Why Evacuation Planning?

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Evacuation Route Planning Scalable Approaches
Shashi Shekhar McKnight Distinguished University Professor Director, AHPCRC University of Minnesota shekhar_at_cs.umn.edu 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
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Why Evacuation Planning?

• Lack of effective evacuation plans
• Traffic congestions on all highways
• Great confusions and chaos

Hurricane Andrew Florida and Louisiana, 1992

• "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)
( www.washingtonpost.com)
Hurricane Rita Gulf Coast, 2005
Hurricane Rita evacuees from Houston clog I-45.
( FEMA.gov)
( National Weather Services)
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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 )
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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

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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 gt 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

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Related Works Linear Programming Approach
G evacuation network
GT time-expanded network (T4)
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.)
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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)

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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

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A Real Scenario
Nuclear Power Plants in Minnesota
Twin Cities
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Minnesota Nuclear Power Plant Evacuation
Monticello Power Plant
Affected Cities
Evacuation Destination
AHPCRC
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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
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Handcrafted Existing Evacuation Routes
Destination
Monticello Power Plant
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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
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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
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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.

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Demographic Datasets

• Night time population
• Census 2000 data for Twin Cities Metro Area
• Source Met Council Datafinder (http//www.datafin
  der.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 gt Omits 10 of
  workers
• Does not include all travelers (e.g. students,
  shoppers, visitors).

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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
• 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 lt Location Center, Footprint Circles gt
• 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

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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
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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
Destinations nodes w/ evacuee assignment
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Evacuation routes on TP network
TP network
MnDOT basemap
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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
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Evacuation Routes for Large Scenario
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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

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Driving vs. Pedestrian Modes

Driving 100 Result
Walking 100 Result
Population Overwritten
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Daytime vs. Nighttime Population

Day Time Result
Night Time Result
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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!

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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

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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

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