15th Conference – Quebéc City

Hoar frost formation is depending on the amount of water vapour in the air and the road surface temperature. But does it matter where and how high above the road the humidity is measured?
Road climate within a few centimetres above the road is, depending on the weather situation, different from the climate above. In clear cold weather, a strong temperature inversion is created near the road. The road temperature drops significantly and the air immediately above the road cools. The cooling of the air means hoar frost formation on the road, but it also means that the amount of humidity in the air is lower in terms of the dew point value. Therefore the vertical dew point gradient will be positive and some difference between the dew point in 10cm and the dew point in 2m or higher should be expected. The Danish Road Directorate has performed measurements to prove the relevance of measuring dew point temperature 10cm above the road. The results indicate that these measurements should be taken into account when considering possibility of frost formation in certain weather situations. The results are presented and the related weather situations described.

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The main objective of IRWIN is to develop an improved winter road index capable of assessing the implications of climate change in various weather parameters and also related costs and benefits. Climate change scenarios have so far been calculated based on ordinary meteorological data, which have large limitations in respect to resolution. The idea of IRWIN is to combine the best traditionally made climate scenarios with the much more accurate spatial data from field stations in the Road Weather Information systems (RWIS) installed in most northern hemisphere countries. The data collection phase of IRWIN revealed that there is enough archived RWIS data in Sweden and Finland to perform the planned winter index development. Ten years of observations were collected from 50 road weather stations in Sweden and 49 stations in Finland. Observations in each country were divided into three regions with distinctive climatic characters. Extensive processing had to be performed first to create a high-quality database with corrected and uniform observations. Maintenance activities from the regions of interest were collected as well, to be used in the winter index calculations. The climate downscaling methods and the climate scenarios are described, and first results shown. Rules for the index development are given. In other countries, similar assessments could be thus done relatively easily if enough road weather information was archived and available. Target user groups of IRWIN results are the road owners and administrations in ERA-NET countries and the EU.

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Maintaining control of snow/ice buildup during winter storms is challenging for winter road maintenance entities. Among these challenges is to make effective and efficient decisions for treatment types, timing, precipitation rates/types, and location of greatest impact to the roadway. These decisions are critical because of the implications to roadway safety as well as economic impacts to the agency. In order to try and mitigate the challenges associated with winter road maintenance, the United States Department of Transportation (USDOT) Federal Highway Administration (FHWA) initiated the development of the Maintenance Decision Support System (MDSS) in 2001. MDSS provides a single platform, which blends existing road and weather data sources with numerical weather and road condition models, in order to provide a display of the diagnostic and prognostic state of the atmosphere and roadway (with emphasis on the 1- to 48-hour time period) as well as a decision-support tool for roadway maintenance treatment options. In addition, during this past years winter season, MDSS was modified and applied over Denver International Airport (DIA) including all six runways and the main arterials leading into the airport. The objective of this presentation is to provide an overview of the present (and future) strategic and tactical capabilities of the MDSS system as they relate to the forecasting of weather that may impact the roadway/runway maintenance operations for various decision-makers.

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Today´s automatic mesoscale networks of meteorological surface stations allow a real time monitoring and model validation of the weather status with a very high resolution for various applications. One important application is road weather. At the University of Vienna, Austria, a tool (VERA – Vienna Enhanced Resolution Analysis) has been developed for this purpose. It consists of a quality control module, a downscaling module, the spatialization module and a graphical user interface (GUI). There are different modes of applications possible:
1. It allows to detect the microclimatological specification of each single station, which is especially important with road weather stations.
2. Problems of sensors are encountered immediately by the sophisticated quality control module, so that measures to avoid misinterpretation of data can be taken.
3. The analysis and downscaling module allows to make maximum use of all sorts of additional information like satellite radiances, radar or thermal maps.
4. In the model validation mode the system can detect even minor model derailments and hence allows to adjust short range forecasts towards the observed state.
5. In a climatological mode the system allows to retrieve valuable information for planning purposes with respect to new road routing or weather related road maintainance.
6. In the lecture an outline of the method will be presented together with case studies, which demonstarte the potential of the system for road weather applications.

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The meaning of friction coefficient as a measure of a slippery road surface due to the presence of ice, snow or hoar frost is discussed. Practical means of measuring friction on highways are briefly reviewed. Also a new simple and inexpensive tool to measure friction is presented together with data from various slippery situations.

