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PROCEEDINGS OF THE 17TH SIRWEC CONFERENCE, LA MASSANA, ANDORRA (30th JANUARY - 1st FEBRUARY 2014)
A Numerical Forecast Model for Road Meteorological Services in Beijing
A fine numerical forecast model (BJ-ROME) is improved and developed for road meteorological services. The RST observation data is assimilated in order to obtain a more accurate initial field. The RST prediction during raining days is also improved after the parameterization of road evaporation. The road water depth is added in the output parameters of BJ-ROME which is extremely important in a metropolitan such as Beijing during summer flood season. Snow ice is added in the output which is also important in winter. This model had already been coupled with 3-hr rapid update cycle system at the Beijing Meteorological Bureau (BJ-RUC) and placed in vocational operation. The forecast time span and the update interval of BJ-ROME in vocational operation are 24 hours and 3 hours, respectively. The prediction results could be used as a reference of maintenance decision support system (MDSS) to mitigate the traffic jam situation especially in large cities.
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New results for analysing and modelling of weather impact on local traffic flow under the
WOLKE project
The objective of the project WOLKE (Weather related microscopic traffic parameters) was to investigate weather impact on motorized individual traffic. The novelty lies in the integrated modelling- approach which looks at micro-and macroscopic modelling impact and is set to harmonize the effects while utilizing different key values on each level. The research project focusing the representative pilot areas of Bavarian highway A8 and the City roads of Vienna and was conducted in cooperation with the Bavarian Highway Authority, the University of Stuttgart (UniS), the Austrian Institute of Technology (AIT), and the companies PTV and Ubimet. The paper describes the results of the research work of micKS about the local road weather impact on traffic flow. Local road weather and detailed traffic flow data at about 30 cross sections with 1 minute samples over a period of 4 Years where analysed. We found, that 15 minutes aggregated data show significant correlations between traffic flow, especially the driving velocity, and certain weather parameters. The first approach was to describe the parameters of the well known VanAerde fundamental diagram model due to 10 weather classes. Also an alternative fundamental diagram introduced by Ning Wu was used. The parameters free speed and traffic density at maximum capacity of the speed/ density diagram where described by using multiple regression analysis. The second microscopic model approach was describing the driving behaviour by considering the driving dynamics in terms of tire friction, stopping visibility due to the weather situation. The approach results in a simplified microscopic based model, which describe the expected driving speed and capacity reduction due to the road weather parameters intensity and type of precipitation as well as road surface temperature. The parameter of the microscopic model approach where gathered from the acquired traffic and weather data. Both microscopic and macroscopic model show similar results, while the macroscopic approach works with weather classes and the microscopic based model delivers continuous results due to the weather measurement or forecast inputs. The models could be used for all kinds of highways with up to 4 lanes and provide weather calibrated parameters for the traffic flow models in order to significantly improve forecast of traffic congestions and critical traffic situations caused by weather conditions.
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First experience with the application of the model METRo in the Czech Republic
The forecast of road surface conditions in the cold part of the year is important for operational safety. In addition, early and reliable forecast of road conditions can optimize road maintenance and consequently save significant financial resources. Our contribution will summarize first experience with the application of the Model of the Environment and Temperature of Roads (METRo) in the Czech Republic. We have implemented and adopted METRo to the conditions of the Czech motorways and roads and available data.
In our version, the model uses online measurements from road weather stations (RWS) in the Czech Republic and weather forecasts from ALADIN, the operational numerical weather prediction model of the Czech Hydrometeorological Institute. The METRo model was quasi-operationally tested during the winter season (November 2012 – February 2013) using data from 25 RWS. We compared three model runs with an emphasis on road surface temperature. The first run used the original model and served as a reference. The second run applied model output statistics (MOS) to ALADIN forecasts of temperature and humidity at 2 m above the ground, which are required by METRo. The third run applied METRo in a nowcasting mode by starting the model every hour and using new measurements available from the RWS. The results indicated that MOS improved the accuracy of the temperature and humidity at 2 m but had little impact on the forecasted road surface temperature. However, the nowcasting scheme significantly improved the accuracy of the forecasts for the initial three to four hours. The evaluation of the METRo results suggests that the radiation flux forecast is crucial for an accurate road surface temperature forecast.
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Upgraded METRo model within the METRoSTAT project
This paper presents the upgrade of the METRo model for forecasting the road surface temperature and conditions with the energy-balance approach. METRo model was upgraded with the two new inputs: anthropogenic flux and depth of the subsurface temperature sensor, and with the new sun-shadow algorithm to determine if the selected location at the selected time is exposed to the sun (with assumption that there are no clouds on the sky).
Each upgrade is detailed described in the paper and interesting tests with results are presented. With the new user defined inputs it is possible to significantly influence the road surface temperature forecasts and achieve better results with more realistic anthropogenic flux and depth of the subsurface temperature sensor values. Sun-shadow algorithm was developed and verified with the sensor measurements at the selected locations. Inputs to this algorithm are: geographic location of the road weather station (latitude and longitude), date and time (UTC) and digital terrain/ elevation model (DEM) for the wide area of the road weather station location. Results shows great improvement of the METRo predictions when using the sun-shadow algorithm especially in the time period between noon and evening.
Described upgrades were done within the Eurostars project called METRoSTAT. The main goal of the METRoSTAT project is to develop a new high resolution web-based service for significantly improved road surface temperature and state forecasting.
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The Kalman-Filter preprocessing within METRoSTAT project and application of the new method
in roadcast system
In this paper we applied Homleid type of Kalman-Filter preprocessing of selected input parameters for the METRo road forecast model. The Kalman filter corrections to atmospheric and dew point temperatures improve surface temperature as it was shown in selected case studies. The improvement is significant mainly in cases when the turbulent heat exchange is not negligible. Here we presented the statistical analyses of the studied parameters also. This new method will be implemented as independent module within the statistical METRoSTAT package. The new approach in surface temperature predictions is presented also.
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HARMONIE: AEMET new operational weather prediction model. The roadmap to the mesoscale.
Numerical weather prediction (NWP) models are the main tool to support the operational weather forecast activities in all the meteorological institutes around the world. At the same time they are very powerful tools to investigate the behaviour of the Atmosphere in many different aspects. But these tools need, as a first condition, a large amount of computer power and this is the reason why traditionally meteorologist have set up different kind of models, devoted to different applications. But in the last years the computer power has increased dramatically and NWP operational models are going to increase their horizontal resolution and then surface processes (orography, soil temperature, soil moisture and so on) are much better represented in the models. In this presentation the new AEMET operational NWP model, HARMONIE, is presented and show how its resolution (2.5 Km) may improve the forecast of surface parameters and in this way improving the products we can supply for Road Authorities around Spain.
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A new system for operational winter road surface conditions forecasting in Météo-France
During winter, snow presence on the road might have serious consequences on road traffic and security. Moreover, impact of snow presence on road is increased in plain areas, where snow occurrence frequency is quite lower than in mountainous areas, and consequently road users and road management services are less prepared. For example, in big urban areas several centimetres of snow are sufficient to stop totally road traffic and limit consequently economical activity. So this is a major problem and consequently many efforts were done for several years to develop decision- making tools for road management in winter. For this aim, Météo-France use in an operational way since 2004 a specific model, ISBA-Route, a model which permit to simulate the behaviour (temperature, liquid and ice content) within the road and the natural soil under the influence of atmospheric conditions. To improve this model and in aim to serve as a forecast tool for snow presence on road, this model was coupled with a detailed snow cover model (CROCUS). The resulting ISBA-Route/CROCUS model permit to describe with accuracy the behaviour of the snow deposited on the road and its characteristics (height, density, liquid water content, grain type…). During last years, winter road surface conditions forecasting was done thanks to a coupling of the ISBA-Route model with the numerical weather prediction model AROME (AROME-ISBA-Route system called AIR). Since the 2012/2013 winter, a new approach for winter road conditions forecasting was implemented in Météo-France. The ISBA-Route model is forced by human expertised atmospheric forecast instead of a direct coupling with numerical weather prediction models. This system, called PEIR (Expertised Predictions for ISBA-Route), is operationally used since the 2012/2013 winter with the ISBA-Route model to predict road surface temperature and road surface water and ice content. The post-event analysis of the 2012/2013 winter showed a significant benefit concerning the accuracy of the predictions. Moreover, the similar system was tested in a pre-operational way with the ISBA-Route/CROCUS model to provide predictions of snow occurrence, height and type on road at the France scale. During the 2012/2013 winter, many significant snow events with high spatial extension and important snow amount occurred in France. It permitted to evaluate the snow on road predictions done with the ISBA-Route/CROCUS model. It was shown that a significant improvement concerning snow prediction on road might be expected thanks to the operational use of the ISBA-Route/CROCUS model. Consequently, this system will be operationally used for the 2013/2014 winter.
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Snowdrift forecast for roads based on NWP data
Snow drifting is a severe problem for road traffic and winter service in many regions. Wind speed, snow cover characteristics, landscape, vegetation, road location and geometry are the main affecting factors. This is not only due to slipperiness and low visibility, but also because snow can be packed along the road sides forming snow dunes. On exposed road sections, snow fences are sometimes effective in reducing the problems and sometimes special measures in the road design are successful.
