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i
INDEPENDENT VERIFICATION AND VALIDATION (IV&V)
OF
REAL TIME EVACUATION PLANNING MODEL (RtePM)
Work performed for
Virginia Modeling and Simulation Center (VMASC)
Old Dominion University
by:
DDL Omni Engineering
440 Viking Drive Suite 200
Virginia Beach, VA 23452
22 April, 2013
Mr. Eugene Nielsen Mr. Thomas Cole
Project Lead Vice President, Simulation and
Systems Division
ii
EXECUTIVE SUMMARY
This independent assessment of the Real Time Evacuation Planning Model (RtePM) was
conducted by DDL Omni Engineering to verify and validate the capabilities of the RtePM. The
tasks were performed in accordance with the following Statement of Work:
Regional Catastrophic Preparedness Grant Program: Real-Time Evacuation Modeling and
Simulation for the National Capital Region
RtePM provides evacuation planners with a tool that allows multiple scenarios to be developed,
saved, and evaluated for evacuations from any type of hazard from any location in the
continental United States. RtePM was shown to be applicable to a wide variety of scenarios
specific to a geographic location.
Normally, verification is conducted prior to or during model development. Since the RtePM
model had already been developed, verification was not accomplished in the normal sense.
Instead DDL Omni Engineering took the approach to identify what an evacuation
model/simulation should have in order to be an effective evacuation tool. We called this
conceptual model validation and identified eight critical factors a model/simulation must have in
order to be an effective evacuation model/simulation. These critical factors and their associated
essential elements are in Appendix B.
We determine that RtePM met six of the eight critical factors. Weather and emergency services
were critical factors that RtePM did not demonstrate the ability to handle. However, we believe
workarounds such as changing speed limits, or closing roads/bridges/tunnels can simulate the
adverse effects of weather.
We also validated RtePM by using the six validation techniques described in “Verification and
Validation of Simulation Models” by Robert G. Sargent. These validation techniques are: 1)Data
Validity; 2) Comparison to other models; 3) Event Validity; 4) Internal Validity; 5)Parameter
Variability-Sensitivity Analysis; and 6) Historical Validation.
The first validation technique we used verified that the data residing in RtePM was accurate and
useful in the determination of evacuation time. We specifically looked at population and road
accuracy. Results indicated population data in RtePM was consistent with the U.S. Census 2010
and road data was accurate to acceptable levels. Model validity is highly dependent on input
data. DDL Omni Engineering recommends that reviews of population and road data occur on a
regular basis to maintain infrastructure currency. Users should take advantage of the
customization features of RtePM that allow for those with local knowledge to make corrections,
updates, and seasonal variations as indicated.
iii
The second validation technique we considered was a comparison of RtePM evacuation times to
other evacuation model times. Evacuation models considered include HURREVAC, OREMS,
CUBE and VISSIM. Because of the differences between models, the issue of comparing models
to models and the limited time frame for learning new software, the model to model comparison
was not performed.
The third validation technique, Event Validity, compared Nuclear Regulatory Commission
(NRC) evacuation time estimates to RtePM evacuation times and compared RtePM to post-
hurricane assessments. Assessments from Hurricanes Hugo, Opal, Floyd, Andrew, Ike, and
Bonnie were used. Actual clearance times obtained from these assessments matched RtePM
model times 95% of the time.
Model reliability was tested with the Internal Validity validation technique. When the user
simulates an evacuation several times in a deterministic model without any changes in the
scenario, it would be reasonable for the user to expect the evacuation times would remain the
same. RtePM reliability was validated.
The next validation technique tested was the Parameter Variability-Sensitivity Analysis
technique. It consisted of changing RtePM input and initial condition parameters to determine
the effect upon the model and its output. By running RtePM scenarios several times and
changing inputs to the scenario, we were able to determine that results produced by RtePM are
dependable and stable.
The last validation technique was to validate the Johns Hopkins University/Applied Physics
Laboratory (JHU/APL) Comparison Document, dated May 2012. This document compared
the output of RtePM to actual evacuation numbers from select real world evacuations.
JHU/APL also conducted a Face Validation that included positive comments about RtePM
from Federal, State and Local emergency managers. When comparing the data from
JHU/APL, US Army Corp of Engineers (USACE), and DDL Omni Engineering results were
consistent.
As a result of this analysis, DDL Omni Engineering determined that the current version of
RtePM is a fast, reliable, and easy to use system. It should be made accessible to evacuation
planners at all levels of government to effectively and efficiently plan evacuation routes for any
type of disaster. Federal, State and local emergency managers must have accurate, reliable
evacuation plans when the need arises, without spending months to develop a new study that
may cost thousands of dollars. RtePM is easy to use, and the effectiveness of the system
capabilities can be realized even by the first time user.
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Contents EXECUTIVE SUMMARY ................................................................................................................................... i
PURPOSE ....................................................................................................................................................... 6
BACKGROUND ............................................................................................................................................... 6
VERIFICATION and VALIDATION TECHNIQUES .............................................................................................. 8
CONCEPTUAL VALIDATION ........................................................................................................................... 8
OPERATIONAL VALIDATION .......................................................................................................................... 9
OPERATIONAL VALIDATION RESULTS ......................................................................................................... 10
1. Data Verification/Validity ................................................................................................................... 10
Table 1 Population Comparisons ........................................................................................................ 11
Figure 1 Roadway Comparison A ........................................................................................................ 13
Figure 2 Roadway Comparison B ........................................................................................................ 14
Figure 3 Roadway Comparison C ........................................................................................................ 14
Figure 4 Roadway Comparison D ........................................................................................................ 15
2. Comparison to other Models ............................................................................................................. 15
Table 2 Comparison Models ............................................................................................................... 16
3. (A) Event Validity - Nuclear Regulatory Commission Evacuation Studies ........................................... 16
Figure 5 North Anna NRC Comparison ................................................................................................ 18
Figure 6 Bellefonte NRC Comparison .................................................................................................. 19
Figure 7 Callaway NRC Comparison .................................................................................................... 20
Figure 8 Nine Mile Point NRC Comparison ......................................................................................... 21
3. (B) Event Validity - Comparison to Real World Assessments ............................................................ 21
Table 3 Real World Comparison.......................................................................................................... 22
4. Internal Validity Stability - Reliability ........................................................................................... 25
Table 4 Reliability Confidence ............................................................................................................. 26
5. Parameter Variability ......................................................................................................................... 26
Table 5 Parameter Variability ............................................................................................................. 27
Table 6 Change in Population and Seasonal Scalability Comparisons ................................................ 29
Table 7 Traffic Density (PPV and Background Traffic) Comparisons ................................................... 30
Table 8 Road Closures and Contra Flow Comparisons........................................................................ 31
6. Historical Data Validation .................................................................................................................. 32
v
Figure 9 Hancock County Comparison ................................................................................................ 33
Figure 10 Jackson County Comparison Scenario A ............................................................................. 34
Figure 11 Jackson County Comparison Scenario B ............................................................................. 35
Table 9 Ocean City Express Way (RT90) Vehicle Count ...................................................................... 37
Figure 12 Ocean City Express Way (RT90) Graph ............................................................................... 37
Table 10 Coastal Highway (RT1) Vehicle Count .................................................................................. 38
Figure 13 Coastal Highway (RT1) Graph.............................................................................................. 39
Table 11 Ocean Gateway (RT50) Vehicle Count ................................................................................. 39
Figure 14 Ocean Gateway (RT50) Graph ............................................................................................. 40
Table 12 Background Traffic ............................................................................................................... 40
Table 13 End Point Weighting ............................................................................................................. 41
DOCUMENT SUMMARY .............................................................................................................................. 43
CONCEPTUAL VALIDATION ................................................................................................................. 43
OPERATIONAL VALIDATION ................................................................................................................ 44
RECOMMENDATIONS ................................................................................................................................. 47
REFERENCES ................................................................................................................................................ 48
APPENDIX A ................................................................................................................................................. 51
TEST PLAN ........................................................................................................................................... 51
APPENDIX B ................................................................................................................................................. 66
CRITICAL FEATURES AND ESSENTIAL ELEMENTS ................................................................................ 66
APPENDIX C ................................................................................................................................................. 75
RtePM ENHANCEMENTS ..................................................................................................................... 75
6
PURPOSE
The purpose of this report is to provide results of the Independent Verification and Validation
(IV&V) of the Real Time Evacuation Planning Model. DDL Omni Engineering conducted the
study to validate the capabilities and limitations of RtePM for the Virginia Modeling, Analysis
and Simulation Center (VMASC). The assessment also provides the identification of additional
capabilities and potential improvements as required by the Statement of Work. Focus was
centered on ensuring RtePM meets system requirements and fulfills its intended purpose.
BACKGROUND
RtePM was developed in response to the emergency management community’s need for a tool to
provide quick estimates of the time required to evacuate an area in response to a natural or man-
made disaster.
The U.S. Department of Homeland Security, Science and Technology (DHS S&T) tasked John
Hopkins University’s Applied Physics Laboratory (JHU/APL) with the research, development
and delivery of an evacuation tool prototype that will enhance the capability of state and local
emergency managers to plan for and execute emergency evacuations.
RtePM Purpose
Provide a planning tool to assist end user planning for all hazard events by utilizing a
color-coded map interface. RtePM uses highway network data and census data to enable
the user to work through an evacuation scenario, setting parameters to model specific
conditions and responses. After the scenario has simulated the traffic flow and
calculated clearance times, an animated graphical depiction can be displayed.
RtePM Users
RtePM is for use by emergency management personnel for evacuation planning.
RtePM Output
RtePM provides evacuation times for user defined geographic regions and identifies
potential impediments to a timely evacuation. The tool allows the end user to quickly
try different “what-if" scenarios and to rapidly modify existing evacuation plans.
Initial planning called for two phases with the first phase using static data injection for planning
evacuations and the second phase expanding the static mode and adding a dynamic mode to
enhance capability and flexibility. 1
1 RtePM Statement of Work, August 2012
7
The Commonwealth of Virginia and the Virginia Department of Emergency Management
(VDEM) are coordinating on a project funded by the Regional Catastrophic Preparedness Grant
Program: Real-Time Evacuation Modeling and Simulation for the National Capital Region
(NCR). Specifically, the project employs modeling and simulation techniques to advance the
planning for and execution of a safe evacuation of residents in the NCR and the surrounding
states following an attack by terrorists, such as the release of toxic gas or detonation of a “dirty
bomb”.2
DDL Omni Engineering was tasked to provide an IV&V of RtePM. Confidence in RtePM must
be gained before its results are used to make decisions involving large sums of money or risk to
human life. Model verification and validation are critical in the development of a
model/simulation. Complicating this effort is the fact that no set of specific tests can easily be
applied to determine the “correctness” of the model. Furthermore, when IV&V is conducted
after the simulation model has been completely developed, as in the case of RtePM, the
evaluation performed can range from simply evaluating the validation conducted by the model
development team to performing a complete verification and validation effort.3
2 RtePM Statement of Work, August 2012 3 VERIFICATION AND VALIDATION OF SIMULATION MODELS Robert G. Sargent, Proceedings of the 2010 Winter Simulation Conference
8
VERIFICATION and VALIDATION TECHNIQUES
Verification and Validation (V&V) assessment activities primarily deal with the measurement
and assessment of accuracy of models and simulations (M&S). Verification is concerned with
the notion of correctly constructing the model, i.e., building the model right. It deals with
converting a problem formulation into a model specification or into a characterization accurately.
Validation refers to the notion that the right model was built and behaves with acceptable levels
of accuracy that are consistent with the goals and objectives of the model.
There are two distinct types of validation: conceptual and operational validation. Conceptual
model validation (validation of design documents) is the determination (usually by a group of
subject matter experts (SME) that the assumptions underlying the proposed conceptual model are
correct and that the proposed model/simulation design elements and structure (i.e., the
model's/simulation’s fidelity) likely will lead to results realistic enough to meet the requirements
of the application. Operational validation (hands-on the model/simulation) compares the
responses of the model/simulation with known or expected behavior from the subject it
represents to ascertain that those responses are sufficiently accurate for the range of intended
uses of the model/simulation.
CONCEPTUAL VALIDATION
Normally, verification is conducted prior to or during model development. Since the RtePM
model had already been developed, verification was not accomplished. Instead DDL Omni
Engineering took the approach to identify what an evacuation model/simulation should have in
order to be an effective evacuation tool. We called this conceptual validation and the following
steps were accomplished in order to determine whether RtePM had the features of an effective
evacuation tool:
1. Identified the purpose of RtePM and the intended users of the model/simulation. This
was accomplished in the purpose and background section above.
2. Defined critical features that an evacuation planning model must have in order to meet
the purpose of the planning model. DDL Omni Engineering identified the eight critical
features below.
a) Type of disaster- A good model will determine evacuation time for any type of
disaster.
b) Population density- The model must accurately model populations.
c) Roadway configuration- The model must accurately display roads and routes.
d) Traffic Density- The model must consider the effects of congestion.
e) Time to Evacuate- The model must accurately determine evacuation time.
9
f) Emergency Services- The model should model the effects of emergency services.
g) Capabilities- The model/system should have user friendly graphical user
interfaces (GUIs) and easy to use displays.
h) Weather- The model/system should consider the impact of weather.
3. Developed essential elements or requirements of a planning model to make the critical
features function properly.
4. Developed validation use-cases that will test all essential elements. See Appendix A.
5. Performed validation, using the use-cases, to determine the model’s/simulation’s ability
to meet the essential elements.
6. Determined that RtePM met all the critical features that an effective evacuation planning
model/simulation should have except considering the impact of weather and the effects of
emergency services. However, we believe workarounds such as speed limits,
road/bridge/tunnel closures can simulate the effects of weather. The results of RtePM’s
ability to meet the critical features and essential elements are documented in Appendix B.