Friction coefficient is a well defined and unique quantity to describe grip of car tires to road surface. Loss of adequate grip will have dramatic consequencies for maneuverability and stopping distance. A thin layer of ice, which may have developed on road surface either by freezing, precipitation or condensing, can reduce friction coefficient easily by a factor of three to four causing the braking distance to increase by the same factor. It is one of the main tasks of winter maintenance to avoid situations developing to slippery surfaces. Spreading of deicing chemical prior to freezing is a widely proven method to keep friction coefficient high enough for safe driving. Practical measurements of friction coefficient reveal that if deicing chemicals have been applied in due time, a fair part of the solution may still freeze, but in this case the freezing does not cause a dramatic reduction of friction. There is a simple reason for this behaviour. When a solution of water an d deicing chemical freezes, the forming ice is mechanically softer than clean ice. Thus friction coefficient can stay at a much higher level compared to the case without any deicing chemicals. It turns out also that the actual amount of ice can be much higher with soft ice than with hard ice and still the friction is better. Usage of deicing chemicals to prevent slippery surfaces can be optimised by letting surface to partially freeze but still keep friction high. This is done often unintentionally by assuming that the surfaces are just moist or wet without any ice due to the chemicals. Nevertheless, it is essential to know the development of surface friction in these cases to prevent slippery surfaces due to increasing ice amount with dropping surface temperatures causing finally a slippery surface. A demonstration measurement of friction under different surface conditions is presented.

Practical means of measuring friction on highways can be divided to methods of (1) measuring mechanical grip force directly, (2) measuring deceleration while braking or (3) indirect means measuring amount of water and ice. These means are reviewed with a discussion of advantages and limitations including a comparison of measured response of two different acceleration based approaches to measure friction.

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Hokkaido is the northernmost island of Japan, and is characterized by cold, snowy winters. In April 2008, a blizzard struck the eastern part of the island, causing the closure of many roads; locals reported that they had never experienced a blizzard of such intensity. To enable the evaluation of blizzard severity, a method to calculate the amount of accumulated snow transported during a single blizzard was suggested. According to Takeuchi (1986), the conditions required for blowing snow occurrence during snowfall are as follows:
1. an air temperature of =- 2°C and a wind velocity of > 5.0 m/s, or
2. an air temperature of = 0.5°C and a wind velocity of > 7.0 m/s.

When meteorological conditions meet these criteria, the transport rate of blowing snow Qt (g/m/s) is calculated using the following empirical expression: Qt = 0.03 * V1 ^ 3, where V1 is the wind velocity at a height of 1 meter (m/s). Accumulated snow transport was derived by summing up the Qt values for the duration of the blizzard. Using this method, the amount of accumulated snow transported during the April 2008 blizzard in eastern Hokkaido’s Attoko area was calculated as 1,540 kg/m. The return period of the accumulated snow transported during this blizzard was then calculated using the Iwai method. The results indicated that the return period for a blizzard of this scale was 1,000 years or more.

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This paper discusses the examination of remote road surface state sensors for temperature (Vaisala DST111) and road conditions (Vaisala DSC111), and assesses the suitability of the new paradigm, which is being introduced by a producer, in the Slovenian area.

The analysis of the results of measurements obtained by remote road surface state sensors, embedded road sensors and observations of an expert is made. The outcome data are assessed on the basis of their suitability for further analysis and the forecast of road conditions, and are also assessed from environmental point of view. It is important to mention that the analysis focuses only on road temperature and condition. The methodology includes the data acquisition, the selection of parameters and the statistic analysis of the data. From January till April 2009 there have been approximately 5000 pairs of measurements acquired and approximately 100 observations made on a selected highway section in Slovenia. The results of the analysis are presented graphically and in tabular form.

The results of the road temperatures show a high degree of correlation, especially in a critical temperature range around 0 °C. Larger temperature deviations occur mostly in the morning. When the correlation with the meteorological parameters was made the results showed that the days with larger temperature deviations are always correlated with a high solar radiation and vice versa – when the solar radiation is low the temperature deviations are smaller.

Overall, the results of the comparison of road condition are in general satisfying. The deviations which occur in critical weather situations have been examined thoroughly and are in the paper also presented through observers’ photos. Most of the deviations between remote road surface state sensors and observers occur when remote road surface state sensors indicate a dry road whereas observers claim that the same road is moist (and vice versa).

