The Maintenance Decision Support System (MDSS) by variablepitch345

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									The Maintenance Decision Support System (MDSS)
Bill Mahoney
National Center For Atmospheric Research (NCAR)

CMOS Vancouver, BC 2 June 2005

FHWA – Road Weather Management Program

What is the Maintenance Decision Support System (MDSS)?

The prototype MDSS combines: • Advanced weather prediction • Advanced road condition prediction • Rules of practice for anti- and de-icing Generates treatment recommendations on a route by route basis

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Overview – MDSS Project Schedule

FY2000: FY2001: FY2002: FY2003: FY2004: FY 2005:

Requirements Analysis Conceptual Prototype Development MDSS Development, Technology Release-1 Field Demonstration 1, Technology Release-2 Field Demonstration 2, Technology Release-3 Technology Transfer, R&D Tracks, Colorado Testbed
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Major Project Goals
• Develop a winter road maintenance decision support system prototype and make them available to the road weather community • Raise overall awareness of the impact of weather on the roadway system by involving: -AMS, ITSA, TRB, AASHTO, State DOTs, Private Sector, Universities, National Labs, NAS etc. • Investigate new weather technologies and methods that may have applicability for road weather use
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2000-2004 State DOT * MDSS Stakeholders

35 State Participants
*Includes the District of Columbia
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2000-2004 State DOT * MDSS Stakeholders

35 State Participants
*Includes the District of Columbia
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MDSS Products
Plow route specific information
– Weather parameters
• • • • • • • • • Air temperature Relative humidity Wind speed and direction Precipitation type, rate, accumulation Road temperature Bridge temperature Bridge frost potential Blowing snow potential Road contamination & chemical conc,

– Road Parameters

– Treatment recommendations
• Treatment type (plow, chemical, pre-treat, etc.) • Treatment amount • Treatment location

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Winter Road Maintenance Decision Support System
Supplemental Weather Models Data Fusion - Road Weather Forecast System
Forecast Module A

Ensemble System
Model Initialization Forecast Models Initialization Data
MM5 WRF Rapid
MM5 RAMS Update Cycle WRF

Eta Eta

Sources AVN Eta RUC

Multiple Members

Forecast Integrator

Forecast Module B

(RUC)

Data Ingest

Forecast Module C

National Weather Service Data Eta Model AVN MOS AVN Model SYNOP METARS DOT Data RWIS DICastTM

Post Processor
Forecast Module D . . . Forecast Module N

Forecast Product

Road Condition & Treatment Module

- Road Temperature Prediction Model - Chemical Concentration Algorithms - Rules of Practice for Anti- and Deicing

Plow route specific treatment recommendations

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Road Condition and Treatment Module

Pavement Temperature Road Snow Depth Chemical Concentration

Net Road Mobility
Recommended

Weather Variables

Rules of Practice

Chemicals Plowing

Selected Plan Chemicals Plowing

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Rules of Practice- Storm Characterization
Post-storm In-storm Pre-storm

Protect against refreeze

Traffic

Chemicals Plowing & dilute Chemicals

Snow Depth & Compaction

Pre-treat logic: - Road T - Winds - Storm type Road temp trends: Warm, In-range, Cold Cold/BS threshold

Precip type: rain, snow, freezing rain, etc. Road temp trends: Warm, In-range, Cold Road temp trends: W, I, C, WI, IC, CI, etc. Total: Liquid & Frozen Precip & Duration

Chemical vs. Plow Only selection Number of treatments Pre-treat strategy
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MDSS Display Application

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MDSS Display Application

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MDSS Display Application

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MDSS Display Application

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MDSS Display Application

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Technology Transfer Process It is the intent of the FHWA that the prototype MDSS be used as a template by the private sector. It is anticipated that the MDSS will ultimately be deployed by road operating agencies and supplied by the private sector. Release-1: Fall 2002 Release-2: Fall 2003 Release-3: Fall 2004 Release-4: Fall 2005
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Current MDSS Shortcomings
• Does not provide explicit blowing snow treatment recommendations • 24-48 hour forecasts for precipitation start, end, type and rate are still inaccurate in many cases. 0-12 hr forecasts are better • Does not handle staff optimization (shifts) or material cost scenarios • Partly cloudy conditions very difficult to model – impacts road temperatures
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General Findings
A) Road operating agencies worldwide are very anxious to obtain better information and utilize weather information more effectively. B) DOTs are often working in a vacuum since there is little meteorological expertise in-house. C) The MDSS project and related activities have been successful at raising the awareness of the need for better road weather services.

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General Findings

D) The MDSS is a complex system and will require strong meteorological expertise and software and civil engineering skills to implement and maintain. E) DOTs indicate that widespread use an MDSS could result in an annual savings of 10-15% of their winter maintenance costs….several millions per state.

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MDSS Plans

• • • • •

MDSS Performance Assessment Sep ’05 MDSS Stakeholder Meeting Oct ’05 MDSS Release 4.0 Fall ’05 Support Technology Transfer FY ’05-06 Colorado Test Bed Winter Season 2005-2006

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MDSS Web Sites
http://www.rap.ucar.edu/projects/rdwx_mdss

http://www.ops.fhwa.dot.gov/weather
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