1 evacuation route planning: scalable approaches shashi shekhar mcknight distinguished university...
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Evacuation Route Planning: Scalable Approaches
Shashi ShekharMcKnight Distinguished University Professor
Director, AHPCRCUniversity of [email protected]
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
"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 )
( FEMA.gov)
Hurricane Rita evacuees from Houston clog I-45.
( www.washingtonpost.com)
Hurricane AndrewFlorida and Louisiana, 1992
Hurricane RitaGulf Coast, 2005
( National Weather Services)
( 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 safetyBase 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
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 Nodes 50 500 5,000 50,000
EVACNET Running Time 0.1 min 2.5 min 108 min > 5 days
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Related Works: Linear Programming Approach
Step 1: Convert evacuation network into time-expanded network with user provided time upper bound T.
G : evacuation network
Step 2: Use time-expanded network GT as a flow network and solve it using LP min. cost flow solver (e.g. NETFLO).
GT : time-expanded network (T=4)
(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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30 50 70 90 100
Source Node Ratio (%)
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Tim
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SRCCP
MRCCP
EVACNET
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30 50 70 90 100
Source Node Ratio (%)
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SRCCP
MRCCP
EVACNET
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A Real Scenario
Nuclear Power Plants in Minnesota
Twin Cities
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Minnesota Nuclear Power Plant Evacuation
Affected Cities
Monticello Power Plant
Evacuation Destination
AHPCRC
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Monticello Emergency Planning Zone
Monticello EPZSubarea Population2 4,675 5N 3,9945E 9,6455S 6,7495W 2,23610N 39110E 1,78510SE 1,39010S 4,616 10SW 3,40810W 2,35410NW 707Total 41,950
Estimate EPZ evacuation time: Summer/Winter (good weather): 3 hours, 30 minutesWinter (adverse weather): 5 hours, 40 minutes
Emergency Planning Zone (EPZ) is a 10-mile radius around the plant divided into sub areas.
Data source: Minnesota DPS & DHS Web site: http://www.dps.state.mn.us
http://www.dhs.state.mn.us
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Destination
Monticello Power Plant
Handcrafted Existing Evacuation Routes
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A Real Scenario – Overlay of New Plan 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
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
Total evacuation time:
- Existing Routes: 268 min.
- New Routes: 162 min.
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Project 2: Metro Evacution Planning (2005)
Agency Roles
Identify Stakeholders
Establish Steering
Committee
Perform Inventory of
Similar Efforts and Look at
Federal Requirements Finalize
Project Objectives
Regional Coordination
and Information
Sharing
Metro Evacuation Plan
Stakeholder Interviews and
Workshops
Evacuation Route
Modeling
Evacuation Routes and Traffic Mgt.
Strategies
Issues and Needs
Final Plan
Preparedness Process
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Road Networks
1. 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
1. 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.
2. 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).
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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
1. Minneapolis Central Business District (CBD)
2. St. Paul Central Business District (CBD)
3. University of Minnesota (East Bank)
4. Mall of America
5. 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
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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
Scenario 4: University of Minnesota
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Evacuation time:3 hr 41 min
Evacuation Zone:Source Radius: 1 mileDest. Radius: 1 mile
Number of Evacuees: 50,995 (Estimated Daytime)
Transportation mode: single occupancy vehicles
Evacuation Zone
Destinations nodes w/ evacuee assignment
MnDOT basemap
TP+ network
Evacuation routes on TP+ network
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Test Scenario: University of Minnesota
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Evacuation Zone:Source Radius: 10 mileDest. Radius: 10 mile
Number of Evacuees: 1.37 Million (Est. Daytime)
Transportation mode: single occupancy vehicles
Evacuation Zone
MnDOT basemap
TP+ network
Scalability Test : Large Scenario
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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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