The results of the comparison of remote road surface state sensors and embedded road sensors also present their advantages and disadvantages. The advantages of remote road surface state sensors are: they are less sensitive to solar radiation, they take measures on a larger road surface, it is easy to install them. These sensors do not measure salinity but they do give results of a new parameter (grip). At present no direct connection between the grip parameter and the systems which provide information that is needed to make winter road maintenance decisions is possible. The results also show that additional temperature measurements in the depth of the road are required when using remote road surface state sensors in order to get the same forecast of road condition as with a physical model.

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Given the non-linear and dynamic nature of weather systems, it is inevitable that errors in weather forecasting will occur. Some of these errors can have significant operational impact on winter service activities. In developing maintenance decision support systems, care must be taken to allow for such errors, by creating systems that are resilient when such errors occur. The lecture will explore both the types of errors that can occur and possible ways of making sure that forecasting errors do not create operational calamities.

While there are numerous ways in which a forecast may be in error, some of these errors pose more of an operational challenge in the area of winter service than others. In particular, errors in prediction of storm start times, of precipitation type (snow, sleet, freezing rain), and surface temperature can have significant impacts on operational actions and on road surface conditions. These operational impacts are explored in detail.

Given the inevitability of forecast errors, it is imperative that decision support systems be sufficiently resilient to allow for the errors. This resilience can be achieved in one of two ways. First, the link between decisions and forecast can be somewhat decoupled, and second, feedback of real time information on road surface conditions can be enhanced. Decoupling the link between forecast and decisions can be done either manually or automatically. If done manually, essentially the supervisor using the decision support system can adjust the forecast so as to examine how small changes in forecast behavior may or may not require radical changes in operational actions. If no radical changes are observed, then small errors in the forecast will likely have little effect. This process can be done automatically, but if so then the forecast must be expressed in such a way that it can be represented by way of a matrix. Then the forecast can be adjusted from that given by moving one “cell” in each direction for each “dimension” of the forecast matrix. This idea is explored further in the presentation.

Feedback on road surface conditions is important for decision support systems for many reasons, not least of which is that such feedback may help to improve forecasts over time. However, in the context of this presentation, the feedback may be of special value since it provides an outcome oriented measure of the accuracy of the road weather forecast. If the forecast is correct and the specified actions have been taken, then the road surface should be in an acceptable condition. If it is not, then either the forecast was lacking or the appropriate actions have not been taken. Either way, corrective action can be taken. However, such feedback should, ideally, be obtained through automated means rather than requiring operator input.

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MeteoTrassa system has been developed to provide the road authorities with a decision support tool for winter maintenance. It provides actual data, warnings, and short range forecasts of the road weather. The system is based on the following data inputs: road weather stations and weather cameras, thermal mapping, weather radars, hydrological sensors, regional forecasts and warnings, local and mesoscale forecasts. The system takes an advantage of existing weather data sources, combines data to present integrated road weather observations and predictions for the road network and generates recommendations on road treatment strategies based on standard practices for effective winter road maintenance. Fifteen automated MeteoTrassa systems have been installed till now. One of the biggest systems has been operated in Krasnodar region since 2001. In this case road network consists of 28 road weather stations and 41 weather cameras gathering data every 30 minutes. Thermal mapping of roads (more than 1900 km) has been performed till now. Database of thermal maps is used for actual data visualization and forecasting. Forecasts of road surface temperature and road surface conditions for the road network are available for 1 – 4 hours ahead with updating every 30th minutes. Composite radar images are generated based on the radar network consisting of five radars in the North Caucasus. Five hydrological sensors give information for monitoring of floods. Central computer MeteoTrassa delivers data to the workstation at the regional Hydrometeorological Centre and to 45 user’s workstations to supply road service providers with data and forecasts and recommendations on the treatment procedures and the timing. The model has been verified against observations from 56 road weather sensors during winter seasons of 2007 – 2008 and 2008 – 2009. New weat her road stations in the region are planned to be installed. Models are to be improved for most adequate forecasting for such complicated landscape ranging from sea coastal plains to mountainous terrain. Recommendations on road treatment strategies are to be more accurate and tailored to the procedures of the local service agency.

