Mitigating the Impacts of Weather on Surface TransportationKevin R. Petty, Ph.D.Head of Technology Research
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Basic Element of Weather
The sun is the driving force behind the weather we receive Creates unequal heating of the
Earth’s surface Circulations are generated in an
attempt to distribute or equalize heating
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Air Masses Air Masses
Air masses are parcels of air that bring distinctive weather features to the country A large body of air with similar
TEMPERATURE and/or HUMIDITY properties throughout Air masses acquire the properties of the
terrain over which they move They are classified according to their
TEMPERATURE and MOISTURE content
Maritime Polar (cool, moist)Continental Polar (cold, dry)Maritime Tropical (warm, moist)Continental Tropical (hot, dry)
cPmP
mTcT
mP
mT
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Frontal Boundaries
LH
Occurrence of a weather front Boundary between air masses of
markedly different properties Marked change in weather with
the passage of a front Cold Fronts Warm Fronts Stationary Fronts Occluded Fronts
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Cold Fronts
Where a cold air mass is replacing a warmer air mass Cold air is undercutting the warm air causing it to rise Hazards: Thunder activity, strong winds, heavy rain and
sometimes snow
Rising Warm AirAdvancing Cold Air
Cold Front
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Warm Fronts
Warm air advancing and rising over a wedge of colder air When air rises it cools and the water vapor within it
condenses to produce clouds and precipitation Hazards: Snow and freezing rain
Cold AirRising Warm Air
Warm Front
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Precipitation Type
Vertical atmospheric temperature profiles and associated precipitation type
Surface
10,000 Feet
0oC/32oF
After Ahrens 1994 – Meteorology Today
0oC/32oF 0oC/32oF 0oC/32oFwarmercolder warmercolder warmercolder warmercolder
Snow Sleet Freezing Rain Rain
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Localized Influences
Proximity to bodies of water Lakes Rivers and Streams Oceans
Orographic Features HillsMountains
ElevationUrban Development
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Road Surface Heat Balance Factors
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Diagnostic Solutions
Assessing current conditions Atmospheric conditions
– Road Weather Information Systems (RWIS)
– Satellite data– Radar data– Cameras– Mobile observations
Pavement conditions– RWIS observations
– In-pavement sensors– Non-invasive sensors
– Thermal mapping– Cameras– Mobile observations
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Lost Trail Pass, Idaho : Guess the driving conditions on the following days? Images courtesy of Idaho Transportation Department
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How well did you do!
Air Temp: 25ºF
Road Temp: 30ºF
Road State: Slush
Grip: Good
Air Temp: 20ºF
Road Temp: 28ºF
Road State: Ice
Grip: Poor
Air Temp: 36ºF
Road Temp: 39ºF
Road State: Dry
Grip: Excellent
Air Temp: 25ºF
Road Temp: 50ºF
Road State: Dry
Grip: Excellent
Air Temp: 23ºF
Road Temp: 43ºF
Road State: wet
Grip: Good
Air Temp: 23ºF
Road Temp: 28ºF
Road State: Ice
Grip: Poor
Air Temp: 28ºF
Road Temp: 43ºF
Road State: Wet
Grip: Good
Air Temp: 24ºF
Road Temp: 28ºF
Road State: Ice
Grip: Very poor
Air Temp: 14ºF
Road Temp: 21ºF
Road State: Dry
Grip: Excellent
Salt Residue
Salt Residue
Images courtesy of Idaho Transportation Department
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Prognostic Solutions Predicting future conditions
Atmospheric– Persistence –
– simplistic method based on the assumption that conditions are not going to change.
– Not a good method to use in environments subject to rapid or dramatic changes.– Analog
– based on past, similar events– Difficult to use, as no two systems are exactly alike
– Statistical – Derive trends from medium to large datasets– In terms of road weather, can be very effective for short range forecasting, but
breaks down at longer lead times (i.e., >6 hours)– Numerical Weather Prediction (NWP)
– Computational simulations based on fluid dynamics and physics– Attempt to capture may of the topics previously mentioned – Although not perfect, generally regarded as the best solution for road weather
applications
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Energy and Mass Balance Pavement Model
Pavement prediction based on numerical modeling is currently the best solution for forecasting changes in pavement conditions (i.e., pavement model). Typically a one dimensional energy and mass balance model is used
A mathematical model capable of simulating the changes in pavement temperature and state using information about the forecast location and predicted atmospheric conditions
The capacity to provide tactical and strategic information (i.e. nowcasts and forecasts) about the ever changing road weather conditions supports the planning and execution of maintenance activities
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Energy and Mass Balance Pavement Modeling
A fundamental element of good weather and road condition forecasts is accurate assessment of the environment and pavement at the beginning of the forecast period
Good pavement models and RWIS serve as a foundation for accurate, timely road weather predictions Not all models are created equal Setup and execution techniques help ensure that forecast
accuracy is maximized Additional products (e.g., thermal mapping) can be used augment
and support pavement forecasting
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Diagnostic and Prognostic Display
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Supplemental Support (Consultation)
Provides an added level of support (human interface opportunity) Forecast clarification Ascertain forecast confidence System monitoring
Consultants should Be trained in weather and experienced Be familiar with the region (e.g.,
microclimates) Understand/speak winter maintenance
operations Understand specific end user
perspective Be available 24 hours/day, 7 days per
week
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Point of Contact Information
Kevin R. Petty, Ph.D.VaisalaHead of Technology ResearchOffice (303)262-4093Email: [email protected]
Mitigating the Impacts of Weather on Surface Transportation
Leon F. Osborne, Jr.