OPERATIONAL VALIDATION
To accomplish operational validation, we used Robert Sargent’s article listing the following
techniques to develop model/simulation confidence. Each of these techniques has specific
application criteria that were used as a guideline for model validation. We also discussed the
strengths and limitations of each technique particularly as it applies to validation of RtePM.4
Data Validity- Ensuring that the data necessary for model building, model evaluation and
testing, and conducting the model experiments to solve the problem are adequate and correct.
This is an excellent technique for verifying databases and we used it to validate the
population and roadway facilities in RtePM. The limitation here is databases can be very
large making it nearly impossible to validate the entire database.
Comparison to other models- Various results of the simulation model being validated are
compared to results of other (valid) models. The simulation model is compared to other
simulation models that have been validated. At first this technique appears to be a good
method to check the model’s/simulation’s ability to stand up to other models/simulations.
But this technique has several limitations including: 1) The models/simulations must be
4 VERIFICATION AND VALIDATION OF SIMULATION MODELS
Robert G. Sargent, Proceedings of the 2010 Winter Simulation Conference
10
similar, e.g. both should be evacuation models. 2) The models/simulation must use similar
databases, i.e. you not only do not want to compare apples to oranges but you may not want
to compare Red Delicious apples with Granny Smith apples. 3) The models/simulations
being compare to must be validated or the model/simulation might be very wrong. 4)
Models/simulations must have similar functionality, i.e. how you get at the data and how you
manipulate the data must have some commonality in order to do a meaningful comparison.
Event Validity- The “events” of occurrences of the simulation model are compared to those
of actual events to determine if they are similar. This is comparing the results of running your
model/simulation to real world results, e.g. model/simulation evacuation times are compared
to actual evacuations for hurricanes or the NRC evacuation case studies.
Internal Validity- Several runs of a model are made to determine the amount of (internal)
variability in the model. A large amount of variability (lack of consistency) may cause the
model’s results to be questionable. This is fairly straight forward, make several runs and
check for consistency. The problem comes when there are inconsistencies, determining what
caused them and why.
Parameter Variability-Sensitivity Analysis- This validation technique consists of changing a
model’s input and initial condition parameters to determine the effect upon the model and its
output. A different output is expected if the input or initial conditions are changed.
Sometimes the output does not change because the model/simulation compensates for
changes so you must be aware of how the model/simulation responds to a changed input.
Historical Validation
Historical validation evaluates the verification and validation efforts that have already been
performed by other agencies including the original developer. Historical validation is useful
when IV&V is conducted after the model/simulation has been completely developed. When
historical validation data is available, the data can be used for testing other models/
simulations to determine their validity. Historical validation may include several validation
techniques such as comparing model results, parameter variability, event validation and face
validation. Face validation determines if the model seems reasonable to people who are
knowledgeable about evacuation planning. Face validation is based on the look and feel of
the model and the results. The limitation of this validation technique is that it can become
subjective. It may also be difficult to obtain qualified SMEs.
OPERATIONAL VALIDATION RESULTS
1. Data Verification/Validity The first operational validation technique we used was to verify that data residing in RtePM
11
was accurate and useful in the determination of evacuation time. This was accomplished by
comparing two of the eight critical factors we identified that an evacuation model/simulation
should have against actual data sets within RtePM. We evaluated population and roadway
configuration.
The first data set verified was population. RtePM utilizes the Oak Ridge National Laboratory
(ORNL), Department of Computational Sciences and Engineering Division population
database known as LandScan™. This database relies heavily on the 2010 U.S. Census data.
LandScan is the standard for global population distribution, and is accepted for estimating at
risk population by the Department of Defense (DOD) and Department of State (DOS).
Table 1 shows randomly chosen population data for five census block groups for seven
representative cities throughout the nation. The first column in the table lists census block
identification areas in the highlighted city. The second column lists the population for census
block in that row that we found in U.S. Census/ LandScan data. The third column lists the
population for the census block in that row that we found in RtePM data.
The population data for RtePM is identical to the U.S. Census/LandScan data in all cases,
verifying that RtePM population data is accurate.
Table 1 Population Comparisons
WASHINGTON DC
Census Block ID US Census/Landscan RtePM
110010108001 599 599
110010055004 1097 1097
110010043001 2062 2062
110010053014 1551 1551
110010108003 2008 2008
VIRGINIA BEACH, VIRGINIA
Census Block ID US Census RtePM
518100442002 2511 2511
518100444011 648 648
518100448061 2081 2081
518100440013 1076 1076
518100440034 874 874
JACKSONVILLE, FLORIDA
Census Block ID US Census RtePM
120310003002 725 725
12
120310029022 1402 1402
120310174001 1345 1345
120310002002 1005 1005
120310147012 3876 3876
MOBILE, ALABAMA
Census Block ID US Census RtePM
10970004021 103 103
10970011001 668 688
10970009031 1627 1627
10970004012 262 262
10970005001 1403 1403
NEW ORLEANS, LOUISIANA
Census Block ID US Census RtePM
220510232002 1911 1911
220510214001 1176 1176
220510239002 784 784
220510201022 942 942
220510202012 1288 1288
SAN DIEGO, CALIFORNIA
Census Block ID US Census RtePM
60730039013 1636 1636
60730039021 1088 1088
60730111001 1851 1851
60730051002 4218 4218
60730050001 2221 2221
SEATTLE, WASHINGTON
Census Block ID US Census RtePM
530330090002 1520 1520
530330089003 1190 1190
530330088003 944 944
530330086002 2503 2503
530330091001 1243 1243
The second data set verified was roadway configuration. We wanted to determine that the
model utilized the most current and accurate roads and routes. The technique we used to do
this was to compare RtePM’s HSIP-Gold 2010 NAVTEQ highway network data, to known
roadway facilities in the Hampton Roads area. We selected fifteen well known roads and
routes in the Hampton Roads area to evaluate and found all fifteen to be accurately portrayed
in RtePM.
13
We also compared roadway facilities in thirty areas outside the Hampton Roads area by using
Google Maps, which map the earth by the superimposition of images obtained from satellite
imagery, aerial photography and GIS 3D globe. We selected thirty areas where evacuations
are likely to occur and compared Google Maps of the selected roadway to RtePM maps and
data of that roadway. In twenty nine of thirty comparisons, the roadway facilities accurately
compared as indicated in Figure 1 and Figure 2. These Figures, for Gulf Shores Parkway (AL
59), showed a four lane divided highway in both the Google Maps and RtePM maps and data.
This verified that the RtePM data was correct.
In one of the thirty comparisons, the RtePM data was not consistent with Google Maps of the
area or the RtePM map of the area. Figures 3 (maps and data on the coastal highway at the
Maryland/Delaware state line) showed the RtePM data (outlined in red) was inaccurate listing
only one lane of traffic on this corridor, although the RtePM picture clearly shows two lanes
of traffic each way. The Google Map picture in Figure 4 also shows two lanes of traffic each
way, demonstrating the need for locally knowledgeable users to make inputs for accuracy and
currency.
Figure 1 Roadway Comparison A
14
Figure 2 Roadway Comparison B
Figure 3 Roadway Comparison C
15
Figure 4 Roadway Comparison D
SUMMARY: Using the Data Validity technique of operational validation, DDL Omni
Engineering found that the critical feature “population” was modeled accurately in RtePM
data. RtePM population data was identical to U.S. Census/ LandScan population data for the
seven U.S. cities and thirty-five census id blocks sampled. Users can be confident that RtePM
population data is accurate.
The critical feature “roadway configuration” that every model/simulation should have in order
to accurately simulate evacuations was evaluated by DDL Omni Engineering using the Data
Validity technique. In twenty-nine of the thirty instances that we tested for road data
accuracy, RtePM maps and data accurately compared to Google Maps and known roadway
facilities in the Hampton Roads area. In one of the thirty instances tested, the road model data
was inconsistent with the RtePM map and Google Maps and may affect evacuation time
results.
DDL Omni Engineering recommends the RtePM ‘Roads Tab’ be updated by local emergency
managers who are familiar with local roads and routes.
2. Comparison to other Models DDL Omni Engineering attempted to compare RtePM evacuation times to evacuation times the
models listed in Table 2 generated. Because of the differences between the models, the issues of
comparing models to models listed in the operational validation lead-in above and the limited
timeframe for learning to operate each new model, the model to model comparison was not
performed.
16
Table 2 Comparison Models
MODEL PURPOSE
HURREVAC (HURRicane EVACuation)
Storm tracking and decision support tool.
Provides evacuation decision time to
emergency managers
OREMS (Oak Ridge Evacuation Modeling
System)
A microcomputer-based system for
simulation of traffic flow during an
emergency evacuation.
CUBE Voyager – macroscopic modeling
simulation
Provides a comprehensive library of
functions for the modeling and analysis of
passenger transport systems: roadways,
public transit, pedestrians and bicycles
CUBE Avenue
An extension of CUBE voyager, mesoscopic
modeling simulation which models traffic at
greater detail than Cube Voyager
VISSIM - Verkehr In Städten -
SIMulationsmodell” (German for “Traffic in
cities - simulation model)
Microscopic traffic flow simulation model
SUMMARY: Comparison of other evacuation models with RtePM was not successful
because learning the operation of compatible models was difficult due to time constraints,
complexity of other models, and the lack of comparable capabilities for those models to those
of RtePM. The resulting validation would be suspect, with a reduced level of confidence in the
accuracy of the comparisons. Comparison of the RtePM model to other models was not
accomplished.
3. (A) Event Validity - Nuclear Regulatory Commission Evacuation Studies
The NRC requires estimated evacuation times in the event of a major accident at a commercial
nuclear power station. The exposure of the public to airborne radioactive materials can be
prevented or greatly reduced by evacuating the area immediately surrounding the reactor site.
Reactor licensees are required to conduct studies to estimate the time needed to evacuate the
public from the area surrounding each nuclear power station. The results of such studies are used
17
by regulatory personnel and emergency planners to assess the potential effectiveness of
protective responses for the public. The time required to evacuate the public from a 2, 5, or 10-
mile radius is estimated by analyzing the available transportation facilities and other relevant
conditions within this radius.
Four NRC evacuation time studies were compared to evacuation estimates modeled by RtePM.
Figures 5, 6, 7 and 8 display the results of the comparison. The first three studies (North Anna,
Bellefonte, and Callaway) simulate summer, midweek, mid-day, and good weather. The final
study (Nine Mile Point) simulates winter, weekend, mid-day with snow. The results display NRC
and RtePM evacuation times for a 2 mile radius, 5mile radius, and a 10 mile radius. The
comparison results are as follow:
1. Dominion Power, North Anna 3 Nuclear Power Plant Emergency Plan, Dec 2008
This plan describes the analyses undertaken by a study to develop an evacuation time estimate
(ETE) for the North Anna Power Station (NAPS) in Virginia. Evacuation times were determined
using the Interactive Dynamic Evacuation Model (IDYNEV), a macroscopic simulation model
that provides dynamic transportation routing features. The IDYNEV System that was employed
for this study is comprised of several integrated computer models. One of these is the PC-
DYNEV (DYnamic Network EVacuation) macroscopic simulation model that was developed by
KLD under contract with the Federal Emergency Management Agency (FEMA). KLD has
performed the ETE studies for 15 of the 18 Combined Operating License (COL) and Early Site
Permit (ESP) applications currently on file with the NRC. The North Anna project began in May
2007 and extended over a period of 7 months, then revised in 2008. The revision included a
refinement of the calculations performed in the original report.
The ETEs calculated for the North Anna Power Station are similar to the results obtained
utilizing RtePM. In the North Anna study, the trip generation time is calculated at 4 hours. Trip
generation in the NRC report is the value of the time that the last person entered the roadway.
RtePM evacuation times vary slightly when compared to the 2008 NAPS study due to the
“custom response time” that can be manipulated in RtePM. If the custom response time in
RtePM is set to “1”, meaning the population would start evacuating within one hour, the
evacuation time is shorter in RtePM than that of the NRC study. When the “custom response
time” in RtePM is changed to 4, meaning the last person entering the roadway is four hours later,
the evacuation times are comparable to that of the NAPS study. See Figure 5.
18
Figure 5 North Anna NRC Comparison
The North Anna comparison results in Figure 5 confirm the importance of accurately recreating
the scenario conducted by the NRC with the same data entered in to RtePM. System
terminology needs to be considered, such as custom response time for RtePM and trip generation
time for the NRC study as described above.
Trip generation times were obtained in the NRC study by consulting emergency managers in the
area, conducting surveys, and contacting the public using phone interviews. Also considered
was time of day, weather, number of vehicles, and schools in session. Emergency managers,
survey and telephone interview results provided different response times due to variables such as
school children riding busses home, spouses returning home from work and packing, shoveling
driveways, or picking up relatives. Although RtePM does not account for the time to shovel a
driveway, these variables can be defined in RtePM by adjusting response times, day or night
timeframe, number of people per vehicle, or changing the percentage of population clearing the
area. Evacuation end points were selected based on wind direction. If evacuees are located up-
wind of the power plant, they would evacuate to any end point away from the power plant. If
evacuees are located down-wind of the power plant, they would evacuate to end points which are
perpendicular to the wind direction.
2. Bellefonte Nuclear Plant ETE Report, September 2007
This report describes the analyses undertaken and the results obtained by a study to develop
ETEs for the proposed Bellefonte Nuclear Plant (BLN) located in Jackson County, Alabama.
0
5
10
15
20
25
30
0 30 60 90 120 150 180 210 240 270 300
NU
MB
ER O
F V
EHIC
LES
in T
HO
USA
ND
S
EVACUATION TIME IN MINUTES
Evacuation Time Estimate Summer, Midweek, Midday, Good Weather
2 mile RtePM 2 5 mile RtePM 5 10 mile RtePM 10
19
ETEs were also calculated using the IDYNEV simulation model. This project began in August,
2006 and extended over a period of 8 months. The traffic demand and trip-generation rate of
evacuating vehicles were estimated by consulting emergency managers in the area, conducting
surveys, and contacting the public using phone interviews. The trip generation rate reflected the
estimated mobilization time (i.e., the time required by evacuees to prepare for the evacuation
trip) that was computed using the results of the telephone survey of Emergency Planning Zone
(EPZ) residents. The evacuation time comparison results are displayed in Figure 6.
