1. Evacuation Route Planning: Scalable Approaches
Shashi Shekhar
McKnight Distinguished University Professor
Director, AHPCRC
University of Minnesota
March 8th, 2006
Presentation at ITS Minnesota
Plans are of little importance, but planning is essential -- Winston Churchill
Plans are nothing; planning is everything.-- Dwight D. Eisenhower

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 ( )
( National Weather Services)
Hurricane Rita
Gulf Coast, 2005 (
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 )

4. Problem Statement Given Output Objective Constraints
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
Number of Nodes 50
500
5,000
50,000
EVACNET Running Time
0.1 min
2.5 min
108 min
> 5 days
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 , 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. Nuclear Power Plants in Minnesota
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 minutes Winter (adverse weather):  
5 hours, 40 minutes
Data source: Minnesota DPS & DHS Web site:

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
Identify Stakeholders
Establish Steering Committee
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 (
Summary: Contain all highway, arterial and local roads.
Limitation: No road capacity or travel time.

16. Demographic Datasets Night time population Day-time Population
Census 2000 data for Twin Cities Metro Area
Source: Met Council Datafinder (
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: Selected Scenarios
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
Minneapolis Central Business District (CBD)
St. Paul Central Business District (CBD)
University of Minnesota (East Bank)
Mall of America
Ashland Refinery
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. Night Population (Census 2000) Employment Flow-Out (2002)
Scenario 4: University of Minnesota
Source Radius (mile)
# of TAZs
Night Population (Census 2000)
Employment (Census 2000)
Employment Flow-Out (2002)
Employment Flow-In (2002) 1 11
18,373
36,373
4,851
34,897 2 53
93,151
213,911
37,081
201,143

19. Test Scenario: University of Minnesota
Evacuation Zone:
Source Radius: 1 mile
Dest. Radius: 1 mile
Number of Evacuees: 50,995 (Estimated Daytime)
Transportation mode: single occupancy vehicles
Evacuation time:
3 hr 41 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 vs. Pedestrian Modes
Driving 100% Result
Walking 100% Result
Population Overwritten

24. Daytime vs. Nighttime Population
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 Congresspersons and Staff
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