Under the SNAPS project (Snow, Ice and Avalanche Applications) funded by EU Northern Periphery Programme, Vegsyn Consult in Iceland has developed a model to forecast snowdrift on the roads. The model takes into account previous snowfall, temperature evolution and wind speed. Snow cover erodibility (mobility index) is calculated using previous parameters. As an output parameter the model calculates a snowdrift index which is a four-valued number; no drifting, low drifting, moderate drifting and high drifting. The model uses hourly data from NWP models. Also, the developed snowdrift scheme can use real-time AWS observations as an input data. It is possible to run the model for either individual points or bigger grids.
The model scheme is a simplified physical interpretation of commonly known principles related to snowdrift. The model development was supported by empirical observations by traffic cameras. Drifting snow may happen several days after snowfall if wind and temperature conditions are suitable. Snow cover erodibility is at maximum during and immediately after snowfall. All drifting seizes if air temperature rises above 0 C. Wind speed has a major rule for the phenomena because weak wind doesn't get the snow moving. 6 m/s is set as a threshold for the snowdrift, during weaker wind snowdrift doesn't occur.
The Icelandic Meteorological Office currently delivers a snowdrift forecast to professional users for evaluation. The results are promising although refinement in the physical interpretation of e.g. the snow cover history is foreseen. Likewise, the Finnish Meteorological Institute has included the snowdrift scheme into their road weather model, enabling snowdrift forecasts for all over Finland as colour coded forecast maps.
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Interactive Webgis System for Weather Forecasting in Spanish Roads
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Road Surface temperature forecast. The statistical insight.
Thermal mapping has been implemented since the late eighties to determine road ice susceptibility. It consists in measuring road pavement temperature along with some other atmospheric parameters to build a winter risk. Measurements are done using a vehicle embedded into the traffic in given road weather conditions. If the dew point temperature is lower than road surface temperature there is a risk of ice occurrence, and therefore a loss of grip for circulating vehicles. Road surface temperature is obtained with an infrared radiometer on board of a dedicated vehicle. Thermal mapping is usually done before dawn during wintertime in specific weather conditions, when the energy accumulated by the road during daytime is mainly dissipated and before the road structure starts a new thermal cycle. Measurements are anyhow time-consuming. A whole road network can hardly be analysed at once.
So far, thermal fingerprints were either used directly to identify the susceptibility to ice occurrence of routes, or as an input to forecast road surface temperature with physical numerical models. These models are based on an energy balance description, and needs an accurate knowledge of both meteorological and road parameters.
The objective of this work is to present how an approach based on multivariate data analysis was implemented to thermal mapping. The objective was to gather a given number of thermal fingerprints, with road surface temperature and air temperature at least, and in weather conditions representative of winter. Then data was processed through partial least-squared regression to establish a statistical forecast model for road surface temperature. Results obtained on a whole route provided a forecast with an error similar to the one of physical numerical model. Its application to specific zones could ease the forecast, reduce computation time, and provide an increasing role to thermal mapping.
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Latest developments in road weather forecasting
An accurate weather forecast is crucial for efficient road winter maintenance. Reduction of the number of gritting kilometers is important for both financial and environmental reasons. For road weather sites it is possible to make a high quality road surface temperature and condition forecast, based on known local characteristics of the site. Road weather stations are mainly located on cold road sections. Large local road surface temperature differences can occur along a route due to environmental characteristics (up to several degrees). If a road authority just uses the forecast of the road weather station, it may happen that in cases with road surface temperatures around 0°C, parts of the road are treated while it was not necessary.
Therefore a route based forecast has been developed, based on information about sky view, solar view, road type and local air temperature. In this way a forecast for every road section can be created. This information can be transferred to the gritting machine and used for dynamic gritting or dynamic routing. Currently a dynamic gritting pilot project is going on in cooperation with a governmental road authority.
Skyview is measured by driving with a car, which is very time consuming and expensive. Recently we have explored the possibilities of retrieving skyview data by means of Google Streetview information. A comparison between traditional skyview measurements and the Google Streetview approach will be presented.
Forecasts can be made for different road types and bridges. As Holland is a bicycle country, and cycling becomes more and more common in other countries, another new development is the creation of special forecasts for cycle lanes. Surface temperature measurements in cycle lanes show that the thinner asphalt layer can cause cycle lanes to cool down more rapidly than normal roads. Currently cycle lanes are often treated in the same way as normal roads. However as cycle lanes are often more critical than normal roads, it is very important for the safety aspect to create special cycle way forecasts, especially in black ice cases.
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Influence of the accumulated screening effect on road surface temperature distribution
Winter road maintenance is of great importance to keep the road safety. Proper prognosis of road surface temperature and condition will lead to more effective winter road maintenance. Many geographical parameters influence the spatial distribution of road surface temperature. Among them, the screening effect is a very important one. During daytime, the screening effect often causes extensive road surface temperature (RST) difference. The temperature of the screened parts is often much lower than the sun-exposed parts, which favors slippery conditions in winter season. The screening effect at a certain moment is often found to be influential. In this study, a new view of the influence of the screening effect, the accumulated screening effect from sunrise to a certain moment, has been adopted to understand the accumulated influence of the screening effect. A segment-based technique has been utilized and a laser dataset with 2 m spatial resolution has been used to generate the screening effect. Outputs from model Strĺng have been used to calculate the incoming radiation to the road surface. In total 11 thermal-mapping measurements have been conducted to examine the hypothesis. It is shown that 84% of the observed screening induced maximum road surface temperature can be explained by the theoretical result. The average residual RST is usually linear correlated with the accumulated screening time and solar radiation amount. Two empirical formulae between the theoretical maximum RST difference, slope and constant of the linear regression have been developed separately to calculate the slope and constant of the linear regression. Results of this study can be applied in the prognosis of the screening effect induced RST difference for the entire road stretches.
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Real-time estimation of surface precipitation type merging weather radar and automated station
One of the major problems affecting road safety conditions during the cold season in mid-latitudes is the possible presence of snow or ice on the ground. This is particularly critical in regions with mild winters such as in the Mediterranean basin where snow, particularly at low altitudes, is a relatively rare phenomenon and vehicles are normally not equipped with winter tyres. Based on earlier developments in Finland and Norway during the last decade, a method to estimate surface precipitation type (snow, sleet, rainfall) that combines information from weather radar and automated surface observations is described.
The method can be regarded as a three-step process. Firstly quality controlled weather radar data provide an areal description of the precipitation field. This information, updated every six minutes, is used to distinguish precipitation areas. Secondly, a temperature and relative humidity analysis is derived from a network of standard surface automated stations. The analysis takes into account not only surface observations but also terrain height to estimate local vertical gradients in the interpolation process in complex orography. Thirdly, an estimation of surface precipitation type is obtained through an empirical formula that provides the conditional probability of snow as a function of temperature and humidity, given that precipitation is present. The resulting product is presented to end-users in a customized way (GIS, specific visualization tool, etc.).
The presentation will provide details of the evaluation of the empirical formula, originally developed in Finland, compared to a second one that takes into account temperature and surface pressure –which might be relevant in mountain regions– using a large database of observations recorded in the Mediterranean basin. Moreover, details on processing aspects (surface analysis types) and case studies will be presented as well as a discussion of advantages and limitations compared with other existing techniques such as those based on in-situ surface measurements or polarimetric radar observations.
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Winter Road Maintenance and the Internet of Things
The Internet of Things literally means 'things' (e.g. sensors and other smart devices) which are connected to the internet. This may seem insignificant, but 'things' represent a new, and increasingly, critical infrastructure requiring their own dedicated technological ecosystem. As an industry, Winter Road Maintenance has been adept with coping with technological change, with the various data streams historically managed by bureaus and, more recently, sophisticated decision support systems. However, how well equipped is the industry to cope with the imminent inundation of additional data and information from new devices? This is a now inevitable challenge as the costs of sensors, communications and power dramatically start to fall. Indeed, since 2008, the number of 'things' has outnumbered users online and this trend will continue as our cities and roads become smarter and increasingly automated – it is estimated that there will be 20 billion things online by 2020. This paper describes the establishment of a winter maintenance demonstration corridor and corresponding IoT hub within a wider meteorological testbed in Birmingham, UK.
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Comparative study of friction measurement devices
Northern Japan has cold winters with snowfall heavier than that typically seen at its latitudes. Since winter weather creates hazardous road conditions for road users, winter road maintenance is essential in these regions. To carry out winter road maintenance works appropriately, a correct understanding of the road surface conditions is required. One approach to determining the state of the road surface involves measuring the road surface friction, and the Civil Engineering Research Institute for Cold Regions (CERI) has been conducting such friction measurement. CERI has been mainly using a continuous friction tester (CFT) that is able to obtain friction values continuously on roadways. The CFT calculates friction by measuring the axial force created by a measuring wheel installed at a 1- to 2-degree skew from the direction of travel. Although the CFT enables continuous measurement, its measurements are affected by the measurement vehicle's steering angle and it cannot measure friction when the vehicle is stationary. Recently, an indirect method using an optical sensor has been developed. The sensor optically measures the thickness of water/snow/ice on the road surface and gives an estimation of grip level. Since this method does not require contact between tires and the road surface, measurement is possible even when the vehicle is stationary. This study aims to verify the feasibility of an optical sensor by conducting comparative testing between the CFT and an optical sensor at a test track. Wet, compacted snow and thin ice surfaces were artificially created in addition to a dry surface on a straight section of the test track. Then, a vehicle equipped with both devices ran on these surfaces at 20km/h, 40km/h, 60km/h and 80km/h, in order to compare the friction values obtained from the CFT with the grip levels determined by the optical sensor. The layer thickness values obtained by the optical sensor were compared with those measured with a ruler or with a NASA water-film depth meter. Furthermore, measurements were conducted on thin ice surfaces treated with salt or grit at a speed of 40km/h.