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Throughout the year road maintenance operators face many challenges when making decisions about how to keep the roadways safe during adverse weather and when to perform general pavement and roadside operations. These decisions are highly dependent on knowing what the projected weather and pavement conditions will be like at the time of operations. Thus, accurate weather and pavement condition forecasts are very important in helping maintenance practitioners make effective decisions. These decisions have considerable impact on roadway safety and efficiency and poor decisions can have unfavorable safety, economic, and environmental consequences.

In 2001, the Federal Highway Administration (FHWA) initiated a program in an attempt to address the road weather related challenges associated with winter maintenance operations. Through this program, the Maintenance Decision Support System (MDSS) was created. This system is designed to provide winter maintenance managers and crews with objective guidance regarding the most appropriate treatment strategies to employ during adverse weather events. The MDSS has evolved considerably over the last 8 years and although it is sill being used primarily for winter maintenance, the concepts are now being applied to non-winter decision support systems aimed at helping practitioners make non-winter maintenance decisions, such has when to pave, install new signs, and mow or weed along the roadside.

A key requirement of any road decision support system is the ability to provide accurate, precise, and timely road condition forecasts. For the past 3 years, NCAR’s MDSS has utilized the Model of the Environment and Temperature of the Roads (METRo), which is a physically-based numerical model developed by Environment Canada. METRo has proven to be a reliable and accurate pavement model across most winter situations, but is relatively untested for non-winter operations. With funding from the FHWA, NCAR is now in the process of developing a non-winter decision support system (XDSS) that will utilize METRo to forecast summer pavement conditions.

This paper describes the use of METRo as a pavement model within the MDSS and XDSS framework as well as its use as a tool for determining road-temperature quality control (QC) values. Discussion topics include, but are not limited to, improvements made to the model by the METRo developers, improvements in the implementation of METRo in NCAR’s realtime systems, and the use of METRo in determining quality-check (QCh) values for Clarus road-temperature observations. Verification results will also be presented showing METRo’s performance during both winter and non-winter months. Based on these results, recommendations will be made on how METRo can perform better to the serve the needs of both a winter and non-winter maintenance decision support system.

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In a typical year, there are 1.5 million weather-related vehicle crashes in the U.S., leading to 673,000 injuries and nearly 7,400 fatalities. Adverse weather and the associated poor roadway conditions are also responsible for 554 million vehicle-hours of delay per year in the U.S., with associated economic costs reaching into the billions of dollars. One possible solution for mitigating the adverse impacts of weather on the transportation system is to provide improved road and atmospheric hazard products to road maintenance operators and the travelling public. With funding and support from the U.S. Department of Transportation’s (USDOT) Research and Innovative Technology Administration (RITA) IntelliDrive initiative and direction from the Federal Highway Administration’s (FHWA) Road Weather Management Program, the National Center for Atmospheric Research (NCAR) is conducting research to develop a Vehicle Data Translator (VDT) that incorporates vehicle-based measurements of the road and surrounding atmosphere with other, more traditional weather data sources, and creates road and atmospheric hazard products for a variety of users.

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Maximizing roadway safety, mobility and efficiency, while minimizing maintenance-related costs, is the common goal of winter maintenance engineers around the globe. The ability to achieve this goal is fundamentally grounded in the decision-making process carried out by such engineers. The inability to make accurate decisions in an effective, efficient manner can result in less than desirable roadway level of service and in some cases, the loss of life. This presentation discusses the concept of a decision support system for winter maintenance operations, a concept that has been gaining increasingly widespread support as a way to address the difficulties associated with the winter maintenance decision-making process. Moreover, some of the challenges associated with providing accurate guidance ar e discussed.

A winter maintenance decision support system, in the context of this discussion, is an interactive, automated system capable of providing winter maintenance personnel with objective guidance regarding options on how to treat networks and routes when hazardous road weather conditions are imminent or exist. The guidance supplied by such a system is based on best practices for anti-icing and deicing, along with road condition analyses and predictions. Eutectic properties of chemicals can also be used in place of best practices (i.e., estimating and tracking chemical concentration and performance). The system automates the procedure of obtaining, synthesizing, and applying road weather data and information in the decision-making process. Guidance provided by the system can include information regarding treatment timing, rate, location, and type. Optionally, an end user can interact with the system to ascertain the consequences of action or inaction.