Figure 6 Bellefonte NRC Comparison
Results closely compare between the Bellefonte study and RtePM when comparing 2, 5, and
10 mile radius evacuation zones. Evacuees, number of vehicles, time of day, and custom
response time were compared. Evacuation end points were selected based on wind direction.
If evacuees are located up-wind of the power plant, they would evacuate to any end point
away from the power plant. If evacuees were located down-wind of the power plant, they
would evacuate to end points which are perpendicular to the wind direction.
3. Callaway Nuclear Plant ETE Report, September 2007
Population estimates for the Callaway evacuation zones in Ameren, Mo, were based upon
Census 2010 data. Estimates of employees who reside outside the EPZ and commute to
work within the EPZ were based upon data obtained from surveys of major employers in the
EPZ. Population estimates at special facilities were based on available data from county
emergency management offices and from phone calls to specific facilities. Roadway capacity
estimates were based on field surveys and the application of the Highway Capacity
Manual 2010. Population mobilization times are based on a statistical analysis of data
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20
acquired from a random sample telephone survey of EPZ residents. The relationship between
resident population and evacuating vehicles were developed from telephone surveys. Average
values of 2.40 persons per household and 1.35 evacuating vehicles per household were used.
All data was modified and duplicated in RtePM and number of vehicles and the time to
evacuate is displayed below:
Figure 7 Callaway NRC Comparison
Again the results are comparable between the Callaway study and RtePM when comparing 2, 5,
and 10 mile radius evacuation zones. Evacuees, number of vehicles, time of day, and custom
response time were compared. Evacuation end points were selected based on wind direction. If
evacuees were located up-wind of the power plant, they would evacuate to any end point away
from the power plant. If evacuees were located down-wind of the power plant, they would
evacuate to end points which are perpendicular to the wind direction.
4. Nine Mile Plant, ETE Report, September 2007
The Nine Mile Plant ETE report was selected for comparison in order to validate evacuation
times during adverse weather. The Nine Mile plant is located on the shores of Lake Ontario in
Lycoming, NY, and receives heavy lake effect snowfall during the winter months. The report
estimates roadway free-flow speed and capacity reductions of approximately 20 percent under
snow conditions. Transient population reductions are not assumed for snow scenarios since
tourism is not high at this time of year.
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Evacuation Time Estimate Summer, Midweek, Midday, Good Weather
2 mile RtePM 2 5 mile RtePM 5 10 mile RtePM 10
21
Figure 8 Nine Mile Point NRC Comparison
Although RtePM does not directly model weather into the simulation, it does provide the
ability to simulate the effect that weather would have on evacuation clearance times by
changing scenario input data. Varying data inputs was accomplished easily in RtePM by
reducing roadway speed limit by 20 percent, reducing the number of lanes available, and/or
closing smaller roads which have not been cleared. By decreasing the free flow speed on
roads during snow or heavy rain events, RtePM clearance times will adjust accordingly. The
time to shovel driveways and clear parking lots can also be accounted for by adjusting the
response time in RtePM. Validation times are displayed in Figure 8.
SUMMARY: Actual evacuation time results of the studies conducted for the NRC compared
to RtePM evacuation times were very similar as evidenced by the near parallel lines for each
of the 2, 5 and 10 mile radius lines for the NRC study and the 2, 5 and 10 RtePM radius lines.
Variables considered were the number of commuters, employees of the plant, bussing of
school students, vacation population being evacuated (transients), and the number of
ambulatory evacuees. ETE’s were calculated using RtePM in less than thirty minutes, saving
enormous time and money compared to conventional methods that take up to 6 months.
3. (B) Event Validity - Comparison to Real World Assessments
The Event Validity validation technique was also used to compare RtePM to real world
assessments. In this technique, a rigorous comparison of M&S performance with real world
phenomena was conducted. The test and review process compared RtePM results with known or
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Evacuation Time Estimate Winter, Weekend, Midday, Snow
2 mile RtePM 2 5 mile RtePM 5 10 mile RtePM 10
22
expected behaviors. This validation technique compares the M&S results to some authoritative
reference data that defines what the expected results should be.
DDL Omni Engineering compared random Post Storm Assessments (PSA) and Hurricane
Evacuation Studies (HES) with results compiled while operating RtePM. These studies looked at
the vulnerability of a population to hurricane threats and gave guidance to local emergency
managers planning an evacuation of the community.
PSAs provide actual clearance times based on interviews with emergency planners, local
officials, and telephone surveys of individuals who remained in place during the hurricane.
During interviews, different understandings of the meaning of "clearance time" existed, so the
actual clearance times reported are approximate, but the most reliable, up to date data available.
One of the key outputs of an HES process is the matrix of Evacuation Clearance Times - the
number of hours it takes to move the threatened population to safety given various factors such
as the category of storm, the tourist occupancy (or population) of the area at the time, and public
responsiveness. The HES provide estimated clearance times provided to FEMA by the Army
Corp of Engineers prior to a storm.
The following PSAs and HES were utilized in determining RtePM validity by comparing real
world assessments results to the model simulation.
Table 3 Real World Comparison
Data Compared PSA Actual Clearance
Time Experienced
RtePM Calculated Clearance
Time
HES Estimated Clearance
Time
Hurricane Hugo
Camden County, GA 7 HRS 8.2 HRS 6 HRS
Glynn County, GA 8.5 HRS 8.5 HRS 8 HRS
Hurricane Opal
Escambia County, FL 9 HRS 9.8 HRS 15.5 HRS
Santa Rosa County, FL 8 HRS 8.7 HRS 7.5 HRS
Mobile County, AL 10 HRS 9.8 HRS 14 HRS
Baldwin County, AL 8 HRS 9.1 HRS 14 HRS
Hurricane Floyd
Beaufort County, SC 24 HRS 18 HRS 20 HRS
Brunswick County, NC 5 HRS 8.5 HRS 8.5 HRS
New Hanover County, NC 8 HRS 10 HRS 7.5 HRS
Hurricane Andrew
Dade County, FL 13 HRS 17 HRS 15 HRS
23
Data Compared PSA Actual Clearance
Time Experienced
RtePM Calculated Clearance
Time
HES Estimated Clearance
Time
Broward County, FL 17 HRS On going 23 HRS
Hurricane Ike
Houston/Galveston, TX 36 HRS 25.5 HRS 19 HRS
Matagorda County, TX 10 HRS 8.4 HRS 8 HRS
Hurricane Bonnie
Georgetown County, SC 9 HRS 11.6 13 HRS
Horry County, SC 12.5 HRS 11.9 13 HRS
POST STORM ASSESSMENT SUMMARY
In all cases, RtePM can produce similar results to HES or PSA by adjusting input numbers
such as:
1. Population - Increasing or decreasing the amount of population evacuating varies the
evacuation time
2. People per vehicle - Increasing or decreasing the number of people per vehicle, varies
the evacuation time
3. Percent of population clearing area - Increasing or decreasing the percentage of people
clearing area, changes the evacuation time
4. Percent using shelters – The percentage of people using shelters affects estimated
evacuation time
5. Modify roads and endpoints – Modifying roads and destinations result in change of
evacuation times
6. Response rate – Changing custom response rates affect evacuation times
7. Seasonal population – Changes in tourist population
8. Level of Background traffic – changes in background traffic affect evacuation times
The purpose of this study is not to manipulate RtePM so that it matches the exact results of
other studies. The purpose of these real world comparisons was to gather as much real world
data from PSAs and enter that data into RtePM and compare the results. When the results do
not match exactly, which is the case in the table above; it is not our intent to make the results
match, even though by manipulating the above listed variables, RtePM can mimic the results
of other models.
Hurricane Hugo – In Georgia, clearance times calculated for FEMA/Corps studies compared
well with the actual times experienced in Hurricane Hugo. For those counties carrying out major
24
evacuations, RtePM study produced times were within an hour of actual times.5 RtePM
evacuation times were nearly identical to the actual and FEMA evacuation times.
Hurricane Opal - Mobile and Baldwin counties reported that traffic flowed fairly smoothly
through their in-county evacuation networks. They also reported that county residents eventually
met significant congestion and long delays north on Interstate 65 in Escambia County and
beyond. Low evacuation times compared to HES evacuation estimates indicate that a smaller
portion of the population evacuated than anticipated.6 RtePM evacuation estimates were nearly
identical for the Mobile and Baldwin County HES actual evacuation times when considering that
just fifty percent of the population cleared the area.
RtePM evacuation times were very acceptable when compared to actual evacuation times of
Escambia and Santa Rosa Counties. Sixty one percent of the population evacuated, and when
configuring RtePM with a sixty one percent of population clearing area, the results were nearly
identical. Escambia did not implement reverse lane strategies on its evacuation routes because of
the resources that would be necessary to control access at intersections were not available.7
Reversible lanes were not used in the RtePM calculations in order to closely simulate actual
events.
The evacuation out of Santa Rosa County was described by county officials as a "nightmare."
The general evacuation was underway at approximately 8 am. Roads feeding Interstate 10
backed up as construction on the Interstate slowed traffic. RtePM calculations included a
reduction of “free flow Speed’ when calculating evacuation times along Interstate 10. Several
in-county back roads were flooded due to heavy rainfall that occurred during the week preceding
Opal. We simulated this phenomenon by closing all small arterial end-points and evacuating
along major arterial and highways.
Hurricane Floyd - The path of Hurricane Floyd and the uncertainty which existed regarding its
potential landfall, caused massive evacuations of populations in the Florida, Georgia, South
Carolina, and North Carolina coasts. These massive evacuations that occurred as a result of the
uncertainty regarding anticipated landfall of Hurricane Floyd, led to the use of various traffic
control actions by local and state officials. Most counties experienced flooding as the main
problem encountered during the evacuation. Due to flooding, the amount of evacuating
population may have been reduced resulting in the Brunswick County five hour observed
evacuation time.8 RtePM, actual and FEMA evacuation times were comparable.
5 Hurricane Hugo Assessment, January 1990
6 Hurricane Opal Assessment, October 1995
7 Hurricane Opal Assessment, October 1995
8 Hurricane Floyd Assessment, May 2000
25
Hurricane Andrew – The Dade and Broward County actual evacuation times were slightly less
than calculated times. This was directly due to less participation on the part of the residents than
had been assumed in the modeling. The times also suggest that many evacuees may have left the
area well in advance of the official evacuation order given early Sunday morning. Traffic counts
collected from the permanent count stations that were functioning prior to and during the
Andrew evacuation confirm this phenomenon.9 While attempting to run the Broward county
scenario in RtePM an, “Error configuring scenario to run” message is displayed. Initial research
conducted by DDL Omni Engineering is inconclusive as to the cause of the error. Further
research is being conducted. When attempting to run Dade County scenario, we were not able to
select the entire county for evacuation. By design, RtePM allows for the selection of 1500
census ID blocks. Dade County exceeds this limit, resulting in “the total number of population
blocks selected has exceeded the maximum of 1500, please retry selection” error message. This
was easy to overcome by running the evacuation in two phases. By splitting the county in half,
the scenario ran perfectly and the evacuation times were comparable.
Hurricane Ike - The 2010 USACE HES updated clearance times were generated based on
differing intensity strengths of hurricanes, levels of background traffic, and the rapidity of
response by evacuees, and different tourist occupancy levels. Category 3 levels were used for
this comparison.10
Hurricane Bonnie - Interviews and analysis conducted for the post Bonnie effort revealed modest
evacuation participation rates on the part of the permanent population. Shelter usage was low
except in Horry County, South Carolina, where many tourists went to public shelters. Few traffic
problems were reported. The lack of traffic problems indicates that local and state officials
started the evacuation in a timely manner that traffic control was appropriate and effective, and
that participation rates were much less than the 100% rates used in the study calculations.
RtePM evacuation times were closer to actual evacuation times than FEMA predicted evacuation
times.
4. Internal Validity Stability - Reliability
A good model/simulation must give the user confidence that the results are accurate. This
confidence must be gained in some part by model reliability. Model reliability is the fourth
validation technique that was conducted. If the user simulates an evacuation several times
without any changes in the scenario, it would be reasonable for the user to expect the
evacuation times would remain the same. When running the same scenario multiple times, the
user should expect the exact same results. The stability/reliability testing of RtePM was
satisfactory and Table 4 represents a sample of the testing results.
9 Hurricane Andrew, January 1993
10 Post Storm Assessment: Hurricane Ike, June 2010
26
Table 4 Reliability Confidence
Location Evacuation Time #1 Evacuation Time #2 Evacuation Time #3
Bethany Beach, DE 14.3 Hours 14.3 Hours 14.3 Hours
Bethesda, MD 8.1 Hours 8.1 Hours 8.1 Hours
Mobile, AL 16.4 Hours 16.4 Hours 16.4 Hours
Panama City, FL 10.7 Hours 10.7 Hours 10.7 Hours
Galveston, TX 25.6 Hours 25.6 Hours 25.6 Hours
Hancock County, MI 8.3 Hours 8.3 Hours 8.3 Hours
Washington D.C. 16.2 Hours 16.2 Hours 16.2 Hours
Anaheim, CA 4.7 Hours 4.7 Hours 4.7 Hours
Concord, CA 8.1 Hours 8.1 Hours 8.1 Hours
Virginia Beach, VA 23.7 Hours 23.7 Hours 23.7 Hours
Savannah, GA 8.4 Hours 8.4 Hours 8.4 Hours
SUMMARY: When duplicating scenarios and running the scenario multiple times, the
evacuation times in Table 4 remained the same, indicating model/simulation reliability. The
following data sets were tested:
1. Population
2. People per vehicle
3. Percent of population clearing area
4. Percent using shelters
5. Modify roads and endpoints
6. Response rate
7. Seasonal population
8. Level of Background traffic
5. Parameter Variability The fifth validation technique used was the Parameter Variability-Sensitivity Analysis
technique. It consisted of changing RtePM input and initial condition parameters to determine
the effect upon the model and its output. By running RtePM scenarios several times and
changing minor inputs to the scenario, we were able to determine that results produced by
27
RtePM are consistent with parameter changes that were made. Table 5 exhibits the results
when varying parameter inputs are manipulated.