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A Robust, Economical, and Reliable technologies for Monitoring the Road Surface Conditions in
South Korea
Next ITS(Intelligent Transportation System) in South Korea is planned around the safety navigation service. In addition, together with the hosting of the 2018 Pyeong-Chang Winter Olympics, the road safety in winter has the most priority. But the road management capability of Korea for adverse weather is very insufficient. In this regard, this study directs its attention to road surface information as a technology to help road management capabilities. If the road surface condition is collected in real time, we are able to advance road management.
There have been many researches on the surface detection field. However, many technologies have not been applied for real roadways. In Japan, Serizawa had completed the relevant base technologies in road surface reflection in road lightening and others in 1967, and Imamura developed optical sensor for monitoring the road surface moisture using the reflection flux polarized in 1979. Keiji Fujimura developed the mobile-concept road surface sensor in 1988. In recent days, Isao Yamamoto developed the CCTV-based surface detection sensor. In Europe, there is an active researches on VTT's prototype stereo camera measuring polarization differences. In recent days, stereo vision and polarization imaging are used to apply in the Robot navigation.
But these technologies are currently introduced with high cost, difficulty of operating system in local area and limited reliability. In this regard, our study is aiming at devising a road surface condition detection system with low cost and robust accuracy. This is what we have firmly decided using the video image processing(VIP) technology because of low-cost and fast installation. VIP is expected to help implement the low-cost and high-efficiency detection system. VIP is based on the classification algorithm which discriminates the polarization images clusters by utilizing stereo cameras.
For this purpose, we have proposed the system integration of the Road Weather Information in combination with mobile-based surface detection data. In this paper, we introduce a mobile-based monitoring system installed on a series of service vehicles, capable of acquiring, elaborating and transmitting road surface condition data. And this concept will be upgraded in order to integrate different technologies for the analysis of textures, crack detection, surface details such as grooving, and others.
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RSI - Road Status Information: A new method for detection of road conditions
ITS – intelligent transport solutions are important in today's society with demand on safe and environmental friendly driving. With the new RSI technology (Road Status Information), it is possible to optimize road maintenance by offering accurate and comprehensive information to contractors and other user groups about road conditions along the entire road network. Using the information available within modern cars combined with models and data from RWIS – road weather information systems makes it possible to find solutions for detection of different kinds of road maintenance needs within areas such as:
• Winter Road Conditions
• Bearing Strength
• Pavement Quality
• Air Pollution
FCD (floating car data) from vehicles and road weather information from RWIS together with road weather prediction models makes it possible to determine the road conditions with regards to slipperiness and friction in between the field stations. Another use is to model and forecast the road status according to bearing strength during thaw freezing periods in a detailed and dynamic way. The availability of car data increases the spatial and temporal resolution of the road condition models and it also makes it possible to detect how the car behaves and react not only on weather related surface conditions. By having access to vehicle based data it is also possible to detect such features such as pavement quality along the roads as well as the ambient air quality.
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Developing a cost effective multi pixel NIR camera for road surface status classification in 2D
One of the major challenges in winter maintenance is automated detection of road surface conditions and especially black-ice. Previous research and solutions have shown that it is possible to detect different road conditions by using IR detectors and illumination. A major drawback with commercially available solutions is that road status classification is only performed on a single spot on the road surface. Consequently there is a big risk that the single spot is not representable for the rest of the road. A second drawback is the, arguably artificially, high price point of the current commercial systems in relation to the information they offer.
In this research, a multi pixel NIR camera for classification of road status over a 2-dimensional surface has been developed. The goal has been to be create a system capable of detecting and distinguishing different road conditions on several different parts of the road surface.
The resulting product is a NIR camera able to classify and distinguish between different road conditions in several thousands of pixels on the road surface. Images and data from the camera can for example be used to overlay on a visual image to visualize the current road status to an operator or used to calculate friction on different segments of the road. By using cost effective components it is also possible to offer the system to the market at a price similar to the existing single point detectors.
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Radar-based remote sensing monitoring of roads
The paper will describe a set of radar-based remote sensing techniques that can be used to monitor roads and other related infrastructures. Three main techniques will be considered:
• Satellite-based radar interferometry and, in particular, the Persistent Scatterer Interferometry technique. This technique, which is sensitive to millimetric displacements, can be used to monitor deformation of roads, bridges and their surroundings. Its wide-area coverage capability (1'000 km2 for high-resolution data and 10'000 km2 for C-band medium- resolution data) makes it suitable to monitor vast regions.
• Ground-based radar interferometry, also known as Ground-based Synthetic Aperture Radar. This is a terrestrial deformation monitoring technique which is very similar to the previous one (it uses the same principle). It can be used to monitor parts of roads that are particular critical, e.g. an active landslide that can potentially affect a major road infrastructure, etc. The main advantage of this technique is its capability to monitor deformation over time with a dense sampling: this make is suitable to support decision-making during emergencies.
• Real-aperture-radar. This terrestrial technique can be used to measure and monitor vibration of objects, structures and infrastructures. The most direct application to roads concerns the vibration monitoring of bridges over time.
The paper will provide a brief introduction to each of the three techniques, illustrating examples of applications made by the Institute of Geomatics.
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Road weather information for different customer groups - case Finland
Road network availability in Finland is at very high-grade in the winter also during bad road weather conditions. Situations, when one single road section is closed because of road weather conditions, are few and far between because of an effective utilisation of road weather information in winter road management.
Road weather data in Finland is based on road weather monitoring stations and road weather cameras. Road weather data collection is made by road weather information system. Data collected is combined in the system with data from weather radars and weather satellites. In addition to observed data and calculated parameters based on data there are shown road weather and rain forecasts in the system.
Customers of road weather information services can be split up road maintenance professionals, road users and service providers of value-added services. Typically road maintenance professionals in this area take care of winter maintenance activities on roads or traffic information services for road users in traffic management centres. Road weather information for road users provided by road authority is focused mostly on traffic safety, traffic fluency and incidents on roads. Increasingly the information for road users is also provided by service providers of value-added services.
Finnish Transport Agency with ELY Centre for Southeast Finland provides road weather information for these customers with various contents. In Finland road weather information user interface with detail information is provided for road maintenance professionals. Road weather and road condition information for road users is available on Finnish Transport Agency's web pages and also for mobile devices. Digitraffic service is used to offer real time and historical data for organisations developing value-added information services. With these different user interfaces and content of information we try to meet expectations of our customer's needs.
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Addressing the environmental impact of salt use on the roads: the CLEAN-ROADS project
The paper presents the RWIS experience carried out in the Province of Trento (an alpine region in the north of Italy) within the EU LIFE+ project "CLEAN-ROADS". The project aims to locally quantify the impact on aquatic systems and air quality which is caused by a wide use of de-/anti icing chemicals during the winter road maintenance operations, and to empirically assess the environmental gain which is produced by the introduction of an advanced demonstrative RWIS. More specifically, an experimental Maintenance Decision Support System (MDSS) will be developed with the purpose of improving the procedures of interventions of the regional road management service, i.e. with the target to at least guarantee the today's level of service but with a significant optimization in the efficiency of how available resources are currently used. The demonstrative system will be tested and validated on a test area located in a valley bottom, where the highest optimization margins are to be expected. The test route is a stretch of the SS12 road, which suffers of limited solar radiation due to surrounding reliefs and is influenced by the presence of the river Adige. The project does not only have a technological dimension, but also a social one: on one side, road operators are currently strongly involved in the requirements' definition process with the purpose to design a user-centric system which tries to optimally fit their empirical needs; on the other side dedicated awareness-raising actions are planned in order to increase the level of responsibility of local travelers when planning and/or carrying out a journey during the winter season. In order to foster this behavioral change process, local road users will have also the chance to take advantage of improved advanced traveler information, i.e. available channels (variable message signs, real-time traffic information web portal, etc.) will be enriched with the output information of the RWIS. During the winter season 2014/2015, an ex-ante impact evaluation campaign will be carried out: the data of a static RWIS station will be combined with measurements performed through a mobile probe, and will be used to test, calibrate and compare different forecast models (e.g. METRo, FASST, and others). The activities of road operators (chemicals used, vehicles kilometers traveled, maintenance operations duration, etc.) will be very specifically recorded through GPS-based recording system for future comparisons with the advanced RWIS. The ex-ante environmental impact will be assessed through air quality PM10 sensors and a special monitoring system which will be used to measure the salinity levels of run-off water streams.
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Advanced Snowplow Information System for the Catalonian DOT
Catalonian Government has about one hundred snowplows ready to help in the cleanliness and safety of the 12.000 kilometers of roads along the territory, with over 2.500 of them around the Pyrenees area; although snowfall can reach sea level some times every year.
Each snowplow has two sensors: a GPS showing location every 90 seconds, and a Status Signal showing the vehicle state when there is any change (moving, throwing salt, shovel down, system failure, …). This information is received in real time by a communications server and stored in a database.
