D ELDOT S TATEWIDE E VACUATION M ODEL
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Transcript of D ELDOT S TATEWIDE E VACUATION M ODEL
2007 TRB Planning Application Conference
DELDOT STATEWIDE EVACUATION MODELDELDOT STATEWIDE EVACUATION MODEL
BACKGROUNDBACKGROUND
In 2006 the Delaware Department of Transportation began updating hurricane evacuation plans for Sussex, Kent, and New Castle Counties. They wanted to develop estimates for the durations of evacuations within each of these counties.
Need for Evacuation Planning
Delaware is located on the Delmarva Peninsula, situated between the Atlantic Ocean, Delaware Bay, and Chesapeake Bay
Their prime beach areas are located about 2 ½ hours from Washington, D.C. and Baltimore, MD
BACKGROUNDBACKGROUND
Need for Evacuation Planning
Two of the primary routes on and off of the Peninsula are bridges susceptible to congestion and closure during storm events.
BACKGROUNDBACKGROUND
Photo by Scott Kozel
BACKGROUNDBACKGROUND
Need for Evacuation Planning
Fortunately, Delaware does not have much history of large-scale evacuations and not much documentation of how long it would take to evacuate.
BackgroundBackground
Today we will use a Sussex County evacuation as a case study.
Maryland
Virginia
New Jersey
Pennsylvania
Delaw
are Bay
Chesapeake B
ay
Virginia
Lewes
Rehoboth Beach
Fenwick Island
Bethany Beach
Ocean City
0 6 12 18 243Miles
Legend
Tourist Area
Study Area
BACKGROUNDBACKGROUNDNeed for Evacuation Planning
Over 50% of the Sussex County Summer Population are not full-time residents
Sussex County has no multi-lane east-westroadways and nolimited-accesshighways
BACKGROUNDBACKGROUNDThe Delaware Department of Transportation set out to develop a model that would estimate the evacuation durations of different “what would happen if…” scenarios based on:
Behavioral Assumptions
Policy Decisions
Operational Assumptions
BACKGROUND – Behavioral Assumptions
BACKGROUND – Behavioral Assumptions
At what pace do people evacuate?
How many people evacuate?
Where would people evacuate to?
BACKGROUND – Behavioral Assumptions
BACKGROUND – Behavioral Assumptions
At what pace to people evacuate?
BACKGROUND – Behavioral Assumptions
BACKGROUND – Behavioral Assumptions
How many people evacuate?A 2003 report by the US Army Corps of Engineers estimates that:
Between 45% and 80% of full-time residents would evacuate if ordered to
Between 90% and 95% of vacationers would evacuate if ordered to
Between 65% and 70% of the vehicles available to Evacuating Households would evacuate
BACKGROUND – Behavioral Assumptions
BACKGROUND – Behavioral Assumptions
Where do they evacuate to?
Full-Time Residents 20% stay in their neighborhood
25% would evacuate within Sussex County
25% would stay on the Delmarva Peninsula
30% would evacuate off the Delmarva Peninsula
Vacationers 90% would return home
BACKGROUND – Behavioral Assumptions
BACKGROUND – Behavioral Assumptions
Where do they evacuate to?
35% to Northern Delaware
22% to Western Sussex County
15% to Bay Bridge
11% to I-95 West
9% to I-95 East
BACKGROUNDBACKGROUND
Policy Decisions
When should the evacuation be declared?
Should a “State of Emergency” be declared?
BACKGROUNDBACKGROUNDOperational AssumptionsOperational Assumptions
Should the operational plan be implemented?