In order for a decision support system to fully meet the needs and requirements of winter maintenance engineers, it should include some fundamental components, as this will enable the system to be optimized for operations. First, the application should have the capacity to retain data about the road network of interest. These data include, but are not limited to, the as-built properties of key routes, available maintenance resources, and maintenance practices unique to the Authority being served. Second, an application should possess the ability to ingest and process observations from environmental sensor stations, as well as forecast data. Together, these data can be used to drive an energy and mass balance road condition model, providing insight into the current and future state of road weather conditions. Treatment recommendations are derived through a separate module that processes and analyses the observations and forecasts in conjunction with winter maintenance best practices. Clearly, there will be a need for any system to access the most accurate data available, both observed and forecast, to ensure that these recommendations are sensible. Finally, the end user should be able to view data and information and interact with the system through an intuitive graphical user interface. A decision support system that uses the aforementioned components as a foundation can effectively act as a “one-stop-shop” for the winter maintenance engineer, since it ingests and processes all the available information to produce succinct, repeatable guidance.

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Adverse weather conditions have without a doubt a significant impact on traffic operations, quality of traffic flow and en-route safety. Active weather-sensitive traffic management strategies can be developed and improved with the deployment of road weather information systems (RWIS), road weather forecasting services, and ITS traffic data archives. In view of the paramount importance of weather-responsive tools for real-time traffic surveillance, a European initiative was launched in 2008 within the European Cooperation in Science and Technology (COST) framework. The main objective of this initiative, namely COST Action TU0702, is to understand the impacts of adverse weather on traffic operations and to develop, promote and implement strategies and tools to mitigate such impacts. Furthermore, the Action fosters the exchange of know-how between interdisciplinary sciences (e.g. road engineering and meteorology), road operators and road maintenance authorities. As of late 2009, altogether 16 European COST member states have joined this research consortium, along with two extra-European countries.

This paper reports on the main conclusions of the state of the art and practice research activities conducted as the Action’s initial major activities during its first year of existence. First, the meaning and conception of “adverse weather conditions” and specific definitions of weather events considered as “adverse” in relation to road traffic are explored. Thereafter, recently compiled research reports and good practice cases are summarized based on the state of the art report covering the perspectives of selected EU countries. Finally, identified research gaps and needs are introduced as basis of future research work and collaboration within the Action TU0702. As an example of the needs, more comprehensive databases are called for to be able to analyze and utilize a wide range of weather event intensities with an objective to identify which types of traffic parameters are affected by given weather events. A further research need deals with the integration of the outcome of such studies into eventual traffic models. More specifically, research effort should be put in designing weather-dependent traffic flow models, i.e. developing basic guidelines on how to integrate weather information into traffic simulation models and, ultimately, into traffic management strategies. Preliminary results of such efforts will be presented.

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Weather plays a key role in most traffic accidents, in which over 40.000 European citizens are killed and more than 1,2 million injured every year. It is thus clear that innovations are necessary to improve traffic safety. As transport is responsible of 20% of green house gas emissions in Europe, better transport services are needed to make traffic more efficient with reduced congestion.

To find answers to these problems, project ROADIDEA “Roadmap for radical innovations in European transport services” was initiated. It is a European Commission co-funded Collaboration project that started in the end of 2007 and continues until mid-2010, see www.roadidea.eu. The main objective is to study the potential of the European transport service sector for innovations, analysing available data sources, revealing existing problems and bottlenecks, and developing better methods and models to be utilized in service platforms. These will be capable of providing new, innovative services for various transport user groups.

The fourteen partners of ROADIDEA come from Finland, Sweden, the Netherlands, Germany, Italy, Hungary, Croatia and Slovenia. The differences of the existing transport systems and available data sources in these countries are analysed as well as the problems caused by local climate and geography. The innovation process is key activity of the project. It has produced more than 100 ideas during two consecutive brainstorming seminars. Ideas have been analysed and the most potential ones shortlisted for research and development. Best ideas will be described in detail, including e.g. advanced friction models, road condition and fog warning systems, which receive input from hybrid (mobile and fixed combined) observing systems and automated messages from private cars. The Hamburg Port with increasing congestion needs multi-modal traffic model that takes weather into account. New semi-public high-quality ways to travel need to be innovated to reduce the use of private cars in the battle against climate change.

ROADIDEA is also extending outside Europe through an international cooperation project with FHWA and its Clarus initiative in the USA, and Environment Canada. Innovation seminars will be conducted and results disseminated to local stakeholders.