The traditional fleet IS uses this information via a map application showing the last known gps position for each snowplow, a symbology for the last known state, and a dropdown with all tabular information (machine type, plate, driver, etc). Some 'advanced' systems adds spatial relational gis analysis to get the municipality or the nearest road code but… really we can't get more from the data?
The Catalonian DOT Roads Metagraph is designed at scale 1:5.000 for upscaling (planning level), roads catalogue, navigation, and dynamic segmentation; allowing these algorithms to be combined with other land variables to get a more complex scenario building. Thinking of our fleet, contains all the roads and paths where a snowplow can move, so… we can go a step further!
A new expert system for snowplow fleet management has been developed using the Metagraph. A real time database listener launchs a new process every time a snowplow changes the status and execute advanced gis procedures to determine implicit information: geocode the gps coordinates, calculates the real path on the graph, intersect other geographic information, reference to the graph milestones, and store the results in a new layer on the database.
Advanced analysis were performed to obtain the spatial positions where snowplow state changed. Being time-dependent, the only way to relate over the lineal reference system was changing the routes to the temporal dimension, relating the states via temporal dynamic segmentation, and changing back the result to the spatial dimension.
Thanks to the new system we are able to generate more complex statistics: worked kilometers, average speed, percentage of activity in each state, etc.
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Planning passive snowdrift reduction on high-altitude roads with lateral obstacles to wind flow
The prediction of snowdrift formation on high-altitude roads is of interest for road maintenance tasks and planning of ski resorts. In this study a three-dimensional time-dependent computer model of drift formation is presented, that takes into account the effect of natural orographic formations, natural obstacles such as trees, man-made obstacles, the form of the road bed and its adjacent embankments. An open-source CFD toolkit is used to implement a 3D air flow model, coupled to a snow transport model and parametrized with time-variable air speed, temperature and snowfall density. The snow transport model takes into account both primary and secondary transport. Domain morphology and surface friction alterations due to accumulated snow are tracked and used in successive calculation time steps.
Direct applications of the resulting tool are discussed: modeling and understanding existing road configurations under different wind and snowfall conditions, but also predicting the effects of proposed alterations to the road bed and the elements around it. With careful design of embankments both above and beneath the roadbed, it is foreseen that airflow can be canalized in such a way to induce passive snowdrift removal with no need for mechanical intervention under favorable conditions. Techniques include embankment profiling and the addition of fixed aerodynamic elements in order to increase secondary snow transport away from the roadbed and into the adjoining spaces. Their possible application to a real-world scenario in Andorra is presented.
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Speed and Safety Analyses on Rural Variable Speed Limit Corridors
The Wyoming Department of Transportation implemented its first Variable Speed Limit Corridor along a rural section of Interstate-80 in February 2009 to address safety and road closures due to frequent severe weather conditions. Since that time, four more VSL corridors have been added with three additional corridors along segments of Interstate 80 and a corridor along Wyoming highway 28 known as South Pass. To evaluate the effect of the VSL corridors on these rural roadways analyses of speed compliance, speed deviations, and safety along the corridor was performed. The speed compliance analysis evaluated the level of compliance of individual vehicles using the VSL corridors during different VSL deployment cases, which were based on the level of speed reduction the system was displaying. The speed deviation analysis also utilized the speed deployment categories to determine the effect of the VSL deployment in reducing speed deviations. Lastly the safety analysis was performed to compare the before and after crash rates and frequencies along each segment of the VSL corridor to determine the effects of the VSL corridor in crash occurrence and crash spatial distribution. To incorporate the effects winter severity on crash occurrence crash models including winter severity variable were also estimated.
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How local weather forecast can save money in snow removal
Andorra is a country with a lot of microclimates close to the Mediterranean Sea. The high altitude makes that the climate of the lower zones in Andorra riding between the rigor of high mountains and also the smoothness of the Mediterranean Sea. When there are heavy snowfalls in the lower areas of the valley, snow, ice and their effects on the roads are very different from how they behave on roads with cooler temperatures and uppers areas of the country. The fact that much of the population live in Andorra and drive by the valleys have more importance in case of heavy snow than in a higher and cold place. To be on the edge of the snow-rain level does that the performance of cleaning services is not always as effective as they want. Often snow melts on the road while we have heavy snowfall. To know the exact height of snow and the temperature at which precipitation will occur is the key to success of road maintenance services in this area of study. We present the case that relates to the Capital. Over time was observed that weather forecasts were not enough to determine if snow damage would be enough to cover the roads. Why? Due to multiple factors such as heat island, orientation and height of the roads. The problem was how to know when forecast prediction announces snow level at 1,000 meters for example and really we need to start effective cleaning. Many times snow falls but we don't need to clean it. The problem is to plan it and how. The first problem is that we can lose a lot of money every time that cleaning snow isn't well planned because we live on the level of the snow-rain. Economic losses are about thousand Euros in every winter season due to false alarms mobilizing snow. That is why we set up a different way of working. In the first stage, in collaboration with the Comú of Andorra la Vella, we study the problem of determining the snow level and which parts of the country could be more troublesome due to its orientation and height. Having studied this aspect, we monitor the temperatures on the road in conflictive spots. With early results are proceeded to draw a map where weather incidents could be worse for driving. The advice to the Comú was to determine for intensity of precipitation, ice, cold and if all of those elements could cause incidents on public roads. The final result was a savings of several thousand Euros thanks to our assistance in the snow.
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Road Weather Nowcast (RWN) data in winter maintenance and ITS systems
RWN project, which was started with cooperation with Institute of Atmospheric Physics (part of the Academy of Sciences of the Czech Republic) and Czech Hydrometeorological Institute, reaches the state of the operational outputs from numerical model and is usable in the end-user SW application.
Our presentation software is designed not only to display data output from the model, but to provide complete data platform for other systems that use these data. It means we are able to prepare other modules/software applications with current forecast data in any form (XML, JSON etc.) to the other users in very short time after the data are available.
Software is able to distribute visualized forecasts to end users in very short time in heterogeneous network environment, with instant impact on decision process in winter maintenance. The data are delivered in context with measured data from each road weather station and forecasts are further validated by currently measured values on hour basis.
This software with current, accurate nowcasts will deliver the basic platform for further more difficult decisions in winter maintenance processes and generate additional data for delivery to Intelligent Transport Systems or to other applications in traffic infrastructure, winter maintenance and public ITS portals to be able to react on changing weather conditions a bit earlier the change actually occurs.
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Use of high resolution ensemble forecasting for high impact road weather events
Due to the chaotic nature of the atmosphere it is impossible to know the future state with absolute certainty. To quantify the possible future states of the atmosphere a technique called "ensemble forecasting" is now commonly used with Numerical Weather Prediction (NWP). The technique uses the uncertainty in the initial state of the atmosphere to run several NWP members each with a different initial state. The forecast output from these members can then be used to quantify the probability of a certain weather event occurring. The majority of major NWP centres now run a global ensemble at horizontal resolutions of approximately 40km. However this is not sufficient to resolve high impact road weather events such as ice, snow, hail, freezing rain, fog and high cross winds. Since June 2012 (to coincide with London 2012 Olympic games) the United Kingdom Meteorological Office (UKMO) have been routinely running a high resolution (2.2km) NWP ensemble out to 36 hours over the U.K. called MOGREPS-UK. Verification will be presented showing how MOGREPS-UK is superior in forecast skill for high impact weather events over a deterministic NWP model of a similar resolution.
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The Pikalert Vehicle Data Translator: Overview and Applications
The United States Department of Transportation (USDOT) Connected Vehicle program has three main goals: to increase safety, mobility, and environmental efficiency. This program is demonstrating the value of using vehicle data (including weather data) from millions of vehicles. It is anticipated that these data will be available to the weather community to support the diagnosis and short-term prediction of weather and road conditions. With funding and support from the USDOT Research and Innovative Technology Administration (RITA) and direction from the Federal Highway Administration's (FHWA) Road Weather Management Program, the National Center for Atmospheric Research (NCAR) conducted research over the past few years to develop a prototype PikalertTM Vehicle Data Translator (VDT). The Pikalert VDT incorporates vehicle-based measurements of the road and surrounding atmosphere with other, more traditional weather data sources (e.g., radar, in situ data), and creates road and atmospheric hazard products for a variety of users. In the past year, NCAR developed two Pikalert applications: the Pikalert Enhanced Maintenance Decision Support System (EMDSS) and the Motorist Advisory and Warning (MAW) System. The Pikalert EMDSS represents the latest innovation in snow control. Building on years of MDSS development, the Pikalert EMDSS incorporates Connected Vehicle data into the forecast and decision process. As a result, end users can obtain information along the entire stretch of roadway, and not just at Road Weather Information System (RWIS) sites. This enables maintenance providers to better monitor prepare for, and proactively address changing road conditions. They are also better equipped to make spot treatments on the road, improving safety, mobility, and reducing the environmental impact of de-icing chemical. The Pikalert MAW is a revolutionary approach to providing hyper-local, near real-time road weather information for the travelling public. Incorporating Connected Vehicle data, the MAW provides current inferences for visibility, road condition, and road precipitation. It also blends the Connected Vehicle data with a forecast engine to provide 24-hour forecasts of road weather conditions. Using the MAW, drivers will be able to plan routes in advance of their travel, including knowing which way to go and whether to delay travel. While on the road, a phone application keeps drivers abreast of changing road weather conditions. This presentation will outline the core tenets of the Pikalert VDT, and discuss the development and testing of the two applications.
