Statistical modelling of wintertime road surface friction

Juga, Ilkka; Nurmi, Pertti; Hippi, Marjo

doi:10.1002/met.1285

Cited by 49 publications

(43 citation statements)

References 13 publications

(16 reference statements)

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“…An explicit method to estimate surface friction has been included in the model. The statistical friction model (Hippi et al , ; Juga et al , ) is based on observations from four Finnish RWSs during the winters of 2007/2008 and 2008/2009. The equations have been validated with independent data from the winter of 2009/2010.…”

Section: Model Descriptionmentioning

confidence: 99%

“…An explicit method to estimate surface friction has been included in the model. The statistical friction model (Hippi et al, 2010;Juga et al, 2012)…”

Section: Surface Frictionmentioning

confidence: 99%

“…As a novel feature, the model produces road surface condition classification as well as a traffic index describing the driving conditions in more general terms. A road surface friction forecast is also included (Juga et al , ).…”

Section: Introductionmentioning

confidence: 99%

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RoadSurf: a modelling system for predicting road weather and road surface conditions

2015

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Forecasting of road surface and traffic conditions is an important aspect of traffic safety and winter road maintenance, especially in the harsh northern climate. The weather conditions can change quickly, for example, with the onset of snowfall or during rapid temperature variations. A prior knowledge of road weather is important from a public road safety standpoint. Proper consideration of upcoming weather events also helps the road maintenance authorities to attend the roads in an effective and economical manner. In Finland, the Finnish Meteorological Institute (FMI) is duty bound to issue warnings of hazardous traffic conditions to the general public. To strengthen these services towards more efficient estimation of rapidly varying conditions of the road surface at a national scale, a simulation model, RoadSurf, has been developed. As input, the model employs numerical weather forecasts, either directly or after modifications made by meteorologists, as well as observations from synoptic or road weather stations and radar precipitation measurement network. As output, the model produces not only road surface temperature, but also road surface condition classification and a traffic index describing the driving conditions in more general terms, as well as road surface friction. The model has been in operational use since 2000. In addition to the original goal of providing road weather forecasts for the national road network, the model has been used in several other applications, for example, in predicting pedestrian sidewalk conditions and in numerous intelligent traffic applications. The present study describes the road weather model RoadSurf and its main applications.

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Section: Model Descriptionmentioning

confidence: 99%

“…An explicit method to estimate surface friction has been included in the model. The statistical friction model (Hippi et al, 2010;Juga et al, 2012)…”

Section: Surface Frictionmentioning

confidence: 99%

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RoadSurf: a modelling system for predicting road weather and road surface conditions

2015

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“…Pavement surface characteristics change over time and they influence many aspects of tyre-road interaction (Vaiana et al 2012;Praticò et al 2010;Juga et al 2013). It is well known that the vehicle's controllability decreases with speed (Rutka, Sapragonas 2011;Žuraulis et al 2014;Sokolovskij et al 2007;Pečeliūnas, Prentkovskis 2006).…”

Section: Introductionmentioning

confidence: 98%

Investigation of Asphalt Texture Roughness on Friction Evolution for Wheeled Vehicles

et al. 2016

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Novelty of the research lies in understanding the safety advantages of asphalt made using advanced pavement design process -compact asphalt pavement installation. Variation of tyre-road friction coefficient for this type of pavement design is new and little-studied phenomenon. The present study focuses on the research into tyre-pavement interaction on dry asphalt pavement surfaces to quantify the effect of microprofile on vehicle performance in establishing its braking conditions. The data on a number of different indications (vehicle speed, adhesion coefficients calculated on the basis of braking and friction forces, maximum adhesion coefficient m max and its 'minimum' value m 100% ) and measurement results generated using SRT-4 device for investigation of two road stretches at intervals of 100 m are presented in this paper. The interdependence analysis of statistical variables m max and m 100% has also been presented to exhibit the use of the method. Following statistical parameters are taken into account during random value analysis in order to assess the homogeneity of data: average value, dispersion, standard deviation, confidence interval around a sample mean, and correlation function. Finally, we provide a map between the adhesion coefficient and the longitudinal slip rate for stable and unstable braking conditions of a vehicle.

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“…The amount of snow, or snow depth, is important to many applications like road safety (Juga et al;, risk assessments (Bocchiola et al;Blanchet and Davison;2011), winter sport activities (Snow Forecast; n.d.; Fnugg; n.d.), hydrology (Jonas et al;2009) and climate change research (Brown et al;McCabe and Wolock;2010;Falarz;2002;Scherrer et al;Zhang et al;2004).…”

Section: Introductionmentioning

confidence: 99%

Statistical models for short- and long-term forecasts of snow depth

Hammer

2017

Journal of Applied Statistics

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Forecasting of future snow depths is useful for many applications like road safety, winter sport activities, avalanche risk assessment and hydrology. Motivated by the lack of statistical forecasts models for snow depth, in this paper we present a set of models to fill this gap. First, we present a model to do short term forecasts when we assume that reliable weather forecasts of air temperature and precipitation are available. The covariates are included nonlinearly into the model following basic physical principles of snowfall, snow aging and melting. Due to the large set of observations with snow depth equal to zero, we use a zero-inflated gamma regression model, which is commonly used to similar applications like precipitation. We also do long term forecasts of snow depth and much further than traditional weather forecasts for temperature and precipitation. The long-term forecasts are based on fitting models to historic time series of precipitation, temperature and snow depth. We fit the models to data from three locations in Norway with different climatic properties. Forecasting five days into the future, the results showed that, given reliable weather forecasts of temperature and precipitation, the forecast errors in absolute value was between 3 and 7 cm for different locations in Norway. Forecasting three weeks into the future, the forecast errors were between 7 and 16 cm.1 age and density of the snow pack. Compared to other factors, precipitation and temperature are the main drivers of changes in snow depth and most snow models are only based on these factors (Brown et al.;Kohler et al.;.Weather forecast services routinely forecast quantities like temperature, precipitation, wind and air pressure, but very rarely snow depth or other snow related quantities. An exception is snow-forecast.com (Snow Forecast; n.d.) which forecast snow depths for skiing resorts around the world, but the methods used are not publicly available.The small number of weather services forecasting snow depth or other snow related quantities are in a big contrast to the amount logged snow depth data available around the world. E.g. in the US and Canada daily snow depth are logged for at least 8000 locations (Brown et al.;. Motivated by the lack of models to forecast future snow depths and the large amount of historic snow depth data available, in this paper we present models to fill this gap. We build both short-term forecasts, where reliable forecasts of temperature and precipitation are available, and forecasts going further into the future. The work in Brown et al. (2003) is related to the work in this paper, except that the authors use a numerical model to relate snow depth to precipitation and temperature, while we present a statistical model such that probabilistic forecasts can be performed.The paper is organized as follows: In Sections 2 and 3 we describe the statistical models for short and long-term forecasts of snow depth. The models are analyzed on real data in Section 4 and the paper ends with some closing remarks in Sect...

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Statistical modelling of wintertime road surface friction

Cited by 49 publications

References 13 publications

RoadSurf: a modelling system for predicting road weather and road surface conditions

RoadSurf: a modelling system for predicting road weather and road surface conditions

Investigation of Asphalt Texture Roughness on Friction Evolution for Wheeled Vehicles

Statistical models for short- and long-term forecasts of snow depth

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