Table 5 Parameter Variability
Parameter Input Change To Parameter Measurement Method Results
Population Change
Percent
Alter the percent of
population clearing the
area in:
1. Baldwin County, AL
2. Las Vegas, NV
3. Virginia Beach, VA
4. Portland, OR
Test for accurate
change in evacuation
time when changing
population density by
adjusting the
population change
block (%)
All tests were
satisfactory. When
the % of
population was
altered, the time
to evacuate
changed
proportionately.
Seasonal Scalability Alter the seasonal
population clearing the
area in:
1. Baldwin County, AL
2. Las Vegas, NV
3. Virginia Beach, VA
4. Portland, OR
Test for accurate
change in evacuation
time when changing
seasonal population.
All tests were
satisfactory.
When the seasonal
population was
altered, the time
to evacuate
changed
proportionately.
Number of
evacuees per
vehicle
Alter the number of
evacuees per vehicle
when clearing the area in:
1. Baldwin County, AL
2. Las Vegas, NV
3. Virginia Beach, VA
4. Portland, OR
Test for accurate
change in evacuation
time when adjusting
people per vehicle
All tests were
satisfactory. After
altering the # of
people per vehicle,
evacuation times
changed
proportionately.
Use of reversible
lanes / Contraflow
Alter reversible lanes
clearing the area in:
1. Baldwin County, AL
2. Las Vegas, NV
3. Virginia Beach, VA
4. Portland, OR
Build scenarios with
reversible lanes and
calculate evacuation
times for different
scenarios
All tests were
satisfactory.
When contra-flow
was utilized,
evacuation times
decreased.
28
Parameter Input Change To Parameter Measurement Method Results
Road closures /
includes bridges,
tunnels
Alter the road closures /
including bridges, tunnels
clearing the area in:
1. Baldwin County, AL
2. Las Vegas, NV
3. Virginia Beach, VA
4. Portland, OR
Close roads, bridges or
tunnels and determine
if evacuation times
differ
All tests were
satisfactory. When
roads were closed,
evacuation times
increased.
Background Traffic/ Background traffic simulates vehicles using the roadways but not directly participating in the evacuation.
Alter the background
traffic density clearing
the area in:
1. Baldwin County, AL
2. Las Vegas, NV
3. Virginia Beach, VA
4. Portland, OR
Change background
traffic density and
determine if
evacuation times differ
All tests were
satisfactory.
When background
density was
altered,
evacuation times
changed
proportionately.
29
The following tables show results of random population and seasonal population changes in
different cities and the corresponding results. We expected that as population increased by an
operator selected percentage or seasonal increase in population, evacuation times would
increase.
Table 6 Change in Population and Seasonal Scalability Comparisons
Baldwin County, AL Background Traffic RtePM 25% Population Increase Seasonal Population
Evacuation Time: Hours
None 15.2 17.9 16.3
Low 15.5 18.8 16.4
Medium 15.8 18.8 16.4
High 15.8 19.8 17.2
Portland, OR Background Traffic RtePM 25% Population Increase Seasonal Population
Evacuation Time: Hours
None 9.5 11.5 11.6
Low 10.0 11.8 12.4
Medium 10.4 12.0 12.5
High 10.5 12.4 12.6
Las Vegas, Nevada Background Traffic RtePM 25% Population Increase Seasonal Population
Evacuation Time: Hours
None 11.5 13.8 15.2
Low 11.9 14.2 15.4
Medium 12.1 14.4 15.4
High 12.2 14.5 15.7
Virginia Beach, VA Background Traffic RtePM 25% Population Increase Seasonal Population
Evacuation Time: Hours
None 13.7 15.8 23.8
Low 13.8 16.0 23.8
Medium 13.9 16.6 23.8
High 14.1 16.8 23.8
Finding: When selected cities were tested for change in population, evacuation times changed
proportionately.
30
The following tables show results for change in number of people per vehicle. The test was
conducted using all selectable types of background traffic. We expected that as the number of
people per vehicle increased, evacuation times would decrease and vice versa.
Table 7 Traffic Density (PPV and Background Traffic) Comparisons
Baldwin County, AL Background Traffic 2.5 PPV 1 PPV 4 PPV
Evacuation Time: Hours
None 16.3 34.5 12.2
Low 16.4 34.8 12.3
Medium 16.4 34.8 13.0
High 17.2 35.8 13.0
Portland, OR Background Traffic 2.5 PPV 1 PPV 4 PPV
Evacuation Time: Hours
None 9.5 20.4 8.4
Low 10.0 20.7 8.4
Medium 10.4 21.3 8.4
High 10.5 21.4 8.5
Las Vegas, NV Background Traffic 2.5 PPV 1.5 PPV 4 PPV
Evacuation Time: Hours
None 11.5 17.9 8.5
Low 11.9 18.2 8.6
Medium 12.1 18.3 8.8
High 12.2 18.3 8.9
Virginia Beach, VA Background Traffic 2.5 PPV 1.5 PPV 4 PPV
Evacuation Time: Hours
None 23.8 30.8 19.8
Low 23.8 30.8 19.8
Medium 23.8 30.8 19.8
High 23.8 30.8 19.8
Finding: When selected cities were tested for change in people per vehicle and background
traffic, evacuation times changed proportionately.
31
The following tables show results for changes in Road Closure and Contra Flow. The test was
conducted using four levels of background traffic. We expected that evacuation times would
increase as roads were closed and evacuation times would decrease if contra flow techniques
were used.
Table 8 Road Closures and Contra Flow Comparisons
Baldwin County, AL Background Traffic Basic Road closure AL50 North near Loxley Contra Flow
Evacuation Time: Hours
None 16.3 18.9 15.3
Low 16.4 18.9 15.9
Medium 16.4 18.9 16.0
High 17.2 19.4 16.3
Portland, OR Background Traffic Basic Road closure I-5 North bound Contra Flow
Evacuation Time: Hours
None 9.5 12.5 9.4
Low 10.0 13.5 9.4
Medium 10.4 13.5 9.4
High 10.5 13.6 9.4
Las Vegas, NV Background Traffic Basic Road closure I-515 South bound Contra Flow
Evacuation Time: Hours
None 11.5 14.3 11.0
Low 11.9 15.0 11.2
Medium 12.1 15.2 11.2
High 12.2 15.3 11.4
Virginia Beach, VA Background Traffic Basic Road closure I-664 North bound Contra Flow
Evacuation Time: Hours
None 27.7 29.7 25.6
Low 27.9 29.7 25.6
Medium 28.0 29.7 25.9
High 28.6 29.7 26.0
Finding: When selected cities were tested for road closures and contra flow, evacuation times
changed proportionately.
32
SUMMARY: Parameter Variability was tested by running 144 scenarios that implemented
minor changes to the scenario inputs. Different levels of background traffic were used in all
scenarios, simulating vehicles using the roadways but not directly participating in the
evacuation. Testing included changes to population, seasonal population, background traffic,
people per vehicle, road closures and reversible lanes. Parameter Variability testing confirms
that RtePM is reliable and stable when changing input parameters and confirming expected
evacuation time results, as indicated in the Tables 6,7, and 8. Results are listed below:
• Population – When increasing population by 25%, the time to evacuate increased
proportionally due to the increased number of vehicles on the road. In the Baldwin
County, AL example, the evacuation time increased from 15.2 to 17.9 with a 25%
increase in population.
• Seasonal population – When adding seasonal population, the number of vehicles on the
roadways increased, therefore increasing evacuation.
• Background traffic – When background traffic increased, evacuation time increased
due to the increase in vehicles on the roadway.
• People per vehicle – As number of people per vehicle increased, evacuation time
decreased due to the fewer amount of vehicles on the road. As number of people per
vehicle decreased, evacuation time increased because there are more vehicles on the
road.
• Road closures and reversible lanes – Evacuation times increased as roads were closed
in all simulations. When adding contra-flow lanes, the evacuation times decreased in
all cases.
6. Historical Data Validation
JHU/APL validated RtePM utilizing the Mississippi Hurricane Evacuation Study, of April
2012 conducted by the US Army Corp of Engineers (USACE). This comparison study was
used as baseline against which the RtePM output was compared. To accomplish this, a
representative sampling of counties from the USACE study was selected and the RtePM tool
was used to create scenarios that were equivalent for comparisons to the USACE model
results. RtePM model parameters were adjusted to closely match the USACE model
parameters such as the evacuation response curves.
The first scenario tested was for Hancock County with maximum occupancy and people per
vehicle was set to 2 to match the USACE study. The end points for this comparison were in
all directions. The results are shown in Figure 9.
33
Figure 9 Hancock County Comparison
For the slow and medium evacuation response curves, RtePM results from JHU/APL and DDL
Omni Engineering closely match the USACE study. For the fast and immediate evacuation
response, the RtePM model run by DDL Omni Engineering yielded evacuation times that were
closer to the USACE study. The second comparison was for Jackson County with maximum
occupancy, 2 people per vehicle, and end points in all directions. The results are shown in
Figure 10.
11
8
6
6
11.1
8.1
5.2
1.3
11.3
8.3
5.3
3.1
0 2 4 6 8 10 12
Slow
Medium
Fast
Immediate
Hours to Evacuate
R
e
s
p
o
n
s
e
Hancock County
DDL OMNI
JHU-APL
USACE
34
Figure 10 Jackson County Comparison Scenario A
For all evacuation response curves the RtePM model yielded evacuation times that are shorter
than the USACE study and slightly higher than the JHU/APL study. RtePM performed well and
evacuation times are reliable.
For the next comparison, Jackson County was used again; however the end points for the
evacuation were restricted to travel east and north out of the evacuation zones. All other
parameters were the same. The results are shown in Figure 11.
12
10
7
7
11.2
8.2
5.3
4.3
11.4
8.4
6.2
5.2
0 2 4 6 8 10 12 14
Slow
Medium
Fast
Immediate
Hours to Evacuate
R
e
s
p
o
n
s
e
Jackson County Scenario A
DDL OMNI
JHU-APL
USACE
35
Figure 11 Jackson County Comparison Scenario B
In this instance the results yielded by DDL Omni Engineering were starkly shorter for all
evacuation response curves. The smallest difference is for the fast evacuation response where
the difference was 6 hours and 12 minutes. The largest difference is for the medium evacuation
response where the difference was 9 hours and 18 minutes.
SUMMARY: Based on these comparisons, it is clear that when compared to the USACE study,
RtePM results yield shorter forecast evacuation times in nearly all cases. However, the
forecasted difference for slow and medium evacuation response curves is not significantly
different when both models use evacuation routes out of the evacuation zone(s) in all directions.
This comparison shows that for fast and immediate evacuation response curves or when the
model scenario is designed to restrict traffic to specific directions, the RtePM model will likely
yield evacuation times that are significantly shorter for all response curves.
22
20
17
17
12.2
10.7
10.8
9.8
11.4
8.4
7.3
6.3
0 5 10 15 20 25
Slow
Medium
Fast
Immediate
Hours to Evacuate
R
e
s
p
o
n
s
e
Jackson County Scenario B
DDL OMNI
JHU-APL
USACE
36
JHU/APL also conducted three event validation studies in the following areas:
• Houston/Galveston
• Delaware
• Ocean City, Maryland
DDL Omni Engineering attempted to validate the JHU/APL studies to determine the quality of
their event validation. While attempting to duplicate the results of the JHU/APL studies, it was
discovered that there was not enough input data set information available for DDL Omni
Engineering to perform event validation in the Houston/Galveston or Delaware area. Numerous
attempts were made to duplicate and run the scenarios, but the results were unreliable. Additional
research and discussion with JHU/APL is needed to ensure the same JHU/APL parameters are
used when validating their results.
DDL Omni Engineering was able to perform testing using data available for Hurricane Irene in
the Ocean City, Maryland coastal region. The University of Maryland (UMD) Traffic Safety &
Operations Laboratory, A. James Clark School of Engineering, in partnership with the Maryland
Department of Transportation, State Highway Administration, pre-deployed vehicle counting
devices at specific locations along hurricane evacuation routes leading away from Ocean City,
Maryland. The results of these data counters are displayed in the UMD Count of Tables 9, 10,
and 11.
The results of JHU/APL RtePM model are displayed in the JHU/APL RtePM column. DDL
Omni Engineering validation results are displayed in the DDL RtePM column.
Three pre-deployed traffic counters that would produce traffic counts by hour at logical
evacuation end points were used:
• Ocean City Expressway (Rt. 90)
• Coastal Highway (Rt. 1)
• Ocean Gateway (Rt. 50)
The figures below show the cumulative traffic counts at these three specific locations by each
hour from the start of the evacuation for both the actual traffic data and RtePM. The x-axis is
hours and the y-axis is cumulative traffic count.
A. Ocean City Expressway - The evacuation times are nearly identical at the eighteen hour mark
as shown in Table 9 and Figure 12. The difference between the RtePM evacuee numbers and
the actual numbers can be attributed to human behavior/what time the evacuees decided to
evacuate.