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Comparison of non-invasive sensors and road sensors for the usage in section control systems
on motorways
Within section control systems on motorways drivers get information about adequate speed limits during adverse weather situations. Therefore road weather data are necessary. To detect the danger of aqua-planning measurements of precipitation intensity and waterfilm thickness as well as the status of the road surface are needed. To detect these atmospheric data different weather sensors are located near the motorway. There are two different sensor systems available which detect these measurements at the moment. For the detection of the status of the road surface, like dry, moist or wet, the sensor is imbedded directly in the road surface in most cases. The other sensor system is non-invasive which can be installed next to or over the road. Nowadays more and more open-pored asphalt is used because of its advantage to be less noisy and its property to drain off water better than normal asphalt. But this kind of asphalt cannot be cut to install a road sensor therefore the usage of non-invasive sensors will be increase in the future.
Within the German Test Site for Road Weather Stations [1] various road sensors as well as various non-invasive sensors are installed. The data for both detection technologies has been collected for the last 2 years and allows a statistically valid comparison of them. In this paper the advantage and disadvantage of the two sensor technologies will be shown for the measurements waterfilm thickness, road surface and road temperature. In order to describe the behavior of the sensors, the available data will be analyzed concerning the availability of valid datasets and the accuracy. Additionally the performance of the sensors is examined during periods of different clusters of traffic volumes.
The measurements of waterfilm thickness, road surface and road temperature are very important for automatic traffic control algorithms [2] as a speed limit caused by rain is derived and shown on variable message signs. In the German Technical Bulletin [2] a matrix with the two measurements precipitation intensity and water film thickness define which wetness-level is detected. There are 5 wetness-levels which cause different speed limits. For the first supply thresholds are given based on the experiences with road sensors. For the newer non-invasive sensors other thresholds may be necessary. This paper will show some possible thresholds for the precipitation/waterfilm thickness matrix and their effects on the speed limits shown on the variable massage signs.
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Customer oriented operations of the Finnish Meteorological Institute for road traffic sector
Weather observations and forecasts set grounds or support many functions of traffic related weather services and research. Weather related information is necessary indicator in weather sensitive decision-making, particularly enhancing safety of traffic. Demanding seasonal conditions cause notable challenges for traffic system in Finland. Disruptions are often explained by weather events.
The paper focuses on the Finnish Meteorological Institute's (FMI) Customer Services' current topics relating to road traffic services, and outlining the central functions and services produced for traffic operations. Current activities involve open data and developments of the data-to-intelligence project with the emphasis in improving operational services.
EU INSPIRE directive has recently steered publicly funded data producers towards opening of various atmospheric and marine data sets. In Finland, these information sources also include national road weather observations in real time. Road weather observations can be retrieved in machine-readable form, from the same IT-interface and under the same license than other above mentioned and freely distributed data. Other service developments focus on traffic-related spatiotemporal data as defined by new service concepts. Interdisciplinary project aims to create service concepts, identify and tackle challenges for business, develop the data analysis methods, collect the required data, develop pre-processing methods for the data, and pilot the services. To achieve these, recent work focuses on ICT-architecture and cloud services capable of handling increasing enormous data volumes, also open data, and methods for a road traffic braking distance application.
As ongoing processes, FMI serves both public and contract clients. Services include among others weather for ground traffic and maintenance, marine and aviation weather services, pedestrian slipperiness advisories and various other safety related services for authorities.
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Edge Storms: A Case Study
When a blizzard drops 50 cm of snow over a twenty four hour period, combined with 50 kilometer per hour (kph) winds, it is apparent to most people that this is a severe winter storm. Likewise, if an ice storm results in 2 cm of ice on every surface, highway travel will be curtailed for a while. But there are a number of other storms which can pose unexpectedly difficult problems for winter service providers. Many such storms involve pavement temperatures at or around the freezing point, and the physical boundary between rain, freezing rain, and snow may move significantly during a storm. Part of the difficulty in such storms relates to the phase changes that occur at the freezing point. These phase changes are energy intensive and predicting those energy flows is difficult, making weather forecasting of, for example, the boundaries between snow, freezing rain, and rain in a given storm extremely difficult.
This paper explores the difficulties associated with such edge storms by way of a case study of a storm that occurred in Iowa in December 2012. The case study uses information taken from the City of West Des Moines and specifically from their Department of Public Works, which provides winter services for the city. In addition to details of the storm, the lessons learned from handling this particular storm will be detailed and expanded upon.
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Exporting FMI Road Weather Expertise: Applications for Sochi 2014 Winter Olympics and for Spanish Highways
Highly successful Forecast Demonstration Projects (FDP) and Research Demonstration Projects (RDP) have been carried out during the past several Olympics Games under the auspices of the WMO World Weather Research Program (WWRP): Vancouver (winter 2010), Beijing (summer 2008) and Sydney (summer 2000). This legacy will continue within the FROST-2014 (Forecast and Research in the Olympics Sochi Testbed) project during the Winter Olympics and Paralympics to be held in Sochi, Russia, in February-March 2014. The project involves many international partners and consortia from Austria, Canada, Finland, Italy, Norway, South Korea, USA and Russia, who all will implement and run their weather forecasting systems during the Olympics. Different forecast ranges from nowcasting to near-medium-range will be covered by state-of-the-art deterministic and probabilistic systems. Many specialized, end-user focused forecast products are needed to address the high impact weather phenomena and systems impending the sports activities taking place in the complex terrain of the Sochi sports sites. Such forecasts must also cover specific weather information delivered to the public who need to use different transport means to reach the sports sites and venues. In this context, Finnish Meteorological Institute (FMI) will test, adapt and fine tune its road weather forecasting system to produce detailed forecasts for the road stretches leading to the Mountain Cluster of the Sochi Games, the location of most of the outdoor sports activities (alpine skiing, ski jumping, cross-country skiing, biathlon etc).
The very same FMI road weather forecasting system is already operationally implemented and piloted on three highway segments in Spain as part of the EU FP7 ITS (Intelligent Transport System) project FOTsis (Field Operational Test on safe, intelligent and sustainable road operation). Model results are post-processed to estimate expected road weather conditions and to issue warnings when adverse weather is anticipated along the highways.
The presentation will provide a general overview of the applicability of FMI road weather forecasting system during the Sochi Winter Olympics and Paralympics, as well as under highly different climatological conditions as piloted under the FOTsis framework in Spain.
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Presentation and application of specialized road weather information in the Czech Republic
As a result of long term development starting in early nineties of the last century a continuously data flow from forecasting centres of CHMI to the dedicated end users from road maintenance was established. The increasing volume of road transportation in the Czech Republic, both on national and international level demands sophisticated road maintenance based on reliable weather support.
The first RWIS was based on specialized forecasts in plain text format distributed in various forms, using dialed link BBS, videotext etc. Since the demand for fresh information was intense, the final distribution schedule was based on 4 nowcasting form forecasts in 24 hours for regions, highway paths and also road networks in large cities (Prague in particular). Later the implementation of internet based information system allowed presentation of other products as rainfall radar, satellite images etc. Last but not least the GIS based RWIS systems were implemented.
The road weather stations in the Czech Republic are supplied by 5 major providers. However there was a strong need to nationwide interpretation of the road weather station data. This was possible after application of the unified data format for road weather data and storing and managing the data on dedicated data servers. This was a basic condition for further development of online GIS based RWIS.
Currently the text based information is converted into XML format enabling much more flexible interpretation of the information. A very popular product is the forecast of the road maintenance intensity for the next 3 days.
Another improvement of the RWIS in terms of accuracy was implementing of various forecasting models as Icebreak of the Forecaster Controller module. Recently the products of the METROCZ model optimized for the Czech Republic are being utilized. This project is a result of mutual co-operation between CHMI and UFA AV CR.
At present the forecasting centres are using data in BUFR and SH70 formats. The data are presented in sophisticated system Visual Weather (VW). This flexible system allows the forecaster to produce variable outputs from road weather stations. Moreover VW is perfect for implementing different data layers for instance outputs from the METROCZ model.
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Fine-scale numerical study of four northerly snow episodes over Andorra (Pyrenees).
Northerly episodes over Andorra (Pyrenees) represent a challenge in terms of forecasting associated effects, especially during the winter season. Usually these episodes generate heavy snowfalls and intense windstorms which can substantially increase hazards and accident occurrence in this small mountainous region (468 km2).
When northerly moist flows impinge on the Pyrenees, precipitating systems mainly affect the North Slope of the Pyrenees and nearby areas due to orographic blocking. However, under particular atmospheric conditions, the moist flow cross over the mountain range and heavy precipitation can affect an extended area. These events are also associated to lower temperatures than average which in the cold season favour heavy snowfalls in the populated valley of Andorra and highly affecting their road conditions. The challenge comes down to the difficulty that atmospheric models have in forecasting the regional effects of these events over a small mountainous area and how far over the southern side of the Pyrenees range will precipitation spread (extension and altitude).
Four different episodes have been selected to assess the performance of the WRF model (Weather Research and Forecast model) with particular emphasis on the precipitation field. The main goal is to analyse the interaction of the northerly flows with this mountain range, their associated effects and detect the features which could be crucial for operational forecasting.
