ROAD MONITORING TO PREVENT WEATHER PROBLEMS Miguel ngel
ROAD MONITORING TO PREVENT WEATHER PROBLEMS Miguel Ángel Rodríguez Jara Civil Engineer Head of Traffic Control Centre of Valladolid
SUMMARY • • Introduction System Monitoring • Sensors • Gaps in the data? • Correlation between Weather Variables • The monitor System
INTRODUCTION • • • The prediction of road weather conditions requires the production of accurate forecasts of temperature, humidity and precipitation at the road surface. It is a big challenge to obtain sufficient forecast accuracy of the road weather in order to produce correct warnings on slippery road conditions. The Valladolid TCC is located in way between Portugal, Basque Country and France. Owing to the location, orography and height have a special characteristics that makes the area prone to low temperatures. The last winter, the area of Valladolid suffered snow problems in a set of roads. It Covers four national corridors and two main international corridors: • Portuguese Corridor • Magrebian Corridor
INTRODUCTION Corridors International National
SYSTEM MONITORING • • The objectives of this system are to monitor the Roads covered by the TCC of Valladolid and create a software tool to evaluate the progress of the variables from the weather station sensors. The roads covered in this system contain sensors which measure temperature, precipitation, relative humidity, wind speed, pavement temperature, surface condition, and chemical concentration.
Sensors • All the weather stations send the values of each sensor to the TCC each 15 minutes.
Gaps in the data? • • In case that any of the SEVAC measure equipments gets broken or has a temporal communication problem, it is necessary to take into account that there exist some weather variables that allow substituting some fundamental variables, in such a way that the systems for generating alerts and notices and the aid decision system of the Traffic Management Centre are guaranteed. Such secondary weather variables are the following ones: Visibility and Air Temperature, which can substitute the fundamental variables: Rain Intensity (mm/h) and Surface Temperature, respectively.
Correlation between Weather Variables The weather variable that allows appreciating the “Snow Classification” is Rain Intensity. In case that there are no rain gauge data, the variable to be used for classifying is Visibility (m) • Rain Intensity (mm/h) Visibility (m) 0 1, 800 1 1, 600 2 1, 400 3 1, 200 4 1, 000 5 800 6 575 7 375 8 175 2000 1500 1000 500 Rain Intensity (mm/h) 0 Road Safety Risk 1 2 3 4 5 6 7 8
Correlation between Weather Variables In case that the road detector is broken down and, thus, it is not possible to record data about the fundamental variable ST (Surface Temperature), the following correlation with the secondary variable AT (Air Temperature) has been obtained in the following way: ST = AT + 1ºC Surf. Temp. (ºC) • 1 ºC Air Tem. (ºC)
Correlation between Weather Variables The weather variable that allows assessing the “Freezing Effect of the Sun” is the Global Radiation. In the graphic below, it is possible to observe how from a radiation superior to 50 W/m 2, the ST surpasses the 0 ºC, rising to 1 ºC with an increase of 33 W/m 2 of solar radiation. Average ≃ 33 w/m 2 / ºC Surf. Temp. (ºC) • Global Radiation (w/m 2)
The Monitor System • • The data can be accessed remotely using a Windowsbased software program. The data are also stored in a central database for future use. The accuracy of the system was evaluated through comparison of atmospheric data with site observations of surface condition, pavement temperature, and air temperature. The reliability was evaluated by reviewing the history log files to located gaps in the data and with the correlation between weather variables. Problems with individual sensors were also documented. The accuracy of the system was found to be good for the sensors that could be directly evaluated.
The Monitor System Each 10 minutes Road Operator PROCESS . . TCC Weather Variables Central Server All Weather stations
The Monitor System
Thanks for your attention
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