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MDSS Challenges, Research & Managing Expectations - Road Conditions -

MDSS Challenges, Research & Managing Expectations - Road Conditions -. Prof. Leon Osborne Surface Transportation Weather Research Center University of North Dakota. Goal of MDSS.

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MDSS Challenges, Research & Managing Expectations - Road Conditions -

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  1. MDSS Challenges, Research & Managing Expectations- Road Conditions - Prof. Leon Osborne Surface Transportation Weather Research Center University of North Dakota

  2. Goal of MDSS . . . • To achieve desired levels of accuracy of road condition predictions at all locations of interest to the winter maintenance community • To convey the appropriate treatment recommendations to decision makers to permit a desired level of service

  3. Factors Determining Road Conditions . . . • ‘Weather’ • The most crucial factor forcing particular road conditions • ‘Land-Use, Soils & Road Structure’ • Sets the background characteristics that influence how pavements respond to weather elements • ‘Treatment Strategies’ • Materials and timing of treatments alter the weather-influenced road conditions • ‘Traffic’ • Can (often) alters ice/snow/water distribution on pavement

  4. Past Future GAINS Weather Weather Traffic Traffic Chemical Chemical Sub-sfc Sub-sfc contaminant layer Pavement Sub-sfc Sub-sfc Weather Runoff Traffic Weather Runoff Traffic LOSSES Plowing Plowing Energy Mass

  5. Road Condition Prediction Process • Predictions are made by achieving a balance between mass and energy forcing on the pavement. • Results in a forecast of: • Pavement temperature • Mass (ice, snow, water) on the pavement BALANCE Mass Mass Energy Energy Before After

  6. Achieving True Balance Is Difficult • Results from a lack of complete description of all physical processes • Gives errors in pavement temperatures • Greater the imbalance, the larger the errors IMBALANCE Mass Mass Energy Energy Before After

  7. Weather Example: Cloud Cover Impacts Pavement Temperature Impact Cloud Cover Cloud Cover SNTHERM_RT Simulation (Adam Mohr - UND)

  8. Treatment Example: The ‘Ice Cream Freezer’ Effect

  9. Balance . . . But At A Price!

  10. Road / Roadway Characterization • Crucial to the success of a road condition prediction model (Yet information is often difficult to obtain) Vegetation (including sky view) terrain snow cover pavement materials/layers sub-grade

  11. Issues / Limitations Requiring Further Research . . . • Weather forecast quality • Particularly precipitation and radiation (cloud cover) forecasts • Quality of observations from ESS sites • Accurately quantifying roadway and bridge properties • Understanding / modeling the effect of traffic • Understanding /modeling of chemical diffusion and loss properties

  12. Issues / Limitations Requiring Further Research . . . (continued) • Understanding the combined affects of multiple chemicals • Quantifying what is an acceptable road condition • Understanding / modeling the processes associated with falling / blowing snow relative to the road surface • Lack of accurate (real-time) information on maintenance actions performed

  13. Simulation of Mass Distribution Due to Traffic

  14. Management of Treatment Action Data Recommendation Action

  15. Summary • Significant progress has been made in understanding the scope of the issues outstanding in road condition monitoring and prediction • More research is needed to advance the methodologies and reduce the uncertainty of operational road condition predictions (although there will always be uncertainty) • The advancements will depend upon cooperation between the maintenance and weather communities • Expectations need to be sized to the capabilities

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