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Experiences of rapid landslides monitoring and warning at catchment scale in the Pyrenees. The
example of the Rebaixader test site.
Monitoring of rapid landslides, as rockfalls and debris flows, improves the understandings of these processes and also provides fundamental information for an efficient early warning and alarm system (EWAS), which commonly focuses on ground vibration. The Rebaixader is a typical high mountain catchment, where sort of torrential phenomena occur with a sub-annual frequency. The monitoring started in July 2009. Iit consists of five stations intended to study the triggering conditions and the post-failure behaviour of the rapid landslides. One of the stations monitors landslide propagation characteristics; it consists of four geophones, an ultrasonic device and a video camera and three infrared spot lights.
Until March 2013, six debris flows, eleven debris floods and four rockfalls (two were large boulder falls, 55 and 18 m3) were recorded at the Rebaixader site. The analysis of ground vibration signal, recorded as impulses per second, allowed to define preliminary thresholds for distinguishing between debris flows, debris floods and rockfalls. For example, rockfalls typically are characterised by a short vibration record (< 10 sec) with a peak of vibration greater than 100 imp/sec. The identification of processes was checked by the analysis of the video images and the post-event field evidences. Field surveys, ground vibration acquired at geophones and the movies recorded at the video camera provided unique information on the rapid landslides occurrence and dynamics, and gave an excellent opportunity to show the importance of monitored data for calibrating of simulation models.
On the other hand, the monitoring results show that the data gathered by the geophones provides valuable information for the design of alarm systems for the event detection and distinction between processes.
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The use of advanced meteorological tools for monitoring the Weather hazards in roads
Catalonia is a country with a very complex terrain, which implies rapid changes in meteorological parameters in short distances. These rapid changes sometimes are dramatic, so road conditions can change suddenly, causing accidents. These accidents are mainly slides off due to the ice or the water accumulated in the road, the sudden changes in visibility due to the fog, the strong lateral winds or due to strong hailstorms. To take into account all the meteorological features for monitoring the Weather hazards in roads, a high resolution numerical modeling system has been used.
The high resolution variables provided by the numerical models are used to generate good initial conditions for Model of the Environment and Temperature of Roads (METRo) to forecast the local road temperature and condition. This road model was selected for its robustness and the possibility of adaptation, since it's open source code and allows to change the code to add new features and integrate all the information. Apart of the variables calculated by the METRo model, it's also useful for road user's and administrators to have information about visibility, hail and precipitation type. This will be achieved adding some additional information:
a) Fog forecasts. Calculated using the 1D model COBEL which is able to run well simulate the boundary layer, as it uses a high resolution vertical levels.
b) The hail diagnostic and forecast. Calculated using a combination of numerical models and radar reflectivity data.
c) Surface precipitation type. Calculated from radar, Automatic Weather Station and model outputs.
The system output, designed specifically for the SMC road forecast users, is a web page containing maps, reports and graphics with the most relevant information from each model. Some study cases will be presented choosing roads located in zones prone to have problems related to snow, ice or visibility to check the skills of the system.
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Ten years of avalanche forecasting risk on "Bonaigua" and "Beret" roads, Aran valley, Spain.
The Aran Valley, located on the northern side of the Central Pyrenees, is one of the areas with the highest rate of avalanche risk of the whole mountain range (52% of the Aranese territory is affected).
Not only are high mountain areas exposed, but also 6 population clusters and 3 main roads. On the roads with which this paper is concerned, a combination of temporary and permanent protective measures is applied. Over a total of 31,5 kilometers there is a total of 82 avalanche prone areas (affecting 63 % of the road), of which 32 present at least a yearly frequency. Most of the slopes affecting the road are south faces, with elevations between 1600 and 2300 meters.
Until the year 2003, the risk on the road was handled on the basis of the regional avalanche danger advisory. In the winter of 2003/2004, a local forecasting system was set up. In order to do so, a snow and weather observation network was implemented and a specific danger scale -adapted to the needs of the handling of the road- was developed. In the winter of 2008/2009, a helicopter based triggering system has been set up, using the Daisybell device.
The implementation of a local forecasting system has represented an improvement of the traffic flux and of the safety of the road. Three times a week or daily in high risk cases, a quick visual interpretation bulletin is made. A specific avalanche danger scale has been designed, including such factors as the probability of occurrence and the size of the avalanches. On the basis of these parameters, a 4-level danger scale has been created: Low, Medium, High or Very High. Only natural danger is taken into account, accidental danger is not considered.
The Daisybell triggering protocol has proved useful whenever: a) triggering can take place immediately after the snowfall; b) the wind during the snowfall has not been too strong. A rigorous shooting protocol has been established, since the avalanche areas are located at the area of influence of Baqueira-Beret ski resort and they constitute important off-piste areas. All the concerned entities are involved in this protocol: the Catalonian road service, the Aranese SAR service (Pompičrs d'Aran), Baqueira-Beret ski resort and the Catalonian Police Department, as well as the owners of private businesses located in the area.
Three times a week or daily in high risk cases, a quick visual interpretation bulletin is made.
Close collaboration between avalanche forecast center and the responsible managers of the road has made improvements on security management.
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The effect of local features to road surface temperature
Local features can have significant effects on road surface temperatures. These can cause great differences in road condition on different parts of the road network. There have been many previous studies about these local features and how they can be modeled. However, the road weather model of the Finnish Meteorological Institute does not take local features into account. This study focuses on examining the significance of local features in the model. Obtained information can be used in further development of the model to make more accurate forecasts. Local features studied here include road surface albedo, asphalt depth, road slope angle and screening. In the study the road weather model was run for 16 different road points using different values for variables which describe the local features. In addition, it was studied how the model could be modified to simulate bridge points.
According to the results the most important factor of the tested features was screening. The biggest obtained temperature difference between screened and not screened road surface was 13 şC, which can greatly affect to slipperiness. Especially if screening started on afternoon it caused rapid decrease in temperature. The second important factors were asphalt depth and sloping of the road surface. The significance of these features was much greater on sunny than cloudy days. According to the results, increasing the asphalt depth from two centimeters to six centimeters decreased the temperature variation during 24 hours 2 şC on a sunny day. On the south faced sloping surface the temperature increase was 0.40 şC when the slope angle was increased by 1ş and on the north faced surface it was -0.46 şC. Albedo had only a small effect on the road temperature. The biggest difference caused by changing the albedo was only one degree. The simulations of the surface temperatures of the bridge succeeded after small modifications to the model. The biggest temperature difference between bridge simulation and normal simulation was found to be only 1.5 şC.
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ITS-capable road weather station
FMI (Finnish Meteorological Institute) has employed the combined Road Weather Station (RWS) and Road Side Unit (RSU) for supporting variety of research projects and initiatives related to vehicular networking and road weather service. The general idea is to develop and deploy "Road Weather Testbeds" with advanced communication applications in the interesting environments to test wireless networks and communications in public. The FMI RWS station in Sodankylä is equipped with up-to-date road weather measurement instrumentation, compatible (but not limited to) with the equipment expected to be available also in the demonstration sites own, permanent and locally owned RWSs. The procedure is to design, develop and test both the local road weather service generation and the service data delivery between RWS and vehicles.
In the communication perspective the focus is on the focus is on V2I (Vehicle-to-Infrastructure) communication. The vehicle bypassing the combined RWS/RSU is supplemented wirelessly and automatically with up-to-date road weather related data and services, and at the same time possible vehicle-oriented measurement data is delivered upwards. IEEE 802.11p is the primary communication protocol, but also the traditional Wi-Fi communication is supported. The RWS/RSU is linked with IEEE 802.11p for communication attempting, but it has also internal Wi-Fi modem, and both of these communication channels are actively seeking the bypassing vehicle communication systems. The local server is also gathering measurement data from local measurement entities in the RWS. The data from these sources, together with possible vehicle-oriented data is sorted and further delivered to FMI local facilities through 3G. The advanced services are developed in FMI facilities and delivered back to the RWS/RSU and ultimately to the vehicles.
The services provided to vehicles are two kinds, based on purely measurement data or data analysis. The measurement data provided to vehicle consists of friction, temperature, wind and visibility. The data analysis services are not completed yet, but the expected services are snowdrift forecast and a special type of route weather service tailored for dedicated road stretch.
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Braking distance application developed on Finnish D2I project
Sometimes drivers are driving too close to another car driving ahead. Many drivers have a false belief that in case of the car in front starts to break they can react, brake and come to a stop, still leaving enough distance between the two vehicles.
The distance between cars, called as a safe following distance, should be as long as the vehicle is possible to stop without collision in case of accident or other sudden braking. Suitable safe following distance improves the fluency and safety on the roads but the local weather and road condition should be taken into account, too. Car manufacturers have developed several systems to inform drivers if the distance between cars is too short. Also, there are systems that are automatically keeping the distance between cars long enough or braking also if needed. Those systems are called as autonomous or adaptive cruise control. However, most probably none of those systems take into account the prevailing road condition and weather.
Road surface slipperiness has a strong influence for the length of the braking distances. The braking distance can be several times longer if the road surface is icy compared to if the road surface is dry. Also, speed has a major role for the braking distances; when the speed is doubled the braking distance becomes four times longer.