37
Table 9 Ocean City Express Way (RT90) Vehicle Count
Hour JHU/APL RtePM DDL RtePM UMD Count
1:00 67 60 298
2:00 119 136 525
3:00 264 332 785
4:00 416 672 821
5:00 856 1286 881
6:00 1605 1984 1022
7:00 2343 2677 1368
8:00 3106 3393 1722
9:00 3843 4092 2105
10:00 4601 4802 2531
11:00 5338 5489 3021
12:00 6099 6192 3502
13:00 6550 6848 3964
14:00 6713 7238 4511
15:00 6855 7462 5164
16:00 6908 7548 5887
17:00 6993 7618 6508
18:00 6994 7624 6942
Figure 12 Ocean City Express Way (RT90) Graph
38
B. Coastal Highway (RT1) - The weighting of endpoints plays an important factor in the
analysis of the Coastal Highway shown in Table 10 and Figure 13. There is not enough
information about the weighting of endpoints in the JHU/APL reports for DDL Omni
Engineering to determine validity. If DDL Omni Engineering increases endpoint
weighting for the Coastal Highway, we can duplicate the road counter and JHU/APL
results, but there is not enough data to make a fair evaluation. More research is needed.
Table 10 Coastal Highway (RT1) Vehicle Count
Hour JHU/APL RtePM DDL RtePM UMD Count
1:00 83 76 201
2:00 209 145 430
3:00 363 244 791
4:00 680 328 1209
5:00 1021 412 1652
6:00 1344 496 2063
7:00 1693 580 2513
8:00 2014 664 2982
9:00 2365 748 3417
10:00 2688 832 3726
11:00 3037 916 3960
12:00 3358 1000 4098
13:00 3709 1084 4192
14:00 4028 1168 4193
15:00 4209 1252 4196
16:00 4317 1331 4210
17:00 4394 1412 4234
18:00 4395 1413 4259
39
Figure 13 Coastal Highway (RT1) Graph
C. Ocean Gateway (Rt. 50) - The difference between the RtePM evacuee numbers and the
actual numbers can be attributed to human behavior/what time the evacuees decided to
evacuate. Model results are only as accurate as the input that goes in the model, and
human behavior is hard to predict. The number of people that decided to evacuate, and
when they evacuated represent the difference in the results of Table 11 and Figure 14.
Table 11 Ocean Gateway (RT50) Vehicle Count
Hour JHU/APL RtePM DDL RtePM UMD Count
1:00 68 75 871
2:00 119 149 1754
3:00 276 315 2832
4:00 495 571 4289
5:00 852 995 5632
6:00 1568 1913 6882
7:00 2758 3387 7676
8:00 4612 5413 8401
9:00 6749 7709 8776
10:00 8692 9920 9060
11:00 9980 11779 9287
12:00 10788 13188 9509
13:00 11204 13847 9690
14:00 11431 14136 9798
15:00 11581 14324 9866
16:00 11658 14423 9926
17:00 11732 14512 10816
18:00 11735 14525 11604
40
Figure 14 Ocean Gateway (RT50) Graph
Parameter-variability testing using background traffic and endpoint weighting was also
conducted in Baldwin county Alabama by JHU/APL.
Table 12 displays a comparison between JHU/APL data and DDL Omni Engineering data
when changing background traffic in RtePM. The results are satisfactory. There is a slight
difference in the results due to the fact that the exact parameters used in the JHU/APL scenario
were not available in our testing, inputs may have varied slightly.
Table 12 Background Traffic
EVACUATION TIMES FOR BALDWIN COUNTY AL
Background Traffic JHU-APL Evacuation Time DDL Omni Evacuation Time
None 16.1 hours 16.3 hours
Low 16.6 hours 16.4 hours
Medium 16.7 hours 16.4 hours
High 17.5 hours 17.2 hours
Table 13 displays a comparison between JHU/APL data and DDL Omni Engineering data
when changing End Point weighting. The results are satisfactory. There is a slight difference
in the results due to the fact that the exact parameters used in the JHU/APL scenario were not
available in our testing, inputs may have varied slightly.
41
Table 13 End Point Weighting
VALIDATION OF EVACUATION TIME WHEN CHANGING END POINT WEIGTING BALDWIN COUNTY AL
Basic Scenario Evacuation time
I-65 End Point Weighted at 70%
I-65 End Point Weighted at 75%
I-65 End Point Weighted at 80%
Evacuation time: Hours
Evacuation time: Hours
Evacuation time Hours
JHU DDL JHU DDL JHU DDL JHU DDL
16.1 16.3 18.6 18.3 19.6 19.3 20.6 20.3
End Point % of Evacuating
Vehicles
% of Evacuating Vehicles
% of Evacuating Vehicles
% of Evacuating Vehicles
JHU DDL JHU DDL JHU DDL
US-31 12.1 7.5 10.8 7.0 9.9 5.6 7.7
Muscogee Road
2.3 1.6 1.4 1.3 1.1 1.1 1.0
AL-59 8.5 4.2 2.0 3.8 1.6 2.4 0.9
I-65 67.0 79.4 79.0 81.9 81.9 85.6 85.8
CR-47 0.9 0.4 0.4 0.4 0.3 0.3 0.3
I-10 9.3 6.9 6.4 5.6 5.2 5.0 4.4
JHU/APL conducted a Face Validation study by consulting with SMEs to determine if the model
seemed reasonable to people who were knowledgeable about evacuation planning. The
following comments were documented in the RtePM Comparison Study conducted by Johns
Hopkins University, Applied Physics Laboratory, May 2012.11
• Brandon Bolinski, Alabama Emergency Management: The tool appeared at first to have
many steps, but after walking through the steps, it was found to be user friendly and
ultimately very intuitive. Roy Dunn concurred with Brandon’s comment about the user
friendly aspect of the tool as well as the decision to operate the tool on the ESRI Flex
platform. John Eringman commented as to the speed of the tool; with David George of
JHU/APL noting that the speed is a factor and we are working to enhance that
functionality. John was particularly interested in conducting a full county and multiple
county evacuations for Alabama with it.
• FEMA Region III, Regional Advisory Council: Value of RtePM is that it could predict
numbers of evacuees into the area.
11 Real Time Evacuation Planning Model (RtePM) Comparison Document, JHU/APL, May 2012
42
• Department of Homeland Security (DHS) Science and Technology (S&T) 1st Responder
Working Group: Overall you have done a miraculous job developing this model. It is
easy to use and gives hr by hr results.
• The National Emergency Management Association (NEMA) will support the
development, maintenance and training for the RtePM and formally endorse the product.
RtePM Focus Group, National Hurricane Program (NHP) – ICCOH with NEMA
Subcommittee
• Cecil County, Maryland Department of Emergency Services: Excellent, real world results
and considerations.
• Pacific Northwest National Laboratory: This can be a very valuable tool - especially for
smaller localities that will not have financial resources to develop such a concept on their
own. Also think the concept of a "shared/standardized" knowledge base will be valuable
- much like HURREVAC makes it easy for folks not based locally to see/use/collaborate
if information and platform standard.
• NEMA Response and Recovery Committee: Overall, an exciting and interesting tool.
However, to be applicable to a highly urbanized environment such as New York City
there must be a way to incorporate public transit information, and to account for car
ownership of less than one car per household.
• Homeland Security Infrastructure Foundation-Level Data (HIFLD) Staff: Additionally, I
did create scenarios for the Delmarva, and for our two nuclear power plants for
comparison purposes to existing studies, and the numbers verified quite well against the
numbers on file in our existing studies! That alone provided a higher confidence in the
results. I’ll look into some more items for suggestions, but again this is a very good
move forward.
• Virginia Department of Emergency Management: This is an easy to use tool that is very
intuitive to use. I like the interface and the mapping. If this tool is made available for
future use, there needs to be commitment that the information would be a current as
possible.
• DHS S&T Evacuation Symposium: Really good tool for special evacuations – Nuclear
power plants, dams, wild land fires, venue applications, etc.
• Office of Emergency Management, San Diego County, California: Strength of tool -
modeling displays choke points, indicated how / where / when.
43
DOCUMENT SUMMARY
The purpose of this report is to provide results of the Independent Verification and Validation
(IV&V) of the Real Time Evacuation Planning Model (RtePM). Focus was centered on ensuring
RtePM meets system requirements and fulfills its intended purpose by employing Conceptual
and Operational Validation Techniques.
CONCEPTUAL VALIDATION
DDL Omni Engineering SME’s took the approach to identify what an evacuation
model/simulation should have in order to be an effective evacuation tool. We called this
conceptual validation and the following steps were accomplished in order to determine whether
RtePM had the features of an effective evacuation tool:
1. Identified the purpose of RtePM and the intended users of the model/simulation.
2. Defined critical features that an evacuation planning model must have in order to meet
the purpose of the planning model. DDL Omni Engineering identified the eight critical
features below:
a. Type of disaster- Determine evacuation time for any type of disaster.
b. Population density- The model must accurately model populations.
c. Roadway configuration- The model must accurately display roads and routes.
d. Traffic Density- The model must consider the effects of congestion.
e. Time to Evacuate- The model must accurately determine evacuation time.
f. Emergency Services- Should model the effects of emergency services.
g. Capabilities- The model/system capabilities and ease of use.
h. Weather- The model should consider the impact of weather.
3. Developed essential elements or requirements of a planning model to make the critical
features function properly.
Our Conceptual validation findings determined that RtePM meets all the critical features that an
effective evacuation planning model/simulation should have except considering the impact of
weather and the effects of emergency services. However, we believe workarounds such as
changing speed limits, or closing roads/bridges/tunnels can simulate the adverse effects of
weather. Locations of emergency services such as Police and Fire stations would be beneficial.
The results of RtePM’s ability to meet the critical features and essential elements are
documented in Appendix B.
44
OPERATIONAL VALIDATION
In accomplishing Operational validation, we used the following techniques to develop model
confidence. Each of these techniques has specific application criteria that were used as a
guideline for model validation.
1. Data Validity- This validation technique was used to validate the population and roadway
facilities databases in RtePM. Using this technique, DDL Omni Engineering found that
the critical features “population” and “ roadway configuration” are modeled accurately in
RtePM data.
a. Population- RtePM population data was identical to U.S. Census/LandScan
population data for the seven U.S. cities and the thirty-five census ID blocks
sampled.
b. Roadway Configuration- We selected fifteen well known roads and routes in the
Hampton Roads area to evaluate and found all fifteen to be accurately portrayed
in RtePM. In twenty-nine of the thirty instances tested outside of Hampton
Roads, RtePM maps and data accurately compared to Google Maps. In one of the
thirty instances tested, the road model data was inconsistent with the RtePM map
and Google Maps and may affect evacuation time results. DDL Omni
Engineering recommends the RtePM ‘Roads Tab’ be updated by local emergency
managers who are familiar with local roads and routes
2. Comparison to other models- Comparison of other evacuation models with RtePM was
not successful due to time constraints, accessibility, complexity, and the lack of
comparable capabilities. The resulting validation would be suspect, with a reduced level
of confidence in the accuracy of the comparisons. Comparison of the RtePM model to
other models was not accomplished
3. Event Validity- This validation technique evaluated the results of running the RtePM
model/simulation to real world results, e.g. model/simulation evacuation times were
compared to actual PSA evacuation times for hurricanes or the NRC evacuation case
studies.
a. NRC- Evacuation case studies for the North Anna, Bellefonte, Callaway, and
Nine-Mile nuclear power plants were used to compare evacuation times with
RtePM. NRC evacuation time results were comparable to the evacuation times of
RtePM.
b. PSA- The following PSA were compared to RtePM:
i. Hurricane Hugo- Clearance times calculated for FEMA/Corps studies
compared well with the actual times experienced in Hurricane Hugo.
ii. Hurricane Opal-.RtePM evacuation times were acceptable when
compared to actual evacuation times of Escambia and Santa Rosa
Counties.
45
iii. Hurricane Floyd- The path of Hurricane Floyd and the uncertainty
which existed regarding its potential landfall, caused massive
evacuations of populations in the Florida, Georgia, South Carolina, and
North Carolina coasts. RtePM, actual and FEMA evacuation times
were comparable.
iv. Hurricane Andrew- The Dade and Broward County actual evacuation
times were slightly less than RtePM calculated times.
v. Hurricane Ike- The 2010 USACE HES updated clearance times were
generated based on differing intensity strengths of hurricanes, levels of
background traffic, and the rapidity of response by evacuees, and
different tourist occupancy levels. RtePM calculated times compared
favorably to the 2010 USACE HES times.
vi. Hurricane Bonnie- RtePM evacuation times were closer to actual
evacuation times than FEMA predicted evacuation times.
4. Internal Validity- Several runs of the model were made to determine the amount of
(internal) variability in the model. When duplicating scenarios and running the scenario
multiple times, the evacuation times remained the same, indicating model reliability.
5. Parameter Variability-Sensitivity Analysis- This validation technique changed the
model’s input and initial condition parameters to determine the effect upon the model and
its output. Parameter Variability was tested by running 144 scenarios that implemented
minor changes to the scenario inputs. Testing included changes to population, seasonal
population, background traffic, people per vehicle, road closures and reversible lanes.
Parameter Variability testing confirms that RtePM is reliable and stable when changing
input parameters.
6. Historical Validation- Using this validation technique we validated the efforts that have
already been performed by the original developer. The data from the JHU/APL
Comparison Document, dated May 2012, was validated by DDL Omni Engineering for
this report.
The current method of developing evacuation studies may take months to complete, resulting in
expensive, possibly inaccurate plans that are outdated upon completion. With RtePM, a planner
can quickly modify a scenario, and see modified results within minutes. Planners can view,
understand, and manage evacuation plans as changes in the evacuation plan become necessary,
or to quickly create and examine alternatives.
As a part of the validation process, we ran large, medium, and small scale scenarios. When
simulating a small geographic area, new calculations are completed within seconds. A
simulation for a densely populated area can take up to two hours. For example, when
46
calculating for an area with a population of nearly two million, and seventy five percent of them
evacuating, the total run time to predict the evacuation time was about two hours.
The affordability of RtePM should also be considered. RtePM uses open source software, it's
Web-based, and it uses population and road-network data sets that local agencies can access for
free. Evacuation planners can update their emergency plans more frequently for less cost than
conventional evacuation studies that may take months to complete.
