1. 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. 2 Why evacuation planning? Florida and Louisiana, 1992 No effective evacuation planning Traffic congestions on all highways Great confusions and chaos "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 ) ( National Weather Services) ( www.washingtonpost.com) Hurricane Andrew

3. 3  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 MapWeather Data Plume Dispersion Demographics Information Transportation Networks ( Images from www.fortune.com ) Homeland Defense and Evacuation Planning Motivation:

4. 4 Task 1 - Problem Statement 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

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

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

7. 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 Number of Nodes505005,00050,000 EVACNET Running Time0.1 min2.5 min108 min> 5 days

8. 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) CityPopulation Albertville city3,621 Annandale city2,684 Becker city2,673 Big Lake city6,063 Buffalo city10,097 Clear Lake city266 Clearwater city858 Elk River city16,447 Hasty City550 Maple Lake city1,633 Monticello city7,868 Rogers city3,588 St. Michael city9,099 Zimmerman city2,851 Total 68,298

9. 9 Task 4 - A Real Scenario(Monticello): Result Routes 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. (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.

10. 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 solutionFigure 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. 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 4 th weekend Fault tolerant evacuation plans, e.g. electric power failure

12. 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