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Introduction/Context: The main objective of the EU FP7/INFSO research project ROADIDEA is the development of new and innovative services for European transport systems. It aims at improvements of road traffic conditions and forecasts with special respect to certain weather conditions in different parts of the European continent. The main prerequisite for achieving this goal is the knowledge about and the availability of valuable and high quality road traffic and weather data sources, which have not been used systematically so far. The base for the targeted approach to be applicable all over Europe is twofold. It consists of the following elements: a comprehensive investigation of available data sources in Europe and;a reliable data mediation between data/service providers and the project data archive. The investigation of available data sources and a tailor-made data mediation system have been the subjects of the ROADIDEA work package 2 (WP2) “Data Collection”. With the start of the project WP2 began in December 2007. All five WP2 tasks (2.1 – 2.5) have been finalized by now. The results are reported with corresponding public deliverables D2.1 – D2.5 which can be found at www.roadidea.eu. This paper describes the main result and achievements of the mentioned data collection as well as the applied data mediation system. Besides that some example are given how data may be assessed, utilized and merged.

Methodology: With the experience of the DLR clearing house for transport data a data survey questionnaire was designed. This questionnaire was used both to identify data sources which are available in the project member states and which are ascertained and utilized by ROADIDEA project partners. This data source identification covered the following initial main fields:data from vehicles; data from infrastructure; weather monitoring.

More detailed data source analyses took place in Germany, Finland, Croatia and Italy. The focus of these analyses was to compare general data availability in different European countries with special respect to the degree of development in terms of data policies and to determine a data archiving and mediation model. With the data source description among more general elements data access possibilities, licence fees but also data quality matters were considered thoroughly. While describing, comparing and analysing the data a very good understanding of its contexts and its contents were obtained. Based on these analyses a project specific data mediation architecture was set up. It follows a generic attempt to connect available raw data sources with service provision data needs via a data mediating architecture.

Results: This paper emphasizes on the main data related findings and results of the project ROADIDEA, which could be from interest for the SIRWEC activities. During the data source identification 55 data sets have been described, partly supplemented with related documents as codebooks, legends or data samples. Based on the data archive a tailor-made XML data scheme has been generated. This scheme is used within the data mediation architecture. Both the scheme and the mediation architecture are described with this paper. Using the ROADIDEA mediation architecture some simple examples give an idea how collected information have been used for the implementation of project pilots as well as for theoretical models, which have been developed within the project in order to combine traffic and weather data.

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To combine weather information with other data source is especially challenging task because the data is very special. Weather data from different sources and with variation in spatial and temporal scale are analyzed together with traffic data. The overall aim is to be able to determine the actual road conditions. The task also involves work concerning quality of data and how different data sources can be combined to increase the information. A new innovation is to use the information about variations in traffic to detect effects by weather elements for example precipitation or slipperiness.

The analysis shows that weather has an obvious impact on traffic and also that it is possible to build a model with the ability to recognize the weather (with weather history), which affects traffic in a negative way. These findings can be used for future development of new information systems. This paper describes a method for modeling weathers impact on traffic, as well as the results obtained when applying that method. The analysis comprises preprocessing, a method for visualizing the effect of weather on traffic parameters (velocity and speed per time of day) and also model building via a decision tree classifier. The visualization is applied to build a dataset with classified samples; “traffic disturbed by weather” or “normal traffic”. A decision tree classifier is used to train models to recognize the combinations of weather parameters that lead to disturbed traffic. The visualization shows a distinct correlation between precipitation and changes in traffic pattern and the decision tree models have a good/useful performance.

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Icy and snowy road surfaces increase traffic incident rates heavily. Friction is defined as the grip between the car tire and the underlying road surface, and friction reduces markedly in cases of freezing rain and snowfall. Also, when wet or damp surface cools down below zero degrees centigrade the road surface becomes notoriously icy. Friction, as considered here, has the value of 0.8 under dry and clear road conditions. Water and especially ice or snow on the surface will reduce friction values down to a minimum of c. 0.1. Braking distances become higher and traffic incident risks increase under low friction circumstances. Road maintenance activities (e.g. salting), on the other hand, are utilized to improve the grip.

