Road Weather Management Program
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Best Practices for Road Weather Management Version 2.0

Title:

Which Variables Should be Measured at a Road Weather Station: Artificial Intelligence Gives the Answer

Abstract:

The road weather stations (RWIS) are constructed to measure the conditions of the road. The sensor equipment normally consists of sensors for surface temperature, air temperature, relative humidity, wind speed, precipitation and type of precipitation. This study tries to answer which variables should be measured at an RWIS-station. This equipment has remained similar since 1979 when the RWIS-stations were first introduced. At a test site outside Göteborg some 100 climate variables, apart from the normal variables of an RWIS-station are measured. A neural network model is used to select the variables that give the best prediction of the surface temperature. Thereby recommendations of how to equip an RWIS-station can be made. Some climatic variables would be difficult to include in the RWIS-system because of high maintenance level, it may be practically impossible or simply too expensive. Results show that more temperature sensors in the ground help the neural network model predict the surface temperature. Ground heat flux and net radiation also improved the output of the model. The temperature predictions by the model were good when common variables were used as input and were improved when the additional variables were included. A forecast model from the Swedish meteorological office (SMHI) was also given as input for the neural network model. While the model from SMHI alone performed rather poorly, when combining it with the measured variables and the neural network model a very large improvement was achieved. The neural network had adapted and improved the output from the SMHI model to the site specific conditions. The analyzed time series was only two months long, so it was too short for the neural network model to learn how to predict occasions of special interest for road climate. A next step is to use a longer time series and more stations to improve the forecasts and so the model can learn to predict frost events. In the future the neural network model can be used as nowcasting system to improve the output from forecasting models, such as the one from SMHI.

Source(s):

13th Standing International Road WEather Conference (SIRWEC); Göteborg University Road Climate Center (Sweden) and Polytechnic of Turin (Italy)

https://www.researchgate.net/publication/237761511_Which_variables_should_be_measured_at_a_road_weather_station_-_Artificial_intelligence_gives_the_answer

Date: 2006

Author:

Almkvist, Walter

Keywords:


Road Weather Information System (RWIS)
Pavement temperature
Forecast/Prediction

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