CHOOSING A MODELCHOOSING A MODEL
Traditional Approaches
Spreadsheets
Regional Travel Demand Models
Simulation Models
CHOOSING A MODELCHOOSING A MODELSpreadsheets
Benefits:
• Easily explainable
• Relatively easy to implement
Drawbacks:
• Difficult to implement at a large scale
• Difficult to account for specific operations plans
• Difficult to estimate where evacuators go
• Difficult to account for background congestion
CHOOSING A MODELCHOOSING A MODELRegional Travel Demand Model
Benefits:
• Readily available framework from DelDOT’s Statewide Model
• Easy to implement statewide
• Easy to account for background congestion
Drawbacks:
• Difficult to account for spillover
• Difficult to account for specific operations plans
Simulation Model
Benefits:
• Easy to account for specific operations plans
• Easy to account for spillover
Drawbacks:
• Difficult to implement statewide
• Intensive calibration process
• Must estimate trip table externally
CHOOSING A MODELCHOOSING A MODEL
MODEL DEVELOPMENTMODEL DEVELOPMENT
DelDOT chose to use their Statewide Travel Demand Model because of its geographic coverage and ease of implementation.
MarylandNew Jersey
Pennsylvania
Virginia
Delaware
Maryland
Wilmington
Dover
Milford
Lewes
RehobothBeach
BethanyBeach
Atl
anti
c O
cean
Delaw
are Bay
Chesapeake B
ay
Bay Bridge
FenwickIsland
Ocean City
Virginia
/
0 6 12 18 243Miles
Legend
Delaware TAZ
Maryland TAZ
Model Network
Municipality
DelDOT’s Peninsula Model
AADT and peak tourist season assignments
Encompasses Delaware + Maryland’s Eastern Shore (9 Counties)
Covers a population of 1,209,944
Area of 5,375 miles2
MODEL DEVELOPMENTMODEL DEVELOPMENT
Evacuation Model
Capacity constrained model with hourly traffic assignments.
Accounts for the specific traffic control in place at intersections (signals, stop signs, roundabouts).
MODEL DEVELOPMENTMODEL DEVELOPMENT
Evacuation Model 10 groups of “evacuation zones” and
“destination zones” can be specified. Specify evacuation start time Model road closures during evacuation Track spillover between hours Input the pace of evacuation Input the percentage of vehicles
evacuated Specify a “State of Emergency” for
groups of zones
MODEL DEVELOPMENTMODEL DEVELOPMENT
Evacuation Model
Trip Generation
Background Assignment
Evacuation Trip Distribution
Evacuation Trip Assignment
MODEL DEVELOPMENTMODEL DEVELOPMENT
Trip Generation
Uses peak tourist season trip table from Peninsula Model
Determines background trip tables for 96 hours
Determines the number of vehicles in evacuating households (full-time and part-time)
Determines how many vehicles evacuate based on an input assumption (approximately 70% based on US Army Corps of Engineers)
MODEL DEVELOPMENTMODEL DEVELOPMENT
Background Traffic Assignment
Assigns non-evacuation trips to the network for 96 hours from the start of the evacuation.
Accounts for “State of Emergency” assumption.
MODEL DEVELOPMENTMODEL DEVELOPMENT
Evacuation Trip Distribution
Uses specified departure curves and specified evacuation and destination areas.
Uses doubly constrained logit distribution model to determine hourly evacuation trip tables for 30 hours of evacuation.
MODEL DEVELOPMENTMODEL DEVELOPMENT
MODEL DEVELOPMENT – Evacuation Assignment
MODEL DEVELOPMENT – Evacuation Assignment
Assign hourly evacuation trip tables to loaded hourly background traffic networks.
Check for Capacity Constraint.
Spill over-capacity trips to subsequent hours.