The Finnish road weather station network operated by the Finnish Road Administration covers c. 500 stations in Finland. Almost 100 of them are equipped with optical sensors (as of autumn 2009). The number of these optical devices has increased remarkably during the past few years. The devices in use are Vaisala DSC111 sensors which measure the depth of water, snow and ice on the road surface, producing also an estimation of road condition and friction. Friction is one of the parameters to define the so-called road weather index but until now there have not been any tools to forecast it. Measured friction values and other road weather observations at given observing stations have been used to define a statistical friction model introduced here. The condition of car tires and the road surface may of course have significant impacts on friction but they are not covered in this application. The data for the friction model were the road weather observations measured at the Utti station during winter 2007-2008. Utti is located in southern Finland, c. 50 km from the coastline. A correlation analysis between the observed friction and the thickness of water/ice/snow was performed, and a strong correlation was found under icy and/or snowy road conditions. There was also a small temperature dependence. Water on the surface was found to reduce friction, too, but in cases of wet or damp surface there was no temperature dependence. The resulting model defines friction as a function of the road surface temperature and the thickness of ice and/or snow on the surface, when ice and/or snow exist. For a wet or damp surface, friction is calculated based on the thickness of the water layer, only. The statistical friction model is further linked to Finnish Meteorological Institute’s (FMI) operational road weather forecast model. The initial results look quite promising with the correlation between observed and modeled friction under icy/snowy conditions being 0.85 when adapted to independent data from the Utti station during the following winter, 2008-2009. Under wet/damp road conditions the correlation was as high as 0.93. The model will be further tested in an operational forecasting environment by FMI during winter 2009/2010, when the model output will be forwarded both to duty meteorologists and road maintenance authorities. The forecast model will eventually undergo a comprehensive verification undertaking after the winter season (see the follow-up Abstract by Nurmi et al).

This study was carried out within the EU/FP7 Project ROADIDEA, where the major goal is to develop new and innovative products and tools for traffic and transport sectors.

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The Finnish Meteorological Institute (FMI) has recently developed a physical-statistical forecast model of road surface friction which was introduced in the preceding paper by Hippi et al. The model is based on correlation analysis between observed friction, using Vaisala DSC111 optical sensors, and other road weather observations at a given single weather station (Utti) in southern Finland. The statistical relationship was derived from measurements at Utti during winter 2007-08, and then validated using independent data from the following winter 2008-09. The initial results, when adapted to this independent dataset, showed a correlation of 0.85 between observed and modeled friction under icy/snowy road conditions and 0.93 under wet/damp conditions. The derived statistical relationship has been linked to FMI’s operational road weather forecast model, which takes its input from a physical numerical weather prediction model, to produce forecasts of surface friction without human intervention. Friction per se has not been a forecast parameter, having up till now been interpreted from the more conventional weather parameters by professional end users. Because we have at a hand a totally new forecast product, it calls for extensive analysis and testing. Hence the model will be adopted and tested in the operational forecasting environment at FMI during winter 2009-10, and a comprehensive scientific verification procedure will follow. The forecasts will be targeted to a number of road weather stations in Finland operated by the Finnish Road Administration, as c. 100 of them are equipped with optical sensors facilitating comprehensive verification. The eventual friction forecasts can be spooled into categories of explicit pre-defined values exceeding a given threshold, e.g. 0.1 accounting for extreme slippery conditions, or 0.8 for dry road surface. Thereafter, evaluation adopting verification methodology for categorical forecasts is technically straight¬forward. However, this study will employ new, emerging techniques in the discipline of meteorological forecast verification like the Extreme Dependency Score (EDS) and the Symmetric Extreme Dependency Score (SEDS), along with some of the more conventional verification metrics. Therefore, in addition to providing feedback on the capabilities of the friction forecasting system itself, this study will give valuable information on these new verification measures. The SIRWEC Conference 2010 will be the first forum to showcase these early results. This study is associated with the EU/FP7 Project ROADIDEA and the EU/COST Action TU0702. The major goals of these undertakings are to study the adverse effects of weather on traffic and to develop new and innovative methods and tools to increase traffic fluency and safety.

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Driving conditions on winter roads are mainly determined by visibility and slipperiness. Both variables depend on weather and environment and cannot be easily estimated. Therefore it is of importance to develop a method by which the corresponding information could be rapidly provided for traffic participants and winter roads services. In the framework of EU project ROADIDEA the company Amanova from Technology Park of Ljubljana has developed for this purpose a new method of statistical modeling of complex physical phenomena and incorporated it into a computer program by which data from weather forecasts and road position can be transformed into variables describing the driving conditions. The basis of the code is a non-parametric statistical model that joins weather and geographical data with the variables that describe the visibility and slipperiness.