On Data to Intelligence project (D2I) different traffic related data was combined to produce new innovative products and applications for traffic sector. One of the new applications is intelligent breaking distance application. The application informs driver if the safe following distance is too short and the prevailing friction is taken into account as well. The friction can be calculated by FMI's road weather model or it can be a measurement. This application has been created in a co-operation with different D2I partners. Noptel is the instrument company, Centria takes care of the implementation and Finnish Meteorological Institute has been creating the service and is delivering data for the application.
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Weather factors triggering the massive car crashes on 3 February 2012 in the Helsinki metropolitan
The variable winter weather conditions put challenge on road maintenance contractors. In spite of efficient maintenance operations, rapidly worsening driving conditions due to low temperature and dense snowfall with very poor visibility may result in severe pile-ups on the highways. Then, it is not even possible to keep the road conditions passable before moving the dozens or even hundreds of crashed cars away from the roadways. In this case we investigate the pile-ups that occurred on 3 February 2012 in the Helsinki metropolitan area in southern Finland. On that occasion, very cold air was advected from southeast in over Finland, the temperature being between -15 and -20 degC even in the southernmost part of the country. Sea-induced convective snowfall formed over the still partly ice-free Gulf of Finland. The most intensive snow band hit the Helsinki metropolitan area, causing a rapid simultaneous decrease of visibility and road surface friction just before noon. Car pile-ups occurred in many locations and several hundreds of cars were crashed and 43 injured persons were taken to hospital. Some of the main roads were closed for most of the day. Regarding the predictions made in the Finnish Meteorological Institute (FMI), the location and intensity of the precipitation band was well predicted with HARMONIE mesoscale model. The FMI road weather model predicted the layer thickness of snow on the road quite well, but the layer thickness of ice was too large in the forecast. Anyway, the derived novelty forecast parameter, coefficient of friction, corresponded well to the friction observed with the optical DSC111 device, especially around the time of the crashes. FMI had issued a warning for poor driving conditions already on the previous day. However, driving speeds were not substantially lower than on a "normal" day, probably due to the local feature of the weather hazard. A similar costly pile-up event occurred in the Helsinki region in March 2005. Preventing such accidents from occurring calls for efficient co-operation between weather service providers, road authorities and maintenance contractors. Real-time road weather information and recommendations need to be delivered to maintenance contractors. The increased usage of variable speed limits and warning messages along the roads as well as wireless data transfer between cars and infrastructure may also increase safety and reduce even costs in the long run.
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The effect of measured vs. estimated -40 cm subsurface temperatures on METRo forecasts
Finland has an extensive road weather station network. The stations are equipped with a large array of road surface and atmospheric sensors, but until recently there have been no subsurface sensors. During autumn 2012, the Finnish Centre for Economic Development, Transport and the Environment installed ten subsurface temperature (SST) sensors in selected locations. The motivation was the desire to see if the additional information from below the road surface would improve the road forecasts. Foreca has made road weather forecasts for the Finnish road weather stations for over a decade and in 2010 Foreca integrated the METRo road weather model into production. METRo was developed at Environment Canada. Since Canadian road weather stations had SST sensors, it is natural that METRo initialization relies on measured SSTs. To use METRo in production in Finland, it has been necessary to develop a separate module for estimating the SST based on the surface temperature history. In practice, this has been done by using METRo one-hour SST predictions as the measured value for the next hourly run, thus iteratively initializing the SST. To study the effect of measured vs. estimated SST on the forecasts, we ran one complete year of METRo forecasts from November 2012 to October 2013 first by using the SST estimation method currently in production and then by re-running the exact same routines, except substituting the real measured SST values in METRo input XML. We then compared the measured vs. estimated SST time series, and the road surface temperature forecast errors from these two runs. As expected, the estimated and measured SST correlate well, generally staying within a degree of each other. In some rare situations, the estimated SST diverges by a couple of degrees. The effect on road surface temperatures is more surprising, since for most months of the winter, the METRo runs based on estimated SST produce better results. On reflection, this is understandable: many METRo configuration parameters have been optimized using several years of data from the Foreca production system. These parameter optimizations affect the SST estimation and likely compensate some biases in METRo forecasts, which now manifest themselves when uncompensated by the SST. To gain a better understanding of the additional value ultimately achievable by installing SST sensors, the production system would need to be recalibrated for runs that use the measured SST. For an immediate benefit from the ten test sensors, however, the time series can be analyzed for situations where the measured SST runs produce better results. These situations provide hints for improving the SST estimation, which may immediately benefit all the forecast locations.
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Intelligent Road - Arctic RWIS demonstration platform
Intelligent Road (IR) is a project aiming at increasing safety of the arctic roads in Northern Scandinavia by creating a demo system providing road-users with short-term road weather information. This article presents the IR-system which demonstrates different ways for road users to benefit from the Road Weather Information Systems (RWIS) and Intelligent Transportation Systems (ITS).
The IR-system acquires data from multiple sources and visualizes it for the road users. The data sources are vehicles equipped with on-board sensors and CANbus diagnostics, road weather stations (RWS), open-data services like Digitraffic, weather forecasts and local weather stations, respectively. The data is stored to the system server to be processed and analysed. The demonstration platform uses the data to create a visualization of the current road conditions on the map which is displayed in a web browser interface.
The visualization has to be simple and fast enough for the road users to gain understandable information. As in many other RWIS implementations, colours are used to represent the varying road conditions. In addition road weather forecasts as well as RWS data are visualized. Several algorithms have been developed to optimize the amount of the visualized data, still maintaining good resolution.
A programmable data acquisition (DAQ) system for vehicles, equipped with GPS, GPRS and multiple communication interfaces for practically any sensor, has been developed. The DAQ acquires data from road condition sensors and wirelessly transfers the GPS-stamped data to the IR-server.
A road condition classification algorithm, developed by the Luleĺ University of Technology (LTU), has been implemented in the DAQ software.
A study of C2x-communication between Finnish Meteorological Institute's (FMI) intelligent RWS and by-passing vehicles, both equipped with 802.11p devices, is carried out. The objective of the study is to test the exchange of weather and road condition information. The exchanged data from the by-passing vehicle is used to enhance the accuracy of local weather forecasts. In larger scale, the aim is to deliver real-time data from the IR-system for the weather model development of FMI.
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The use of video surveillance for research of snow accumulation on the highways with crash
The improving of operational management of road winter maintenance is moving towards the development of intelligent transportation systems (ITS). Subsystems of weather monitoring and video surveillance are part of ITS. The practical experience in managing of road winter maintenance based on the use of information from road weather information systems (RWIS) is available in Russia today. In addition to the solution of practical problems the RWIS information can be used for scientific research.
The observations were made on the experimental sections of roads during two winter periods on the site of the federal highway M-4 "Don" Moscow – Novorossiysk.
Experimental works include: the surveillance by video cameras placed on the highway: the collection of videos for the entire winter; the special snow surveys, measurement of the snow height after the blizzards and snowfalls; analysis of data from automatic road weather stations located on the highway. The special attention during observations was paid to condition of road surface during the passage of blizzards and heavy snowfalls.
Experimental road sites have crash barriers with different scheme placements.
Video surveillance during the winter allowed to reveal that the crash barriers have an effect on the snow distribution on the road surface. A large amount of snow is accumulated during blizzards on the road with crash barriers.
Two possible variants of crash barrier works during blizzards were identified. The variants depend on the scheme of snow removal next to the barrier.
At the first variant, a condition of complete removing of crash barrier from snow must be implemented.
In this case crash barrier will work as blow-snow fence. At the second variant, snow is not removed from the crash barrier. So, crash barrier will work as entire, snow accumulation fence.
The process of snow accumulation on the highway was studied on the analysis of the collected video and snow survey results on experimental sites. The schemes of snow deposits for four-lane roads and four rows of crash barriers were updated.
The research has shown that the availability and placement scheme of crash barriers on highways should be considered when planning the snow removal.
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Usage of RWIS MeteoTrassa in the ITS
Intelligent Transport System (ITS) is used to control the car traffic at the Ring Highway, which surrounds St. Petersburg over the length of 141 km. The ITS ensures optimal traffic organization in automatic mode. To improve the quality of the ITS functionality meteorological information and information about road surface conditions has to be taken into account In its algorithms. Twenty five Road Weather Stations are mounted at the Ring Highway. The MeteoTrassa collects data from all of them every five minutes. It includes a prediction module to forecast temperature and state of the road and precipitation for 4 hours ahead. Forecasts are based on the numerical model of atmospheric boundary layer. Weather radar data are used to predict precipitation quantity. Procedure of thermal mapping has been applied for the Ring Highway. It allows us to retrieve data about surface temperature along the whole length of the Ring Highway. The RWIS provides ITS with the following information: current and forecasted road surface conditions, surface temperature, wind speed, and visibility. The RWIS MeteoTrassa transmits all required meteorological data to the ITS.
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Advances in winter maintenance decision support in the Czech Republic
For the winter season 2013/2014 the Czech Republic advanced to the higher level of winter road maintenance decision support. These improvements were connected with the standardization of winter maintenance operations on road network in the Czech Republic, which is to be presented at PIARC winter road congress in Andorra 2014. The standardization was defined as the evolution of the existing state, existing contractual terms and was focused on specification of relation between road authority and contractors. Besides this, the effort was carried out also in evolution of maintenance decision support.