MODEL DEVELOPMENT – Evacuation Assignment
MODEL DEVELOPMENT – Evacuation Assignment
Excess Trips spillover to next hour Excess Trips spillover to next
hour
Assign Subsequent Hour Background trips to spillover
network
Assign Subsequent Hour Background trips to spillover
network
Determine Operating Capacity (Jam Density)
Assign Hour 1 Evac. Trips
Check roads where volume exceeds Capacity
Assign Hour 2 Evac. Trips to Spillover Network
Determine Operating Capacity (Jam Density)
Check roads where volume exceeds Capacity
Case Study 1 ParametersCase Study 1 Parameters 50% of non-evacuation traffic in Sussex
County stays home 70% of vehicles in Sussex County Beaches
evacuate Fast evacuation (all evacuees depart within
13 hours) Operational Plan is Implemented Evacuation is ordered at 6:00 AM 8% of evacuees depart prior to the order,
beginning at midnight
Case Study 2 ParametersCase Study 2 ParametersSame as Case Study 1 except with Ocean City, MD evacuation
Case Study ResultsCase Study Results
US 113 North of Georgetown
SR 26 East of Dagsboro
Order at 6:00 AM (2% Evacuating)
Evacuation Trips Exceed Capacity at 11:00 AM (12% Evacuating)
Majority of Evacuation Complete 4:00 PM (10% Evacuating)
Evacuation Complete 7:00 PM
Case Study 1 US 113 Northbound – North of Georgetown
Case Study 1 US 113 Northbound – North of Georgetown
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Time
Tri
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Background Traffic
Spill-over evacuation tripsfrom previous hours
Evacuation trips gettingthrough during this hour
Case Study 2 US 113 Northbound – North of Georgetown
Case Study 2 US 113 Northbound – North of Georgetown
Order at 6:00 AM (2% Evacuating)
Evacuation Trips Exceed Capacity at 6:00 AM (2% Evacuating)
Majority of Evacuation Complete 4:00 PM (10% Evacuating)
Evacuation Complete 7:00 PM
Case Study 1 SR 26 Westbound – East of DagsboroCase Study 1 SR 26 Westbound – East of Dagsboro
Order at 6:00 AM (2% Evacuating)
Evacuation Trips Exceed Capacity
at Noon (12% Evacuating)
Majority of Evacuation Complete 4:00 PM (10% Evacuating)
Evacuation Complete 7:00 PM
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Tri
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Background Traffic
Spill-over evacuationtrips from previoushoursEvacuation trips gettingthrough during this hour
Case Study 2 SR 26 Westbound – East of DagsboroCase Study 2 SR 26 Westbound – East of Dagsboro
Order at 6:00 AM (2% Evacuating)
Evacuation Trips Exceed Capacity at 8:00 AM (5%
Evacuating)
Majority of Evacuation Complete 4:00 PM (10% Evacuating)
Evacuation Complete 7:00 PM
Case Study 1 Evacuation Volumes by Time of DayCase Study 1 Evacuation Volumes by Time of Day Evacuation at 2:00 am (1%) Evacuation at 8:00 am (5%)
Case Study 2 Evacuation Volumes by Time of DayCase Study 2 Evacuation Volumes by Time of Day Evacuation at 2:00 am (1%) Evacuation at 8:00 am (5%)
Case Study 1 Evacuation Volumes by Time of DayCase Study 1 Evacuation Volumes by Time of Day Evacuation at Noon (12%) Evacuation at 5:00 pm (2%)
Case Study 2 Evacuation Volumes by Time of DayCase Study 2 Evacuation Volumes by Time of Day Evacuation at Noon (12%) Evacuation at 5:00 pm (2%)
Case Study ResultsCase Study Results
Case Study 1 Evacuation duration is 14
hours
Case Study 2 Evacuation duration is 21
hours
ConclusionsConclusions
The model would be easily applied to any situation where capacity constrained traffic assignments would be beneficial (ex. Event Planning, Major Roadway Construction)
The model would be easily modified to estimate non-hurricane evacuations
Next StepsNext Steps
Continue Working with DelDOT to Refine Evacuation Scenarios
Test Road and Bridge Closures Due to Storm Events
Continue to Improve Reporting Capabilities
Questions?Questions?
Scott Thompson-Graves, PE, [email protected]
Laura Rice, [email protected]
Mike [email protected]