President
Meridian Environmental Technology, Inc.
TRB Webinar 21 September 2010
”“The combination of weather and
road conditions in a given area.
2
Road Weather
Mobility Safety
Productivity Environment
Types of Road Weather Impacts
3
7,130
167
573
47
41
18
170
117
62
44
74
00 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000
Annual Weather-related Fatalities(1995 - 2008)
Road
Speed reductions on signalized arterial routes
can range from:
10-25% on wet pavement and
30-40% on snowy or slushy pavement.
“Weather Impacts on Arterial Traffic Flow”, Mitretek Systems, Inc., December 2002
5
On freeways, 23% of non-recurrent
delays are due to snow, ice, and fog.
“Temporary Losses of Highway Capacity and Impacts on Performance”, Oak Ridge National Laboratory, May 2002.
6
Vehicle Performance
Travel Speed
Roadway Capacity
Travel Time
Road Closures
& Detours
Operating Costs
7
Each year weather-related highway crashes
account for more than 7,130 people are killed
and over 673,000 people are injured.
8
Reduced Visibility
Reduced Pavement Friction
Increased Accident Risk
Loss of Life and Injury
Property Damage
9
More than $2 billion is spent
each year by state and local agencies
on snow and ice control operations.
Highway Statistics 1999, FHWA Office of Highway Policy Information, October 2000
10
Approximately 20% of state
DOT maintenance budgets annually.
Shutting down a statewide highway network for
one day results in $15 to $76 millionin lost time, productivity, and wages.
Standards and Poor’s DRI Study, 199911
”“Chemical anti-icing and deicing account
for roughly 1/3 of expenditures for snow
and ice control.
~ Environment CanadaPSL2 Road Salts Draft Assessment ReportAugust, 2000
12
Strategies and tools designed to implement these strategies to mitigate the impact of weather on surface transportation activities.
Advisory:
Notification of weather impacts and mitigation measures
Control:
Implementation of measures to alter transportation system
use due to weather events
Treatment:
Action to reduce the impact of weather on roadways
13
Modern Tools to Mitigate the Impacts of Weather on Surface Transportation Operations
• Improved Road Weather Data Resources
• Tailored Surface Transportation Weather
Condition Forecasting
• Maintenance Decision Support Systems
Combined effort of U.S. DOT RITA &
FHWA Road Weather Management Program
To develop and demonstrate an integrated transportation
weather observations data management system
Promote effective quality checking of ESS data
15
http://www.clarus-system.com
16
Applications of CLARUS ESS Data
Benefits:
“Road view” of atmospheric conditions
Source of in situ pavement condition monitoring
Useful for monitoring current conditions and
initialization of road weather forecasts
17
Quality checking information for filtering “undesired” data
Cross jurisdictional availability of ESS data
Use of Road Weather Data Provides…
Road weather forecasts
supporting transportation
agency decision making
Critical current observations
for transportation operations
18
Road Weather Forecasts are Tailored Forecasts
• Contain forecasted weather and pavement conditions
• Highlight specific forecasted conditions that affect
user operations
• Updated each hour or less to support user-specific
decision processes
• Done for zones, road segments, or specific points
19
Pavement Condition Forecasts
• Tailored forecast of road surface conditions
(dynamic layer)
• Generated from weather forecasts inputted into a
pavement condition model
• Created using computer assisted decision logic
• Requires greater understanding of material
physics, radiation, and chemistry
20
Maintenance Decision Support Systems (MDSS)
Benefits of using MDSS:
Cost savings for use of labor,
materials, and equipment