47
RECOMMENDATIONS
DDL Omni Engineering recommends further development of RtePM. When compared to other
evacuation models in use today, it has proven to be a fast, reliable, and easy to use system. The
observations listed below were compiled by DDL Omni Engineering during the IV&V process,
and should be considered for future development. These recommendations in no way diminish
the effectiveness or validity of the current version of RtePM:
• Recommend developing a planning window that is clear of the map when building a
scenario. An evacuation planning wizard interface would be useful down the left hand
side to show user progress of planning while map is still visible.
• Reversible flow function is difficult to operate. The reversible lane function needs to
be modified for ease of operation. When selecting a large section of highway for
reversible lanes, multiple road segments need to be selected. For example, Hampton
Roads to Richmond required approximately 50 road segments to be selected.
• Need to ensure that roads are easily updated on a yearly basis due to new construction.
• Update User’s Guide. Help window with user’s guide embedded in tool may be useful.
• Include Mass Transit in future builds. Not everyone owns a car in the National Capital
Region. They rely on mass transit.
• Configurable shelter location should be automated. Shelters may be added to the
evacuation area using the “Add” icon, but known shelters could be included in the
software, including capacity sizes. The number of people sheltering in place will affect
roadway congestion and evacuation times
• Although RtePM is not designed to consider weather, weather factors are important in
order to model dispersion direction, specifically wind direction and speed. This would
be critical in the case of wild fire evacuation planning, where smoke from the fire could
reduce visibility for driving in one direction, and not the other. Another instance where
wind direction would be critical is in the case of a dirty-bomb. Wind direction and
speed would dictate the area to be evacuated, direction of escape, or the need to shelter
in place. Currently the effects of weather can be simulated with workarounds such as
speed limits, road/bridge closures and people per car.
• Flood zone overlay would be useful in planning for evacuation. Enhanced topography
needs to be included. Terrain features that adequately identify potential flood zones
leading to road closures would be an enhancement that could be considered. In the
case of tidal surges during hurricanes, many roads are known to be underwater.
48
REFERENCES
A. Real Time Evacuation Planning Model (RtePM) Independent Validation and Verification
Plan, 16 October 2012
B. Sargent, R.G. (2010) “Verification and Validation of Simulation Models,” in the
Proceedings of the 2010 Winter Simulation Conference
C. National Research Council. 2012. Assessing the Reliability of Complex Models:
Mathematical and Statistical Foundations of Verification, Validation, and Uncertainty
Quantification. Washington, D.C.: The National Academies Press.
D. Department of Defense Standard Practice. Documentations of Verification, Validation,
and Accreditation (VV&A) for models and simulations MIL-STD-3022. 28 January
2008.
E. Real Time Evacuation Planning Model (RtePM) Comparison Document, May 2012;
Prepared by John Hopkins University, Applied Physics Laboratory.
F. Development of Evacuation Time Estimate Studies for Nuclear Power Plants, January
2005; Prepared by L.J Datson and J. Jones, Sandia National Laboratories, P.O. Box 5800,
Albuquerque, NM 87185; http://www.nrc.gov/reading-rm/doc-
collections/nuregs/contract/cr6863/cr6863.pdf
G. Maryland Hurricane Evacuation Study, December 1990; Prepared for Maryland
Emergency Management Agency FEMA Region III and U.S. Army Corps of Engineers
(USACE), Baltimore District; Prepared by Baltimore District, USACE under direction of
Colonel Frank R. Finch.
H. Mississippi Hurricane Evacuation Study Transportation Analysis, Final Report, April
2012; Prepared by Mobile District, USACE.
I. Army Corps of Engineers Technical Guidelines for Hurricane Evacuation Studies
September 1995; Prepared by USACE Wilmington District’s Flood Plan Management
Services Branch.
J. Technical Report, Dynamically Modeling Hurricane Evacuation Decisions, June 2007;
International Hurricane Research Center, Florida International University;
http://www.ihrc.fiu.edu, 11200 SW 8th Street, University Park, MARC 360
Miami, Florida 33199
49
K. Modeling and Simulation of Staged Evacuations: A Case Study of Hurricane
Evacuations of Galveston Island; Xuwei Chen, Department of Geography, Northern
Illinois University, Dekalb, Ill. 60115
L. Hurricane Evacuation in Delaware, University of Delaware; REU Program Summer
2008 Disaster Research Center Science and Engineering Scholars, University
Transportation Center; Sarah Dalton, University of Delaware. http://www.ce.udel.edu/UTC/Dalton_Summer08
M. 2010 US Census Report; U.S. Department of Commerce, U.S. Census Bureau;
http://www.census.gov/2010census/
N. Virginia Hurricane Evacuation Guide, 2011; Commonwealth of Virginia, Department
of Transportation; 10501 Trade Court, Richmond, Virginia 23236,
http://www.vaemergency.gov/news/news-releases/2012/hurricane-guide
O. Dominion Power, North Anna 3 Combined License Application, Emergency Plan,
November 2007.
P. Bellefonte Nuclear Plant Development of Evacuation Time Estimates, September
2007; http://pbadupws.nrc.gov/docs/ML0731/ML073110546. ; KLD Associates Inc. 48
Mall Drive, Suite 8, Commack, NY 11725; Mr. Jay Maisler, Enercon
Corporation,14502 North Dale Mabry Hwy, Suite 200, Tampa, FL 33618
Q. Callaway Plant Evacuation Time Estimate report, June 2012; Callaway Plant Unit 1,
Union Electric Co. Facility Operating License NPF-30; Thomas Elwood (314) 225 -
1905;http://pbadupws.nrc.gov/docs/ML1220/ML12200A026.pdf
R. Nine Mile Point Development of Evacuation Time Estimates; November 2012,
Prepared for Constellation Energy and Entegry; Prepared by KLD Engineering, P.C
43 Corporate Drive, Hauppauge, NY 11788; mailto:[email protected]
S. Hurricane Isabel Assessment, March 2005; NOAA Coastal Services Center,
Hurricane Planning and Impact; https://www.csc.noaa.gov/hes. Prepared for USACE,
Philadelphia and Wilmington District and Federal Emergency Management Agency
(FEMA) Region III & IV; Prepared by Post, Buckley, Schuh and Jeringan, Inc. 1901
Commonwealth Lane, Tallahassee, Fl. 32303.
T. Hurricane Hugo Assessment, January 2000; NOAA Coastal Services Center,
Hurricane Planning and Impact; https://www.csc.noaa.gov/hes. Prepared for USACE,
South Atlantic District and Federal Emergency Management Agency (FEMA) Region
50
IV; Prepared by Post, Buckley, Schuh and Jeringan, Inc. 134 South Bronough St.
Tallahassee, Fl. 32301.
U. Hurricane Opal Assessment, October 1995; NOAA Coastal Services Center,
Hurricane Planning and Impact; https://www.csc.noaa.gov/hes. Prepared for USACE,
Mobile District and Federal Emergency Management Agency (FEMA) Region IV;
Prepared by USACE, Philadelphia District, September 1996.
V. Hurricane Floyd Assessment, May 2000; NOAA Coastal Services Center, Hurricane
Planning and Impact; https://www.csc.noaa.gov/hes. Prepared for USACE, Savanna
District and Federal Emergency Management Agency (FEMA) Region IV; Prepared
by Post, Buckley, Schuh and Jeringan, Inc. 1901 Commonwealth Lane, St.
Tallahassee, Fl. 32303.
W. Hurricane Andrew Assessment, January 1993; NOAA Coastal Services Center,
Hurricane Planning and Impact; https://www.csc.noaa.gov/hes. Prepared for USACE,
South Atlantic Division and Federal Emergency Management Agency (FEMA)
Region III & IV; Prepared by Post, Buckley, Schuh and Jeringan, Inc. 134 South
Bronough St. Tallahassee, Fl. 32301.
X. Framework for Modeling Emergency Evacuation, University of Central Florida, 2005
Y. A Case Study of Hurricane Evacuations of Galveston Island
Z. Hooks, Elise-Miller, and Tarnoff, Phil. “Traffic Signal Timing for Urban Evacuation:
Draft Final Report”, Maryland Center for Advanced Transportation Technology,
University of Maryland for the Federal Highway Administration. 18 August 2005
AA. Chen, M., L. Chen and E. Miller-Hooks (2007). “Traffic Signal Timing for Urban
Evacuation, ”Special Emergency Transportation Issue of the ASCE Journal of Urban
Planning and Development
51
APPENDIX A
TEST PLAN
M#s MEASURE METRIC DATA
ELEMENT
MEASUREMENT
METHOD DATA ANALYSIS
Does RtePM consider evacuation times and routes for different types of disasters? M1-M11.12
1. Man-made
disasters
M1 a. Chemical Y/N Test director
observation
Run RtePM
scenarios,
observe
evacuation time
results, conduct
interviews from
SME’s, distribute
and collect
surveys from
SME’s.
If Test director
observes
positive results
from model
runs, and gains
positive
feedback from
SME’s/sharehol
ders, these
measures will
be evaluated as
satisfactory
i. Dispersion
of lethal
clouds due
to wind
speed and
direction
Y/N Test director
observation
Run RtePM
scenarios,
observe
evacuation time
results, conduct
interviews from
SME’s, distribute
and collect
surveys from
SME’s.
If Test director
observes
positive results
from model
runs, and gains
positive
feedback from
SME’s/sharehol
ders, these
measures will
be evaluated as
12
RtePM is an evacuation model used to calculate evacuation times for any type of disaster. It is designed for the user to select a specific area to evacuate, no matter what the disaster may be. The model is designed to be used anywhere that road and population data can be obtained
52
M#s MEASURE METRIC DATA
ELEMENT
MEASUREMENT
METHOD DATA ANALYSIS
satisfactory
ii. Persistency
of lethal
clouds
based upon
dispersion
and time
Y/N Test director
observation
Run RtePM
scenarios,
observe
evacuation time
results, conduct
interviews from
SME’s, distribute
and collect
surveys from
SME’s.
If Test director
observes
positive results
from model
runs, and gains
positive
feedback from
SME’s
/shareholders,
these measures
will be
evaluated as
satisfactory
M2 b. Nuclear
(terrorist
attack)
Y/N Test director
observation
Run RtePM
scenarios,
observe
evacuation time
results, conduct
interviews from
SME’s, distribute
and collect
surveys from
SME’s.
If Test director
observes
positive results
from model
runs, and gains
positive
feedback from
SME’s/sharehol
ders, these
measures will
be evaluated as
satisfactory
M3 c. Conventional
Explosive
Y/N Test director
observation
Run RtePM
scenarios,
observe
evacuation time
results, conduct
interviews from
SME’s, distribute
If Test director
observes
positive results
from model
runs, and gains
positive
feedback from
53
M#s MEASURE METRIC DATA
ELEMENT
MEASUREMENT
METHOD DATA ANALYSIS
and collect
surveys from
SME’s.
SME’s/sharehol
ders, these
measures will
be evaluated as
satisfactory
M4 d. Fire Y/N Test director
observation
Run RtePM
scenarios,
observe
evacuation time
results, conduct
interviews from
SME’s, distribute
and collect
surveys from
SME’s.
If Test director
observes
positive results
from model
runs, and gains
positive
feedback from
SME’s/sharehol
ders, these
measures will
be evaluated as
satisfactory
2. Natural Disasters
M5 a. Hurricane Y/N Test director
observation
Run RtePM
scenarios,
observe
evacuation time
results, conduct
interviews from
SME’s, distribute
and collect
surveys from
SME’s.
If Test director
observes
positive results
from model
runs, and gains
positive
feedback from
SME’s/sharehol
ders, these
measures will
be evaluated as
satisfactory
M6 b. Earthquake Y/N Test director
observation
Run RtePM
scenarios,
observe
If Test director
observes
positive results
54
M#s MEASURE METRIC DATA
ELEMENT
MEASUREMENT
METHOD DATA ANALYSIS
evacuation time
results, conduct
interviews from
SME’s, distribute
and collect
surveys from
SME’s.
from model
runs, and gains
positive
feedback from
SME’s/sharehol
ders, these
measures will
be evaluated as
satisfactory
M7 c. Flooding Y/N Test director
observation
Run RtePM
scenarios,
observe
evacuation time
results, conduct
interviews from
SME’s, distribute
and collect
surveys from
SME’s.
If Test director
observes
positive results
from model
runs, and gains
positive
feedback from
SME’s/sharehol
ders, these
measures will
be evaluated as
satisfactory
M8 d. Fires Y/N Test director
observation
Run RtePM
scenarios,
observe
evacuation time
results, conduct
interviews from
SME’s, distribute
and collect
surveys from
SME’s.
If Test director
observes
positive results
from model
runs, and gains
positive
feedback from
SME’s/sharehol
ders, these
measures will
be evaluated as
satisfactory
55
M#s MEASURE METRIC DATA
ELEMENT
MEASUREMENT
METHOD DATA ANALYSIS
3. Catastrophic
accidents
M9 a. Aircraft
crashes
Y/N Test director
observation
Run RtePM
scenarios,
observe
evacuation time
results, conduct
interviews from
SME’s, distribute
and collect
surveys from
SME’s.
If Test director
observes
positive results
from model
runs, and gains
positive
feedback from
SME’s/sharehol
ders, these
measures will
be evaluated as
satisfactory
M10 b. Train wrecks
/derailments
Y/N Test director
observation
Run RtePM
scenarios,
observe
evacuation time
results, conduct
interviews from
SME’s, distribute
and collect
surveys from
SME’s.
If Test director
observes
positive results
from model
runs, and gains
positive
feedback from
SME’s/sharehol
ders, these
measures will
be evaluated as
satisfactory
M11 4. Industrial
accidents
(Factory or
Vehicular)
Y/N Test director
observation
Run RtePM
scenarios,
observe
evacuation time
results, conduct
interviews from
SME’s, distribute
and collect
If Test director
observes
positive results
from model
runs, and gains
positive
feedback from
SME’s/sharehol
56
M#s MEASURE METRIC DATA
ELEMENT
MEASUREMENT
METHOD DATA ANALYSIS
surveys from
SME’s.
ders, these
measures will
be evaluated as
satisfactory
Does RtePM utilize the most current population estimates and is population database
adjustable? M12-M17
M12 1. Source of
population data
Y/N-
within 5%
accuracy
1. US Census
data from
2010.
