Intelligent route-based forecast (i-rbf)

The project "intelligent route-based forecast (i-rbf)" is about developing an innovative model and a web service that will enable high resolution forecasting of road surface temperature for sections of the road.

Accurate weather information or prediction is becoming more and more important. Extreme weather events especially impact road transport, also in urban areas. Climate change has altered weather patterns and led to more intense weather events. Weather information is an important input data for traffic management systems and traffic infrastructure management systems aiming to: - Provide safer roads - the influence of weather on road safety is not directly derivable from statistics, however there is no doubt that weather conditions determine road conditions and influence the driver's behaviour. The estimated economic cost of weather-related crashes alone amounts to nearly 30 mrd € annually. - Reduce winter road maintenance costs (i.e. salt consumption, work hours) - efficient ice control and snow removal is based on anti-icing strategy that includes the application of chemicals to the road before the forecasted event. Studies have shown that it should take place not more than 1–2 hours before snowfall. Smaller amounts of salt (5–10 g/m²) are usually applied compared to the de-icing technique. There are many reports or quotations of substantial savings on winter road maintenance costs. Winter maintenance engineers base their decision making by consulting Road Weather Information System (RWIS) that combines weather forecast data with road temperature and road surface condition data. The current generation of RWIS is based on forecasts generated by energy-balance physical model (one-dimensional diffusion equation). Physical model can predict the road surface temperature, which is the most important parameter for determining road surface conditions (i.e. dry, wet, ice, snow). The prediction is usually made for the location of road weather station. Currently, one of the biggest challenges in the road meteorology is how to enable road surface condition forecasting for the whole road network (route-based forecasts). There were some methods tested to generate route-based forecasts (e.g. thermal mapping, linear extrapolations, 2-D inverse distance weighting algorithm), but accuracy of such predictions is still in question. A model that would enable real-time road weather forecasting of road surface temperatures for each section of 30 metre of the road would therefore represent a dramatic technological shift for RWIS/MDSS systems. Due to the absence of this information, forecasts of road surface conditions often times turn out to be inaccurate, the maintenance services carry out applications of salt or other materials when there is no real need for it, or they apply higher concentration of salt than it would have been necessary. To this end, the project’s aim is to develop an innovative model and a web service that will enable real-time forecasting of road surface temperatures for sections of each 30 metres of the road. The following are the planned results of the project: 1. Intelligent route-based forecast model (i-RBF module) 2. Intelligent route-based forecast cloud service (i-RBF web service). The consortium consists of CGS Labs d.o.o. (Slovenia) as the Main Participant and research performing SME, and the Slovak Hydrometeorological Institute (SHMU) as a Participant and a research institute. CGS Labs is the leading company for RWIS/MDSS systems in Slovenia. The SHMU carries out objectives and duties of the national hydrometeorological service in Slovakia. CGS Labs will lead and coordinate activities of the project as well as provide all technical knowledge about road physical model and road weather modeling, software development and RWIS/MDSS needed for successful realization of the project. SHMU will contribute their knowledge about numerical weather prediction models and their operational exploitation in the field of high impact weather prediction. Added value through the proposed project cooperation will be synergy of knowledge and experiences of both partners, resulting in an innovative software module i-RBF. Results of the project will contribute to the increase in CGS Lab’s and SHMU’s competitiveness and research and development activities, while also contributing to closer working relationships between the partners as well as with the European research community. The project will help to improve everyday life of citizens world-wide by increasing the safety of drivers and passengers everywhere.
Project ID: 
13 028
Start date: 
Project Duration: 
Project costs: 
660 000.00€
Technological Area: 
Information Processing, Information System
Market Area: 
Other computer services

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