Maintaining a consistent,
higher level of service on
roads
Providing training for new
DOT personnel
Reviewing maintenance actions
from past storms
21
Maintenance Decision Support System Process
Maintenance Actions
Modifications to Standard Practices
Results Expected from Proposed
Treatment
Road Condition Observations /
Analyses
Propose Alternative Treatment
‘Optimal’
Treatment & Expected Results
Maintenance Practices and
Activities Databases
MDSSProcessing& IntegrationSystem
RWIS
Yellow: Forecast / Theoretical RED: Real-Time / Actual BLACK: Applies to Both
SOLID: Automatic or Semi-Automatic Process DASHED: User-Driven Process
ResourcesUsed /
Available
Road ConditionAnalysis &
Forecast System(RCAF)
High ResolutionGridded Weather
Forecast Database
High ResolutionGridded Weather
Observations Database
Observed & Analyzed
Roadway State Databases
Available ResourcesDatabase
Roadway & Environment
Characterization Database
Maintenance Decision Support Methods
• Synthesis of observed road and weather conditions
• Geospatial awareness of:– Current & projected
weather conditions / alerts
– Projected roadway conditions
– Maintenance action recommendations
• Use of selectable display features
Maintenance Decision Support Methods
• Route-based display of past, present, and future road & weather conditions
• Treatment recommendations– Determined from
physics-based rules driven by changing road & weather conditions
– Alternative treatments defined by user defined requirements / scenarios („What If‟)
Deploying MDSS
Source: “Maintenance Decision Support System - Deployment Guide”
FHWA Road Weather Management ProgramFHWA-JPO-08-059 (EDL #14439) - July 2008http://ops.fhwa.dot.gov/weather/mitigating_impacts
For More Information:
Contact . . .
Leon OsborneMeridian Environmental Technology, Inc.
701-792-1800
http://mdss.meridian-enviro.com
MDSS – Mitigating Winter’s Effects on the Highway
Anthony K. McClellan P.E.
Senior Transportation Engineer
September 21, 2010
Did you know?
• In Indiana One load of salt is approximately $500 - $1000 in materials.
• How would saving one load per truck per season affect your budget?
• MDSS is Green - One trip saved could result in significant reductions in salt.
MDSS
Maintenance Decision Support System
•Dynamic•Situation
•Road•Condition
•Material•Applications
•Plowing•Operations
•Current Status
•Materials•Inventory
•Crew•Schedules
•Equipment•Availability
•Static Resources
•Facilities
•Policies
•Practices
•Maintenance Information• RWIS• Road Reporting• Maintenance Activity• Tracking
•MDSS Processing Model – Maintenance Input
MDSS Overview
• Provides Managers and Foremen one location for snow and ice information
• Provides recommendations to Managers and Foremen
Graphical User Interface (GUI)• Provides:
– MDSS Routes and condition
– Forecast with hourly update
– Radar/Satellite
– RWIS data
– Multiple views: Table, Graph, Map
– Alerts
GUI Map View
GUI Graph View
GUI Table View
Data Collection from the Road
• Data from operators can be radioed in to switchboard or foreman and then entered into the system.
• Data entered into the system includes; Temperature, Material Used, Application Rate and Weather Conditions
INDOT’s FY 09 MDSS Mitigating the Costs of Winter Weather
Observed Hours of Snow/Fz Rain
3 Year Ave. FY 08 FY 09 Variation from 3 yr ave to 09
Variation from 08 to 09
Crawfordsville 304 390 334 9.9% -14.4%
Fort Wayne 420 501 496 18.1% -1.0%
Greenfield 304 390 334 9.9% -14.4%
LaPorte 263 329 396 50.6% 20.4%
Seymour 119 162 128 7.6% -21.0%
Vincennes 78 121 69 -11.5% -43.0%
All Districts 1,488 1,893 1,757 18.1% -7.2%
Observed Hours of Snow/Fz Rain (Nov - Apr)
Crawfordsville
020406080
100120
FY 06 FY 07 FY 08 FY 09
(Lbs per Lane Mile) perSN/FZRN Hour
La Porte
0
50
100
150
200
