 - Performance Evaluation Experiment 1: Effect of People Distribution (Source node ratio) Results: Source node ratio ranges from 30% to 100% with fixed occupancy ratio at 30% Figure 1 Quality of solution Figure 2 Running time • SRCCP produces solution closer to optimal when source node ratio goes higher • MRCCP produces close-to-optimal solution with half of running time of optimal algorithm • Distribution of people does not affect running time of proposed algorithms when total number of people is fixed

11. Conclusions • Evacuation Planning is critical for homeland defense • Existing methods can not handle large urban scenarios • Communities use manually produced evacuation plans • Our new GIS algorithms • Can produce evacuation plans for large urban area • Reduce total time to evacuate! • Improves current evacuation plans • Next Steps: • More scenarios: contra-flow, downtowns e.g. Washington DC • Dual use – improve traffic flow, e.g. July 4th weekend • Fault tolerant evacuation plans, e.g. electric power failure

12. Acknowledgements • Federal Agencies • AHPCRC, Army Research Lab. • Dr. Raju Namburu • CTS, MnDOT, Federal Highway Authority • National Science Foundation • Congresspersons and Staff • Rep. Martin Olav Sabo • Staff persons Marjorie Duske • Rep. James L. Oberstar • Senator Mark Dayton