We've seen and maybe witnessed first hand how more extreme weather conditions affect our daily lives and can make certain mundane things more difficult. Same goes for wood industry, other companies and people living in remote areas, only accessible via gravel roads: Climate change has already caused great financial problems: Yearly +100 mil.€ estimated costs for timber and timber logistics ind. only! This will only get worse in the future.
In the Arctic areas, road safety and its condition is a big concern. The concerns are various, but in this hackathon, we focus especially on "kelirikko"/ "rasputitsa", so-called "mud season", since it is the phenomena behind poor gravel roads. Society as an ecosystem would benefit it if we can limit the financial costs caused by this phenomenon, and we zoom especially to a company perspective. Good road condition also increases Arctic road safety in general, which our initial value as a team.
Roads, especially the gravel roads, start to suffer, when it's temperature zig zags above and below zero. In Finland, we have 25 000 km gravel roads under public maintenance, of which 8 000 km gets "kelirikko", of which heavy traffic gets limited in roughly half. Because of climate change, there will be more "kelirikko" in arctic areas aka. remote areas with grovel roads. Climate change will lengthen the midseasons, which in turn will result in a longer thaw period and zig zagging (see picture below).
We're improving the tools available for road maintenance and trip and schedule planning to take into account the road conditions and weather forecasts so that we can more accurately plan the upkeep and take the ever changing road conditions when we go out. The tool could also be useful for rescue operations that take place on the roads as it might be scalable. It is also suitable to different areas or market, from local to global. However, first our target market is Northern Finland.
By combining open source data about local weather (Digitraffic + Ilmatieteenlaitos) and satellite data (GALILEO GNSS + COPERNICUS weather forecasts & height map), we can come up with a data model with what the issue could enjoy a refreshing breeze machine learning with more data points from meteorological factors. With a neat and easy UI, it can make things more efficient.
If we can predict the need for road maintenance during winters more reliably, it will on a larger scale reap economical benefits as we have fewer hindrances on the roads. Fewer accidents overall mean fewer resources spent on accident related costs, machines or people. Commercial trip planning, wood industry, municipalities and individual people can also gain value as the overall on-road experience improves. Our target market is, however, the contractor/government client and the relevant timber industries.
Aki Leinonen, programming, designer, general management (THE REALIST)
Inga Metsola, designer, general management (THE DREAMER)
Markus Anetjärvi, programming and data (THE CRITIC)