FY 06 FY 07 FY 08 FY 09
(Lbs per Lane Mile) perSN/FZRN Hour
Seymour
0
100
200
300
400
FY 06 FY 07 FY 08 FY 09
(Lbs per Lane Mile) per SN/FZRNHour
Vincennes
0
50
100
150
200
250
FY 06 FY 07 FY 08 FY 09
(Lbs per Lane Mile) per SN/FZRNHour
Fort Wayne
0
20
40
60
80
100
FY 06 FY 07 FY 08 FY 09
(Lbs per Lane Mile) perSN/FZRN Hour
Greenfield
0
50
100
150
FY 06 FY 07 FY 08 FY 09
(Lbs per Lane Mile) perSN/FZRN Hour
•Lbs per lane mile /Observed Hours of Snow and Fz Rain
The Bottom Line
FY 08 Through February
FY 09 Through February
1,324 1,359
$27,100,384 $17,350,292
Observed SN/FZRN HoursTons of Salt Used
Dollars of Salt511,328 327,364
Salt Usage – All Districts
ALL DISTRICTS - STATEWIDE SALT USAGE
0
100,000
200,000
300,000
400,000
500,000
600,000
NOV DEC JAN FEB MAR APR
Months
Tons
5 Year Ave.FY 200870% of FY 2008FY 2009
Salt Usage
3 Year Ave. 5 Year Ave. 10 Year Ave. FY 08 FY 09 Variation 3 yr ave to 09
Variation 10 yr ave to 09
Variation from 08 to 09
Crawfordsville 58,313 58,324 51,484 95,318 41,402 -29.0% -19.6% -56.6%
Fort Wayne 70,389 71,946 66,993 100,762 71,674 1.8% 7.0% -28.9%
Greenfield 74,067 74,886 78,863 110,670 60,686 -18.1% -23.0% -45.2%
LaPorte 86,387 98,830 103,021 132,039 89,546 3.7% -13.1% -32.2%
Seymour 62,212 53,174 45,398 66,726 40,250 -35.3% -11.3% -39.7%
Vincennes 35,355 32,997 29,059 52,759 26,246 -25.8% -9.7% -50.3%
All Districts 386,723 390,157 374,818 558,274 329,804 -14.7% -12.0% -40.9%
Salt Usage (Nov - Apr)
Overtime Hours – Snow & Ice
FY 08 FY 09 Variation from 08 to 09
Crawfordsville 38,240 17,971 -53.0%
Fort Wayne 44,896 35,603 -20.7%
Greenfield 36,614 32,074 -12.4%
LaPorte 50,961 51,743 1.5%
Seymour 33,240 19,027 -42.8%
Vincennes 22,533 11,792 -47.7%
All Districts 226,484 168,210 -25.7%
OVT Hours - Snow and Ice (Nov - Apr)
Tons of Salt per Snow/Fz Rain Hour
Tons/Sn Hour 3 Year Ave
Tons/Sn Hour 08
Ton/SnHr 09
Diff from 3 Yr Ave Diff from 08
Crawfordsville 192 244 124 -35.4% -49.3%
Fort Wayne 168 201 145 -13.8% -28.2%
Greenfield 244 284 182 -25.4% -36.0%
LaPorte 328 401 226 -31.2% -43.7%
Seymour 523 412 314 -39.9% -23.7%
Vincennes 453 436 380 -16.1% -12.8%
All Districts 260 295 188 -27.8% -36.4%
The Bottom Line
$AVING$ - Overtime
FY 08 (Reduced by 7.2%) FY 09 Difference (Hours) Savings @
$23.33/hour
All Districts 210,177 168,210 41,967 $979,136
Overtime Hours
$AVING$ - Salt Usage
FY 08 (Reduced by 7.2%) FY 09 Difference (Tons) Savings @
$53/Ton
All Districts 518,078 329,804 188,274 $9,978,536
Salt Usage (Tons)
So, Why isn’t MDSS Used Everywhere?
• MDSS involves Organizational Change
• “We already know how to fight snow”
• “We can save the salt without MDSS”
• It’s too expensive
But we already know how to fight snow!
How many people know how to change a tire?
•What movie is this shot from?
• Where was the Movie Setting?
“There are two basic rules of life: Change is inevitable and everybody resists change”
– Author Roger Von Oech
“Attitude Reflects Leadership”
- Can You Name the Movie?
How is our work affected by change?
We’re Interested In MDSS – Where Do We Go form Here?
•The Pooled Fund Study for MDSS, TRB and FHWA and even the Internet are all good sources for information on how to get MDSS in your organization.
•Training Plan will be needed
•Account and plan for “Organizational Change”
•Create Experts by using past saved storms for training and support
•Account for the resistance to change and be prepared for it.
Maintenance Decision Support System (MDSS):
Indiana Department of Transportation (INDOT)
Statewide Implementation
Prepared by:
Tony McClellan, P.E., Project Manager
Paul Boone, P.E.
Melody A. Coleman
Link for Report: www.in.gov/indot/files/MDSSReportWinter08-09.pdf
Questions?Anthony K. McClellan, P.E.
Sr. Transportation Engineer
Meridian Environmental Technology