Why Evacuation Planning?

2. Why Evacuation Planning? Hurricane Andrew Florida and Louisiana, 1992 • Lack of effective evacuation plans • 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." Mayor Tim Mott, Morgan City, Louisiana ( http://i49south.com/hurricane.htm ) ( National Weather Services) Hurricane Rita Gulf Coast, 2005 ( www.washingtonpost.com) Hurricane Rita evacuees from Houston clog I-45. ( National Weather Services) ( FEMA.gov)

3. Homeland Defense & Evacuation Planning • Preparation of response to a chem-bio attack • Plan evacuation routes and schedules • Help public officials to make important decisions • Guide affected population to safety Base Map Weather Data Plume Dispersion Demographics Information Transportation Networks ( Images from www.fortune.com )

4. 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. Limitations of Related Work 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

6. Related Works: Linear Programming Approach G : evacuation network GT : time-expanded network (T=4) Step 1: Convert evacuation network into time-expanded network with user provided time upper bound T. Step 2: Use time-expanded network GT as a flow network and solve it using LP min. cost flow solver (e.g. NETFLO). (Source of network figures: H. Hamacher, S. Tjandra, Mathmatical Modeling of Evacuation Problems: A state of the art. Pedestrian and Evacuation Dynamics, pages 227-266, 2002.)

7. Proposed Approach • Existing methods can not handle large urban scenarios • Communities use manually produced evacuation plans • Key Ideas in Proposed Approach • Generalize shortest path algorithms • Honor road capacity constraints • Capacity Constrained Route Planning (CCRP)

8. 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

9. A Real Scenario Nuclear Power Plants in Minnesota Twin Cities

10. Minnesota Nuclear Power Plant Evacuation Monticello Power Plant Affected Cities Evacuation Destination AHPCRC

11. Monticello Emergency Planning Zone Emergency Planning Zone (EPZ) is a 10-mile radius around the plant divided into sub areas. Monticello EPZ Subarea Population 2 4,675 5N 3,994 5E 9,645 5S 6,749 5W 2,236 10N 391 10E 1,785 10SE 1,390 10S 4,616 10SW 3,408 10W 2,354 10NW 707 Total 41,950 Estimate EPZ evacuation time: Summer/Winter (good weather):  3 hours, 30 minutesWinter (adverse weather): 5 hours, 40 minutes Data source: Minnesota DPS & DHS Web site: http://www.dps.state.mn.us http://www.dhs.state.mn.us

12. Handcrafted Existing Evacuation Routes Destination Monticello Power Plant

13. A Real Scenario – Overlay of New Plan Routes Total evacuation time: - Existing Routes: 268 min. - New Routes: 162 min. Monticello Power Plant Source cities Destination Routes used only by old plan Routes used only by result plan of capacity constrained routing Routes used by both plans 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. Twin Cities

14. Project 2: Metro Evacution Planning (2005) Metro Evacuation Plan Evacuation Routes and Traffic Mgt. Strategies Evacuation Route Modeling Establish Steering Committee Identify Stakeholders Perform Inventory of Similar Efforts and Look at Federal Requirements Regional Coordination and Information Sharing Finalize Project Objectives Agency Roles Preparedness Process Stakeholder Interviews and Workshops Issues and Needs Final Plan

15. Road Networks • TP+ (Tranplan) road network for Twin Cities Metro Area • Source: Met Council TP+ dataset • Summary: • - Contain freeway and arterial roads with road capacity, travel time, • road type, area type, number of lanes, etc. • - Contain virtual nodes as population centroids for each TAZ. • Limitation: No local roads (for pedestrian routes) • 2. MnDOT Basemap • Source: MnDOT Basemap website (http://www.dot.state.mn.us/tda/basemap) • Summary: Contain all highway, arterial and local roads. • Limitation: No road capacity or travel time.

16. Demographic Datasets • Night time population • Census 2000 data for Twin Cities Metro Area • Source: Met Council Datafinder (http://www.datafinder.org) • Summary: Census 2000 population and employment data for each TAZ. • Limitation: Data is 5 years old; day-time population is different. • Day-time Population • Employment Origin-Destination Dataset (Minnesota 2002) • Source: MN Dept. of Employment and Economic Development • - Contain work origin-destination matrix for each Census block. • - Need to aggregate data to TAZ level to obtain: • Employment Flow-Out: # of people leave each TAZ for work. • Employment Flow-In: # of people enter each TAZ for work. • Limitation: Coarse geo-coding => Omits 10% of workers • Does not include all travelers (e.g. students, shoppers, visitors).

17. Scenarios • Sources: • Prioritized list of vulnerable facilities and locations in Twin Cities • Federal list of scenarios – Subset requiring evacuation • Input from advisory board and stakeholder workshop • Selected Scenarios • Downtown 1 • Downtown 2 • University • Mall • Suburban Facility • Scenario Specification for Evacuation Route Planning • Explicit • Source = < Location Center, Footprint Circles > • Destination = choice ( fixed locations OR outside a destination circle) • Implicit (Estimates from databases) with user overrides • Transportation Network – connectivity, capacities, travel times • Demand: Population estimates, mode preferences

18. Scenario 3: University

19. Test Scenario: State Fair Playground Evacuation Zone: Source Radius:1 mile Dest. Radius:1 mile Number of Evacuees: 19,649 (Estimated Daytime) Transportation mode: single occupancy vehicles Evacuation time: 1 hr 48 min Evacuation Zone 50 Destinations nodes w/ evacuee assignment Evacuation routes on TP+ network TP+ network MnDOT basemap

20. Scalability Test : Large Scenario Evacuation Zone: Source Radius:10 mile Dest. Radius:10 mile Number of Evacuees: 1.37 Million (Est. Daytime) Transportation mode: single occupancy vehicles Evacuation Zone TP+ network MnDOT basemap

21. Evacuation Routes for Large Scenario

22. Common Usages for the Tool • Compare options • Ex.: transportation modes • Walking may be better than driving for 1-mile scenarios • Ex.: Day-time and Night-time needs • Population is quite different • Identify bottleneck areas and links • Ex.: Large enclosed malls with sparse transportation network • Ex.: Bay bridge (San Francisco), • Designing / refining transportation networks • Address evacuation bottlenecks • A quality of service for evacuation, e.g. 4 hour evacuation time

23. Population Overwritten Driving 100% Walking 100%

24. Day Time Result Night Time Result

25. Limitations of the Tool • Evacuation time estimates • Approximate and optimistic • Assumptions about available capacity, speed, demand, etc. • Quality of input data • Population and road network database age! • Ex.: Rosemount scenario – an old bridge in the roadmap! • Data availability • Pedestrian routes (links, capacities and speed) • On-line editing capabilities • Taking out a link is not supported yet!

26. Conclusions • Evacuation Planning is critical for homeland defense • Existing methods can not handle large urban scenarios • Communities use manually produced evacuation plans • New CCRP 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

27. Acknowledgements • Organizations • 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