Modelling 1: PULP FRICTION
Leaders:
WP3, WP2: Marjo Hippi (FMI), Torbjörn Gustavsson (Klimator)
Description:
• Friction model: combined with RWIS and weather and maintenance activities;
testing testing
• EYEAR - Road Eye: friction data collection and transmission (acoustic, optical, invehicle etc.).
WP2 question:
To proceed with WP2 task 2.2 "New types of data needs" please determine the data requirements for your 'idea' untill August, 22!
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What's new:
• Mobile measurements
• Photos see link and see link. Photos can be freely used in the ROADIDEA project.
• Mobile measurements: data available (see
Mobile measurements)
• Mobile measurements: analyzed results (see
Mobile measurements)
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The description of the "Pulp Friction" pilot
Finnish road weather
observation network consists of about 475 stations (situation in November
2008). 90 of those stations are new types of optical sensors which give an
estimation of prevailing friction on the surface, too. The road weather station
network provides good monitoring system for road maintenance personnel as well
as meteorologists. There is several road weather products developed which help monitoring
the road weather.
In the road weather
warning service, which is operated by FMI and Finnish Road Administration
together, the road conditions are divided into three categories: normal, bad
and very bad road conditions. There is a link between friction and road
conditions: friction above 0.3 means normal road conditions, friction
0.15…0.3 bad road conditions and friction below 0.15 is linked to very bad road
conditions (by Yrjö Pilli-Sihvola, Finnish Road Administration, see table 1).
Estimated friction data from road weather stations is not included to the
weather warning service system so far, but in the Pulp Friction pilot the
usability will be studied.
The Pulp Friction pilot
is mainly considering of road weather and friction. FMI will do two separate
studies. On the first research the relationship between observed friction value
and the classified road condition will be studied. In this pilot FMI is
evaluating the road classification criteria against the "subjective"
value for road conditions given by the personnel in the Road Monitoring Center
of Finnish Road Administration (and also against other RWIS data). If the
results look good we could have a more automated analysis of road conditions
based on friction measurements.
Figure 1: Correlation between friction, description of the road
surface, slipperiness classification and road weather index.
The main idea of the
other research is to study the correlation between road condition and road
weather observation and create a friction model based on that information. The
friction model could do an estimation of prevailing friction based on
prevailing and past road weather observation and weather. Also, the friction
model can predict the coming friction based on the forecasted road weather
model data. The friction model will probably be part of FMI’s existing road
weather model.
The needed input data is
road weather observations (including friction observations) and road weather
models inputs and outputs. FMI’s road weather model is an operative product
already, so it won’t need any new inputs. Road weather classification indexes
for last winter are compared to the prevailing friction value and the
connection between those values will be studies. The road weather
classification index data is stored in FMI for previous winters.
The outcomes of these
two separates studies will be aimed for the road maintenance personnel and
meteorologist to help monitoring prevailing road weather and to estimate the
prevailing and forecasted road weather. Also, some products based on friction
or processed friction can be developed for driver.
See also Mobile measurements on winter 2009 (MOBILE MEASUREMENTS)
What have been done
Road weather observations from winter 2007-2008 have been collected and analyzed. Also, observations from previous winter 2008-2009 have been collected and they are waiting for analyzing.
Status of the pilot
Statistical analysis for the road weather observations have been done and correlations between observed friction and other road weather parameters have been found. FMI's road weather model has been developed so that friction is included to the model and predicted friction is now a new output of the model.
Next step: Parameters, including e.g. predicted friction, road surface temperature and state of the road, will be delivered to Destia's platform via ftp.
Later: Another friction parameter will be developed and tested. In this case the value of friction will be based on the deduction.
Links
• Road weather stations with optical sensors in Finland (link):
• Finnish Meteorological Institute: warnings
• Finnish Road Administration: RWS-observations
• Weather warnings in Europe: Meteoalarm