2. RtePM
Total
Population
data
Sample small,
medium and
large population
areas to
compare
population
values of RtePM
with that of
2010 Census
If Test director
observes
positive results
from
population
comparisons,
these measures
will be
evaluated as
satisfactory
M13 2. Adjustable
density levels
Y/N-100%
accurate
1. Population
change
2. Total
population
3. Total
Vehicles
needed to
evacuate
Test for accurate
change in
population
density by
adjusting the
population
change block (%)
by 10-25% in
order to
determine that
these changes
affect total
population and
total vehicles
results in RtePM.
If Test director
observes
positive results
from
population
density
comparisons,
these measures
will be
evaluated as
satisfactory
57
M#s MEASURE METRIC DATA
ELEMENT
MEASUREMENT
METHOD DATA ANALYSIS
M14 3. Seasonal
Scalability
Y/N-100%
accurate
Seasonal
population
increase and
total vehicles
required to
evacuate
Validation of
populations
when adding or
subtracting for
seasonal
variations will be
investigated for
correctness.
If Test director
observes
positive results
from
population
seasonal
scalability
comparisons,
these measures
will be
evaluated as
satisfactory
M15 4. Number of
evacuees per
vehicle
Y/N-100%
accurate
People/Vehic
le block and
total vehicle
Test for accurate
change in total
number of
vehicles by
adjusting the
people/vehicle
block (1-4).
If Test director
observes
positive results
from the total
vehicle
comparison
test, these
measures will
be evaluated as
satisfactory
M16 5. Shelters/Evacuat
ion Centers
Y/N Viewable
shelters
Add/Subtract
shelters in
various
scenarios
If Test director
observes
positive results
from shelter
manipulation
comparisons,
these measures
will be
evaluated as
satisfactory
M17 a. Capacity Y/N Shelter Change capacity
58
M#s MEASURE METRIC DATA
ELEMENT
MEASUREMENT
METHOD DATA ANALYSIS
Capacity of shelter
Does RtePM model the most current and accurate Roads and Routes? M18-M25
M18 1. Must have
logical evacuee
flow
Y/N 1. End Points Reconfigure end
points, location
of end points,
number of
endpoints
If Test director
observes
positive results
for a logical
traffic flow,
these measures
will be
evaluated as
satisfactory
M19 2. Number of lanes
available
Y/N 1. RtePM
2. Google
Maps-Street
View
Go to Websites
(Google) and
compare with
RtePM # of lanes
data.
If Test director
observes
positive results
after changing
number of lanes
available, these
measures will
be evaluated as
satisfactory
M20 3. Use of reversible
lanes
Y/N Add use of
reversible
lanes
Build scenarios
with reversible
lanes and
calculate
evacuation
times for
different
scenarios
If Test director
observes
positive results
with the use of
reversible lanes,
these measures
will be
evaluated as
satisfactory
M21 4. Use of shoulder
lane
Y/N Add use of
shoulder
traffic
Include use of
shoulder in
different
If Test director
observes
positive results
59
M#s MEASURE METRIC DATA
ELEMENT
MEASUREMENT
METHOD DATA ANALYSIS
scenarios and
determine if
evacuation time
changes
accordingly
from use of a
shoulder lanes,
these measures
will be
evaluated as
satisfactory
M22 5. Road closures Y/N Close roads Close roads
during
evacuation
scenario
construction and
determine if
evacuation
times differ
If Test director
observes
positive results
from the closing
of roads, these
measures will
be evaluated as
satisfactory
a. Tunnels
blocked
Y/N Block tunnels
b. Roads blocked Y/N Block roads
c. Bridges
blocked
Y/N Block bridges
M23 6. Road speed limit Y/N 1. RtePM
2. Google
Maps-Street
View
Go to Websites
(Google) and
compare with
RtePM Road
Speed Limit
data.
If Test director
observes
positive results
when speed
limit changes
are made, these
measures will
be evaluated as
satisfactory
M24 7. Identify
bottlenecks
Y/N Insert
bottlenecks
Identify known
bottlenecks/brid
ges, tunnels,
merges, make
changes to plan
If Test director
observes
positive results
when
considering
60
M#s MEASURE METRIC DATA
ELEMENT
MEASUREMENT
METHOD DATA ANALYSIS
and determine
calculation
changes
bottlenecks,
these measures
will be
evaluated as
satisfactory
a. Bridges Y/N
b. Tunnels Y/N
c. Merges Y/N
M25 8. Alternate routes Y/N Change
endpoints
Add/Delete
endpoints and
observe change
in Time to
evacuate and
direction of
evacuation.
If Test director
observes
positive results
from change in
endpoint, this
measures will
be evaluated as
satisfactory
Does RtePM model make sense to the user when modeling the effects of traffic M26-
M29
M26 1. Traffic Density Y/N None
Low
Medium
High
Run scenarios
with different
levels of traffic
density and
observe change
in evacuation
time.
If Test director
observes
positive results
when traffic
density
variables are
changed, these
measures will
be evaluated as
satisfactory
a. Congestion Y/N
i. Number of Y/N
61
M#s MEASURE METRIC DATA
ELEMENT
MEASUREMENT
METHOD DATA ANALYSIS
vehicles
ii. Seasonal
tourists
Y/N
b. Accidents Y/N
c. Bridges
blocked
Y/N
d. Tunnels
blocked
Y/N
e. Roads blocked Y/N
f. Railways
blocked
Y/N
M27 2. Time of Day Y/N Nighttime
(Census)
Daytime/Wor
kweek
(Land Scan)
Run scenarios
with different
time of day and
observe change
in evacuation
time.
If Test director
observes
positive results
when time of
day variables
are entered,
these measures
will be
evaluated as
satisfactory
M28 3. Realistic effects
of congestion on
travel routes,
speeds and trip
lengths
Y/N Increase/Dec
rease
congestion
levels
Run scenarios
with different
congestion
levels and
observe change
in evacuation
time, speed.
If Test director
observes
positive results
from time of
evacuation due
to traffic
congestion,
these measures
will be
evaluated as
satisfactory
62
M#s MEASURE METRIC DATA
ELEMENT
MEASUREMENT
METHOD DATA ANALYSIS
M29 4. Fuel Availability Y/N N/A N/A
Does RtePM accurately determine Time to Evacuate M30-M33
M30 1. When
evacuation starts
Y/N Evacuation
Start time
Observe change
in evacuation
time when
changing
evacuation start
time.
If Test director
observes
positive results
when time of
evacuation is
changed, these
measures will
be evaluated as
satisfactory
M31 2. Staggered
evacuations
Y/N Stagger
evacuation
start time
Observe change
in evacuation
time when
staggering
evacuations.
If Test director
observes
positive results
when using
staggered
evacuations,
these measures
will be
evaluated as
satisfactory
M32 3. Evacuation zones Y/N Add/Delete
evacuation
zones
Observe change
in evacuation
time when
adding or
deleting
evacuation zone.
If Test director
observes
positive results
when
evacuation
zones are
modified, these
measures will
be evaluated as
satisfactory
M33 4. Advance notice Y/N N/A If Test director
63
M#s MEASURE METRIC DATA
ELEMENT
MEASUREMENT
METHOD DATA ANALYSIS
observes
positive results
when advanced
notice is given,
these measures
will be
evaluated as
satisfactory
Does RtePM provide visual displays that are user friendly M34-M35
M34 1. Provide visual
displays
If Test director
observes
positive results
with reference
to visual
displays, these
measures will
be evaluated as
satisfactory
a. Locations of
incidents in
familiar terms
(i.e., street
addresses vice
grid
coordinates or
lat/long)
Y/N
b. Global maps
capable of
zooming to a
building in a
city
Y/N
c. Operator
friendly
Y/N
64
M#s MEASURE METRIC DATA
ELEMENT
MEASUREMENT
METHOD DATA ANALYSIS
graphical user
interface (GUI)
d. Display terrain
features
Y/N
M35 2. Rapid Use Case
generation
capability
Y/N If Test director
observes
positive results
for rapid
scenario
generation,
these measures
will be
evaluated as
satisfactory
a. Ability to easily
export and
modify Use
Case data from
one Use Case
to another
Y/N
b. Ability to
import real
data from
databases
Y/N
3. Ability to save a
Use Case
Y/N
Does RtePM consider the impact of weather on evacuation time M36
M36 1. Does the
evacuation time
change with
Y/N Slow traffic with
higher density,
lower speeds,
If Test director
observes
positive results
65
M#s MEASURE METRIC DATA
ELEMENT
MEASUREMENT
METHOD DATA ANALYSIS
changes in the
weather?13
and closed
roads.
from evacuation
times, these
measures will
be evaluated as
satisfactory
13
RtePM is not designed to account for changes in the weather
66
APPENDIX B
CRITICAL FEATURES AND ESSENTIAL ELEMENTS
DDL Omni Engineering identified eight critical features and the underlying essential elements of
an effective evacuation model, before operating RtePM. After gaining access to RtePM,
observations were made and identified in the right hand column.
CF
&
EE #’s
CRITICAL FEATURES AND
ESSENTIAL ELEMENTS COMMENTS
The model shall represent, with acceptable accuracy, the effects of the following Critical Factor
and Essential Elements:
CF1000 I. Different types of Disasters RtePM is an evacuation tool used for
the types of disasters listed below. It
is designed to select a specific area to
evacuate, no matter what the disaster
may be.
EE1001 1. Man-made disasters
EE1002 a. Chemical
EE1003 i. Dispersion of lethal clouds
due to wind speed and
direction
Weather factors need to be included
in order to model dispersion
direction. Specifically wind direction
and speed.
EE1004 ii. Persistency of lethal clouds
based upon dispersion and
time
Weather factors need to be included
in order to model dispersion
direction.
EE1005 b. Nuclear (terrorist attack) Easy to identify evacuation zone and
time to evacuate
EE1006 c. Conventional Explosive Easy to identify evacuation zone and
time to evacuate
EE1007 d. Fire Easy to identify evacuation zone and
time to evacuate
EE1008 2. Natural Disasters
EE1090 a. Hurricane Functionality of model provides for
multi-day evacuation which is needed
for hurricane evacuation
EE1010 b. Earthquake Easy to identify evacuation zone and
time to evacuate
EE1011 c. Flooding Does not display terrain features that
adequately identify potential flood
zones that would affect road closures.
67
CF
&
EE #’s
CRITICAL FEATURES AND
ESSENTIAL ELEMENTS COMMENTS
EE1012 d. Fires Easy to identify evacuation zone and
time to evacuate
EE1013 3. Catastrophic accidents
EE1014 a. Aircraft crashes Easy to identify evacuation zone and
time to evacuate
EE1015 b. Train wrecks/derailments Easy to identify evacuation zone and
time to evacuate
EE1016 4. Industrial accidents (Factory or
Vehicular)
EE1017 a. Chemical
EE1018 i. Dispersion of lethal clouds
due to wind speed and
direction
Weather factors need to be included
in order to model dispersion direction.
Specifically wind direction and speed.
EE1019 ii. Persistency of lethal clouds
based upon dispersion and
time
Weather factors need to be included
in order to model dispersion direction.
Specifically wind direction and speed.
The model shall represent, with acceptable accuracy, the effects of the following Critical Factor
and Essential Elements:
CF2000 II. Must accurately model Populations:
EE2001 1. Source of population data Population data has been obtained
through the 2010 census.
EE2002 2. Adjustable density levels Under the POPULATION BLOCKS
TAB, the following is viewable, and
is adjustable:
1. Census Block ID Group
2. Daytime population
3. Nighttime Population
4. Average household size
5. Number of Households
Under the CONFIGURATION TAB,
Population Change (%) is adjustable.
This tab is used to adjust the percent
that the population has increased or
decreased since the population data
was obtained. Can range from 0-
100% and defaults to 0%.
EE2008 3. Seasonal Scalability Under the SEASONAL TAB,
seasonal populations can be adjusted
and impact evacuation times will
68
CF
&
EE #’s
CRITICAL FEATURES AND
ESSENTIAL ELEMENTS COMMENTS
adjust. The seasonal populations are
illustrated on the map display by a
grid overlay that may be turned on or
off using the “Show Layer” select
box. Seasonal populations are defined
as surge type populations that fall
outside of the census data. Scenarios
may be run with seasonal populations
active or not active to aid planners in
understanding variations in
evacuation events. Clicking the
“Add” button defines seasonal
populations:
EE2009 4. Number of evacuees per vehicle The number of passengers (on
average) that will be in each
evacuating vehicle is adjustable under
the configuration TAB. Maximum
number of passengers per vehicle is
4. This number is adjustable.
EE2010 5. Shelters/Evacuation Centers Shelters may be added to the
evacuation area using the “Add”
icon. They will be displayed on the
map as a small green house icon. If
shelters exist in the evacuation zone
already they may be selected using
the Polygon or bounding box tools.
Shelters are also removable.
Selecting the shelter to be removed
by choosing the “Remove” tool on
the “Shelters” tab and selecting them
with the mouse on the map display
do this. The checkbox making a
shelter active may also be unchecked
allowing the configuration of the
shelter to remain, but not factored
into the current simulation.
Configurable shelter location, should
be automated.
EE2011 a. Capacity Shelter capacity is adjustable
69
CF
&
EE #’s
CRITICAL FEATURES AND
ESSENTIAL ELEMENTS COMMENTS
The model shall represent, with acceptable accuracy, the effects of the following Critical Factor
and Essential Elements:
CF3000 III. Must accurately model Roads and
Routes:
EE3001 1. Must have logical evacuee flow Under the Evacuation Zone sub tab
is End Point Assignments.
Evacuation End Points define where
evacuees are being sent. End Points
are not available until after Roads are
assigned. Endpoints are displayed as
yellow circles on the map. Each
yellow circle corresponds to one of
the end points named in the table.