For winter season 2013/2014, the fourth generation of national road weather information system has been prepared and launched. It brings many new features which partly react to users' demands. The RWIS contains also the latest version of national maintenance decision support system, which provides dispatchers with the road condition and road surface temperature forecast for each 1 km road stretch for up to 12 hours.
In 2013/2014 the MDSS system will expand to last three regions of the Czech Republic and so it will cover the whole country – all highways, Ist and IInd class roads. In this winter season it is also expected that the new MDSS module will be introduced, trying to provide dispatchers with the treatment recommendation.
Besides RWIS and MDSS the road authority also put effort to prepare detailed manual of winter maintenance which should help dispatchers to choose the best available technology. It extends the existing documents and legal conditions with further specification of different types of reaction to specific winter road conditions and phenomena dangerous for road traffic. As the direct result, the decision diagram has been produced to be placed on every dispatcher's table.
The presented paper is trying to describe the key features of decision support in the Czech Republic, by presenting the information and decision support system and the manual of winter maintenance.
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Roads Inundation Simulation using Weather Radar Rainfall in Urban area
Over the past few years in South Korea, flooding inundation and landslide have been occurred at the part of mega city named Seoul occasionally, and which was caused by concentrated rainfall with climate change and by land use change along the rapid urbanization. If it is possible to predict the inundation area and to provide some warnings to citizen, social and economic damage may be reduced. To predict and prevent these disasters, the WISE (a next-generation Weather Information Service Engine) project division has been started in 1st June 2011, funded by Korean Meteorological Administration. Through scientific advances in high-resolution weather forecasting, urban flood prediction, road meteorology and urban carbon dynamics and new urban service systems, WISE project has represented an investment over eight years for efforts to resolve urban environmental issues for the minimization and mitigation of the impacts of natural disasters and climate change on urban dwellers in Korea. Especially, the roads inundation occurred by heavy rainfall at 2010 and 2012, e.g. inundated area around Samsung-subway station at 21st September 2010, and Gangnam-subway station at 27th July 2011. The rainfall duration of these rain events was too short to forecast for the disaster. And roads inundation has attracted more attention than traffic accidents, traffic congestion, and construction because it is caused travel delays and threatens driver safety, simultaneously. In order to prepare the countermeasures of roads inundation, we will develop the simulation method of roads inundation using weather radar rainfall through the WISE project. For this study, we will estimate the surcharge flow using Strom Water Management Model (SWMM) for considering the drainage network. The surcharge flow is used as inflow of two-dimensional diffusive overland-flow model which was developed to simulate inundation in urban roads. Moreover, we use the weather radar rainfall, because rain is occurred locally (within a few kilometers) during short time (less than 1 hour) in urban area. Hence, weather radar, which has 10 minute time resolution with 250m ~ 1km spatial resolution, is useful for forecasting of urban roads inundation. The radar rainfall in the near future is predicted by extrapolation method using radar QPEs. Finally, the result will be providing through the integrated weather service of WISE platform for Seoul area.
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Impact of natural hazards on the roads of Andorra (1933-2000). Historical database analysis
Natural hazards are very present in a mountainous country like Andorra, of only 468 km2 in the Pyrenees. They affect everything on their way, like landscape, people, buildings, roads, etc. and they can be the cause of significant damage, and even, in the worst cases, of fatal victims.
As far as the Andorran roads are concerned, which are the main communication routes in the country, it's vital for roads managers to be aware of the natural phenomena that affect them, in order to know how to prevent and act to minimize their impact.
Analyzing the historical database of natural hazards based on press news created by the CENMA (Snow and Mountain Research Center of Andorra), between 1933 and 2000 the 5 natural phenomena that have affected more frequently the roads of the country have been rock falls, followed by tree falls, snowfalls, floods and landslides. Concerning the main meteorological phenomena associated (which are often the triggers), there is rain, wind and snow. Talking about fatalities, the most frequent phenomenon is not the one which has caused more victims. Indeed, in this period of study, Andorra has had to lament a total of 19 deaths caused by natural hazards impact on roads, which of 13 have been victims of a single episode of floods in 1982.
That rock falls are the phenomenon that have affected most often Andorran roads, according to press news, seems to be normal in a country with so much elevation, where roads go from about 850 m (border with Spain) to 2,400 m of altitude (Port d'Envalira, near the border with France) in just 40 km. But we must be aware that this database is not exhaustive regarding the impact of natural hazards on the roads, such as, for example, the snowfalls in the country not always appear on press news, whereas they are probably the phenomenon that request more means and dedication from the Andorrans roads managers.
Even if they are not part of the study period (1933-2000), it is worth mentioning several episodes of significant snowfalls suffered by Andorra last winter, causing more than one evacuation of population due to the high risk of avalanche, and a large increase in the cleaning tasks of the country road network. Examples of those important actions taken on the roads have been significant increases in kilometres covered by snow blowers and tons of salt thrown on the roads to minimize frost.
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Development of weather tools to forecast adverse road weather in Catalonia
Nowadays there is an increasing demand of more specific, georeferenced and high resolution meteorological information, due to the impact that meteorology has on several economic activities and also on the daily living.
Traffic is an example of this outstanding necessity. In the case of Catalonia, a country with a complex terrain that gives rise to climatological variability, it is necessary to supply precise information about ice, snow, fog, rain, hail and strong wind with the aim to solve the local particularities of each region.
So, for example, while it is essential to provide an accurate gust wind forecast to avoid the risk of overturning trucks in south regions, it is required to have an accurate fog model inland where the lack of visibility causes important collisions. Furthermore, independently to seasonal conditions, the management of intense showers that cause landslides and fast changes in visibility is crucial to avoid dangerous driving situations.
Consequently, the Meteorological Service of Catalonia provides a wide range of products that adapt to the particularities of each region. Among the last developments the 11th day forecasting stands out, thanks to the implementation of ensemble statistical prediction. On the other hand, the accuracy of prediction models with a higher resolution (up to 400 m) ensures the development of products that can simulate local conditions.
All these improvements combined with specific weather models for traffic including variables as fog, hail, visibility and type of precipitation, and with a system of alarms generated from radar information, can allow a better forecasting of adverse phenomena and a substantial benefit to apply management measures and for the activation of emergency plans.
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Quantitative analysis of the susceptibility of avalanche triggering on the road to Bonaigua pass(Pallars Sobirŕ county)
The prevention of landslide risks, as well as the phenomenon of avalanches, necessarily involves making their cartography. The characterization of a particular phenomenon requires, in a first stage, the knowledge of its susceptibility as a fundamental part to determine its hazard. Its cartographic representation is a basic tool for land use planning and especially in mountain areas.
This work has focused on the application and comparison of three methods to determine quantitatively the susceptibility of avalanche triggering on the road to Bonaigua pass (Pallars Sobirŕ county). 1) By using the analytic hierarchy method, 2) By Geographic Information Systems (GIS) spatial analysis and 3) Based on the multivariate statistical analysis.
Bonaigua pass is located at the northwest corner of the Catalan Pyrenees. Affectation due to avalanches in the road C-28 is a current problem. This infrastructure links Vielha (Val d'Aran) and Esterri d'Ŕneu (Pallars Sobirŕ), being the busiest connection between the two regions. The C-28 is the access road to the ski resort of Baqueira Beret, an attraction that in winter significantly increases the number of users of this road. The section considered in the study has a length of 10km.
The study has been based on the analysis and understanding of avalanche terrain. Its aim is to classify the starting zones according to their susceptibility and to implement the results in areas without observational data. The final goal of the study is to help to improve the knowledge of the susceptibility of avalanche triggering in order to prevent and mitigate the consequences of this phenomenon in infrastructures and roads.
The results show how the values obtained from the three methods have a high correlation. In spite of the degree of subjectivity introduced in the process of the analytic hierarchy the results have a good fit with the results of the other two methods. Finally, the GIS analysis and the multivariate statistical analysis require to use observational data, while is not required when applying the analytical hierarchy method.
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Lunada Port: Singular example of isolation by road in the XXI century in Spain
The Port or "Portillo de" Lunada is a mountain pass located in the easternmost part of the Cantabrian Mountains and connects the Espinosa de los Monteros town in Burgos, Las Merindades región, with San Roque de Riomiera town, one of the Three Villas "pasiegas" of Cantabria.
BU572 - CA643 is the name of that road and crosses Castro Valnera Massif,in the south leaving the summit of Castro Valnera (1718 m) and in the north Picon del Fraile ( 1625 m).
For a few days of autum and spring and especially during the winter , this road is often closed due to the abundance of precipitation as snow and avalanche danger.
This is the third mountain pass over height of Cantabria , behind the ports of San Glorio ( 1609 m) and Piedraluengas (1354 m) that connect the provinces of León and Palencia with Liébana in the Cantabrian region , through the Picos de Europa Massif.
During the winter of 2012-2013 the Lunada Port closed more than 40 days and the Unit Army of the Air, Air Surveillance Squadron 12th number , with air base at 1600 m elevation in Picón del Fraile was more than 40 days isolated by road.
On February 5 was the last day that could be accessible by land after performing cleaning the road with trucks snow plows and milling . Previously , in 2005 were isolated 28 days.
In the environs , is the Lunada winter resort , the lower sky station at a lower height above sea level in Spain , managed by Detulsa company , due to the accumulation of snow was not able to open access to over many winter days.
In this paper we analyze and study this unique case of heavy snow on relatively low sea level and the influence and snow conditions on the roads and infrastructure in the region.
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