Mousing over the end points in the
table will highlight their location on
the map.
EE3002 2. Number of lanes available There are also four filters that are
applicable to road networks. They
are “Highway,” “Major Arterial,”
“Minor Arterial,” and “Smaller.”
“Highway” defines the routes of
egress as Highways, and the others
follow suit. “Smaller” refers to non-
major roadways included in the
evacuation area. Checking or
unchecking the filter boxes will
include or preclude the roadway type
from the evacuation. Filtering of
specific roadways may also be done
using the button to select a specific
roadway for editing.
EE3003 3. Use of reversible lanes By clicking on the tab titled “Roads,”
then “Modified Roads” and then
click on the “Add A Modification
Section” button (green plus sign) in
the upper right hand corner. This will
create a section row.
Click the “Edit” button under the
column “Segments.”
On the map, starting at the end point,
70
CF
&
EE #’s
CRITICAL FEATURES AND
ESSENTIAL ELEMENTS COMMENTS
click on the segments of roadway
back to the point at which you want
to start the contraflow operation. As
you hoover over the roadway
segment it will turn green, it will turn
purple after you click on it.
In the tool, click on the “Editing”
button.
Check the box under the column
“Contraflow”
In the open box under “Contraflow”
indicate the numbers of lanes on this
side of the highway that should not
be contra flowed, for our example,
“1.”
Click on the tab “Additional Roads”
and then click on the “Add an
Additional Section” button (green
plus sign) in the upper right hand
corner. This will create an additional
road row.
Click the “Choose” button under the
column “Points”
Go back to the starting point of
contraflow on the map and link the
contraflow segment with the opposite
of the roadway. This will create a lane
to move cars directionally into the
lane(s) to be contra flowed.
You can choose the free flow speed
of this new segment as well as specify
how many of the total lanes in the
new segment will be used.”
Click on the “Choosing” button, it
will return to “Choose,” completing
the operation
The reversible lane function needs to
be modified for ease of operation.
Sometimes a road needs to be added,
and other times a road does not need
71
CF
&
EE #’s
CRITICAL FEATURES AND
ESSENTIAL ELEMENTS COMMENTS
to be added to complete the contra-
flow. It is not always clear if a
contra-flow has been added, and if
there is an adjustment to evacuation
times.
EE3004 4. Use of shoulder lane Modified in the same section as
reversible lanes
EE3005 5. Road closures Modified in the same section as
reversible lanes. Tunnels, roads and
bridges are easily opened and closed.
EE3006 a. Tunnels blocked Modified under the “ROADS” tab
EE3007 b. Roads blocked Modified under the “ROADS” tab
EE3008 c. Bridges blocked Modified under the “ROADS” tab
EE3009 6. Road speed limit Current speed limits are integrated,
and can be changed. Modified under
the “ROADS” tab
EE3010 7. Identify bottlenecks Bottlenecks are identified during
playback in the throughput mode.
Bottlenecks are not easily identified
during playback unless playback is
paused
EE3011 a. Bridges Choke points identified during
playback in the throughput mode
EE3012 b. Tunnels Choke points identified during
playback in the throughput mode
EE3013 c. Merges Choke points identified during
playback in the throughput mode
EE3014 8. Alternate routes Alternate routes can be manipulated
by changing location of end-points
and/or closing of roads
The model shall represent, with acceptable accuracy, the effects of the following Critical Factor
and Essential Elements:
CF4000 IV. Must model the effects of traffic
EE4001 1. Traffic Density
EE4002 a. Congestion Choke points are identified in
playback mode
EE 4003 i. Number of vehicles Controlled by population and
number of passengers per vehicle.
Other options available such as
number of vehicles being towed.
72
CF
&
EE #’s
CRITICAL FEATURES AND
ESSENTIAL ELEMENTS COMMENTS
EE4004 ii. Seasonal tourists Allows seasonal populations to be
considered during evacuations. The
seasonal populations are illustrated
on the map display by a grid overlay
that may be turned on or off using
the “Show Layer” select box.
EE4005 b. Accidents Accidents can be simulated in the
roads section by closing a road or
changing the number of lanes
available.
EE4006 c. Bridges blocked Blocked bridges can be simulated in
the roads section by closing a road or
changing the number of lanes
available.
EE4007 d. Tunnels blocked Blocked tunnels can be simulated in
the roads section by closing a road or
changing the number of lanes
available.
EE4008 e. Roads blocked Blocked roads can be simulated in
the roads section
EE409 f. Railways blocked RtePM does not account for mass
transit. In the case of a train
blocking a road, use roads section to
close a road.
EE4010 2. Time of Day Daytime/Nighttime census data is
considered
EE4011 3. Realistic effects of congestion on
travel routes, speeds and trip lengths
Evacuation times vary accordingly
with changes in traffic congestion.
EE4012 4. Fuel Availability Fuel availability functions are not
available
The model shall represent, with acceptable accuracy, the effects of the following Critical Factor
and Essential Elements:
CF5000 V. Must accurately determine Time to
Evacuate
EE5001 1. When evacuation starts Ability to set evacuation start time.
EE5002 2. Staggered evacuations RtePM gives you the ability to create
phased evacuations. The user must
first edit or create a new scenario as
detailed earlier in this users guide.
Then the user should select the
“Evacuation Area” tab.
73
CF
&
EE #’s
CRITICAL FEATURES AND
ESSENTIAL ELEMENTS COMMENTS
EE5003 3. Evacuation zones This allows the user to create
evacuation zones that can have
delayed start times and have different
response and behavioral parameters
EE5004 4. Advance notice Evacuations can be created using
RtePM as a planning tool with
advanced notice
The model shall represent, with acceptable accuracy, the effects of the following Critical Factor
and Essential Elements:
CF6000 VI. Must accurately model the effects of
Emergency Services
RtePM does not a requirement to
model the effects of Emergency
Services
EE6001 1. Traffic Police Does not include location or effects
of emergency services.
EE6007 2. Ambulance Does not include location or effects
of emergency services.
EE6012 3. Fire Trucks Does not include location or effects
of emergency services.
EE6017 4. Tow Trucks Does not include location or effects
of emergency services.
The model shall represent, with acceptable accuracy, the effects of the following Critical Factor
and Essential Elements:
CF7000 VII. Model/System capabilities shall include:
EE7001 1. Provide visual displays
EE7002 a. Locations of incidents in familiar
terms (i.e., street addresses vice
grid coordinates or lat/long)
Locations of incidents may be
identified with polygon or rectangle
feature
EE7003 b. Global maps capable of zooming
to a building in a city
Provides the ability to view a map in
terms of a street view, an aerial view,
or a topographic view
EE7004 c. Operator friendly graphical user
interface (GUI)
1. Contra flow difficult
2. Need hour glass when calculating
3. Evacuation planning wizard
interface would be useful down the
left hand side to show user progress
of planning while map is still visible
4. User guide is in development,
needs updating. Help window with
user guide embedded in tool may be
useful.
EE7005 d. Display terrain features Terrain features that would identify
potential flood zones needs to be
74
CF
&
EE #’s
CRITICAL FEATURES AND
ESSENTIAL ELEMENTS COMMENTS
more robust Flood zone overlay may
be useful in planning for evacuation.
Topo option is available on the map,
but needs to link to flood database
EE7006 2. Rapid Use Case generation capability
EE7007 a. Ability to easily export and
modify Use Case data from one
Use Case to another
Scenario can be saved to personal
computer. If someone deletes
scenario from RtePM, user can
import that scenario so it allows user
to secure scenarios in the current
version of RtePM. It also provides an
easy way to move scenarios from one
server of RtePM to another.
The model shall represent, with acceptable accuracy, the effects of the following Critical Factor
and Essential Elements:
CF8000 VIII Impact of weather on evacuation RtePM does not have a requirement
to include the effects of weather
EE8001 1. Models affected by Weather Workarounds such as speed limits,
road/bridge closures can simulate the
effects of inclement weather.
EE8002 a. Weather is modifiable. Road closure for high winds or heavy
seas on bridges, or flooding for
tunnels
EE8003 b. Day / Night. Model effects of
Day / Night
Day/Night taken into consideration
75
APPENDIX C
RtePM ENHANCEMENTS
VMASC is currently in the process of enhancing the existing RtePM tool by the addition of
evacuation time improvements and widget/overlay additions. By combining the listed features
below, a knowledgeable local emergency manager can fine tune their evacuation time estimates
considerably, making these new features valuable.
A. Evacuation Time Improvements include:
1. Probabilistic techniques that enhance the accuracy of RtePM. The prototype RtePM
is highly deterministic, meaning the tool’s output is directly dependent on the inputs
provided by the user (response time, participation rate, route availability, etc.). The
existing tool is thus very useful for providing a rough estimate of evacuation times
and for assessing the impact of discrete changes to entered characteristics, but limited
in its ability to provide the range of results for which planners should prepare. The
natural advancement of the tool would include addition of probabilistic measures.
Early indications of DDL Omni Engineering beta testing reveal that this feature is
functional, and will offer valuable data to the emergency manager, but further
validation is required.
2. Vehicular accidents and incidents interface that will significantly increase the
accuracy of evacuation times, especially when combined with the probabilistic
calculations. Early testing indicates that the feature is working and impacts the time
to evacuate in a densely populated area. It does not impact evacuation times in rural
areas since traffic density is not a major factor in calculating evacuation times.
3. Public transit interface allows the user to enter the percentage of the total population
that may be evacuating by public bus and rail. It does not model evacuation time for
public bus or rail, it simply removes that amount (%) of population from the number
of people evacuating by private vehicle. Testing proves that the higher percentage of
population using public transportation results in decrease of evacuation time due to
the decrease in vehicles on the roadway.
4. Pedestrian interface allows the user to enter the percentage of the total population that
may be evacuating by foot. It does not model evacuation time for pedestrians, it
simply removes that amount (%) of population from the number of people evacuating
by private vehicle. Testing proves that the higher percentage of population evacuating
by foot results in decrease of evacuation time due to the decrease in vehicles on the
roadway.
76
B. WIDGET/Overlay additions
The following widgets were added to the prototype RtePM. The intention was to add
capabilities that provide flexibility when modeling plume dispersal or displaying KML
files/shape file overlays. DDL Omni Engineering did not validate widget additions 1-4
listed below. But we did ensure that the additional features, when selected, displayed on the
RtePM map. These four additional widgets displayed properly and are an effective
enhancement to RtePM.
1. ALOHA Threat Zone - ALOHA (Areal Locations of Hazardous Atmospheres) is a
computer program developed by Office of Emergency Management, Environmental
Protection Agency and Emergency Response Division, National Oceanic and
Atmospheric Administration. It is designed to model chemical releases for emergency
responders and planners. It can estimate how a toxic cloud might disperse after a
chemical release - as well as several fires and explosions scenarios. It incorporates
source strength, as well as Gaussian and heavy gas dispersion models and an
extensive chemical property library. Model graphical output includes a "footprint"
plot of the area downwind of a release, where concentrations may exceed a user-set
threshold level. Within RtePM, ALOHA acts as a graphical interface between the
user's computer and the ALOHA program running on the RtePM server and provides
a subset of the functionality of the standalone program.
The following are actions available to the user:
• Select/Load a base script file.
• Change any configuration parameters via the Configure Script widget icon
• Select the script filename and output filename to store on the server.
• Generate the Aloha output file.
• Save a copy of the script and output files on the user's computer (optional).
• Display Aloha output files using the Display Plume Widget icon
• Remove loaded Aloha files.
• Override stylistic parameters of displayed Aloha files.
• Change visibility and opacity of displayed Aloha files.
• Threat Zone - allows user to draw threat zone and point of origin
2. HotSpot Plume - allows user to generate a plume. Plume-HotSpot was developed by
Lawrence Livermore National Laboratory's (LLNL) National. It provides a first-
order approximation of the radiation effects associated with the atmospheric release
of radioactive materials. The HotSpot program was created to equip emergency
response personnel and planners with a fast, field-portable set of software tools for
evaluating incidents involving radioactive material. The software is also used for
safety-analysis of facilities handling radioactive material. This program is designed
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for short-range (less than 10 km), and short-term (less than a few hours) predictions.
Within RtePM, PLUME acts as a graphical interface between the user's computer and
the PLUME program running on the RtePM server.
The following are actions available to the user:
• Select/Load the base configuration file
• Select a point of origin via the Select Point of Origin icon
• Change any configuration parameters via the Change Configuration Parameters
• Select the configuration filename and output filename to store on the server
• Generate the HotSpot plume
• Save a copy of the configuration and output files on the user's computer
(optional)
• Display the HotSpot output plume using the Display Plume Widget icon
• Remove loaded HotSpot files.
• Override stylistic parameters of displayed HotSpot files.
• Change visibility and opacity of displayed HotSpot files.
3. KML (Keyhole Markup Language) Layer allows for the loading and display of KML
Overlays on the RtePM map.
The following are actions available to the user:
• KML files can be loaded from the user's computer or the server, but not KMZ
files (compressed KML files with optional sub-directories and files).
• KML or KMZ files can be loaded from publically accessible sites.
• Consideration should be taken as to the order of loading overlays so that shapes
of interest are not obscured by other shapes.
• The following are actions available to the user:
• Select/Load KML files.
• Remove loaded KML files.
• Override stylistic parameters of displayed KML files.
• Change visibility and opacity of displayed KML files.
4. Load Shape Files - allows for the loading and display of shape file overlays on the
RtePM map.
The following are actions available to the user:
• Select/Load shape files.
• Remove loaded shape files.
• Set stylistic parameters for display of subsequent shape files loaded.
• Change visibility and opacity of displayed shape files.
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Testing has been performed on the enhanced features listed above, but at this time, many more
data sets will be required before a proper validation can be performed. DDL Omni Engineering
has every confidence that, in the future, the enhanced version of RtePM will provide evacuation
planners with an even more effective decision support system that allows multiple scenarios to
be evaluated.