The visibility predominantly depends on concentration of particulate material in the air above the roads surface that can be well represented by measured PM10. More problematic is to describe the slipperiness since the physically defined friction coefficient does not correspond to the property that is perceived by drivers as the slipperiness. In the framework of Swedish research project SRIS (www.sris.nu) the estimation of the slipperiness was therefore performed by a fleet of expert drivers. For this purpose they have utilized personal feelings during driving, as well as data provided from a network of ABS and ESP sensors. However, such an intuitive estimation leads to a serious problem if we want to develop an automatic information processing system that could forecast the driving conditions based upon weather forecasts. In our approach this problem was solved by development of an intelligent computer program that learns from data obtained by previous observations to estimate the slipperiness from the environment state with a similar accuracy as an expert driver.

The program utilizes a statistical basis of joint data about the state of environment and driving conditions. At a forecasting of driving conditions the computer first obtains new data about the environment and then compares them with the corresponding ones in the data base. Based upon their similarity, the associated stored data about the driving conditions are then accounted in the forecasting of new driving conditions that are non-parametrically expressed by the statistical conditional average. Such a procedure corresponds to an optimal statistical estimation and resembles associative estimation of unknown properties from given sensory and memorized signals in neural networks of intelligent living beings.

The performance of the corresponding forecasting method is characterized by the correlation coefficient r between estimated and really observed data. In the article we demonstrate the performance on the forecasting of driving conditions by using slipperiness data provided by the SRIS project in Sweden and data about PM10 in the Po valley provided by ARPAV, Centro Meteorologico di Teolo, Italy. Relatively high values of correlation coefficient (r~75%) indicate that the proposed method is applicable for prediction of hard winter driving conditions.

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In the framework of the EU project “ROADIDEA” two innovation seminars were organized: in Prague (2008) and in Dubrovnik (2009). During these seminars 34 different innovative ideas have been discussed. (See Dr. Auli Keskinen’s presentation ) Out of these 34 ideas 12 have been analyzed in details. The participants of the project have accepted among others 6 pilot projects. One of these pilots is the “Fog warning systems”. The fog has influence on the level of service and the safety of the road traffic. The amount of this influence is different but significant in the individual countries. During the preparation for the pilot we carried out analyses regarding the unfavourable weather conditions, first of all regarding the effects of fog on road traffic and road safety situation.

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Way finding is a key aspect of travel of all kinds, but the costs of inaccurate information are highest for human powered transport (bicycles and pedestrians). Even in the Netherlands, with the extensive provisions for bicycles, the management of routing information catering specifically for bicycle rider needs has many failings. The present paper describes a process and tools for addressing these information shortfalls, making use of rider input on a continuing basis, and delivering a bicycle trip planning system that is the more reliable for it. Recently, as part of the RoadIdea EU FP7 project on innovations in transport, a real time link to the Dutch weather service (courtesy of the KNMI) was tested as a part of the trip planning system. The real-time link provides route rainfall prediction and allows users to determinie if postponing a trip helps in avoiding the rain. This successful overall model for harnessing user participant knowledge (aka “crowd sourcing’), linked to what is clearly an ITS system with dynamic weather integration, shows both that ITS has major and appreciated benefits for a category of road users that are most affected by bad weather.

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Winter weather is a significant cause of road traffic accidents in Northern Europe. This paper uses a combination of climate change scenarios along with temporal and spatial analogues to investigate the relationship between temperature and severe road accidents in Sweden and the UK. These approaches allow for the quantification of change in the severity of the winter seasons over the next century in the two regions. Whilst the predicted reduction in the number of frost days by climate change scenarios should reduce the number of road accidents caused due to slipperiness, the use of analogues currently suggest otherwise. The paper attributes this to complacency in driver behaviour, but also identifies the potential pitfall of complacency in future winter maintenance regimes. A warmer climate may result in budget cuts for highway maintenance which in turn may well reverse long-term declining accident trends.

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A road surface temperature fine forecast model is built and tested using road stations observed data in Beijing city. The model is developed based on the Common Land Model (CoLM) and Beijing Rapid Update Cycle Model (BJ-RUC). The observed data is measured by ROSA, Vaisala Company of Finland. The temporal resolution of the model is 1h and forecast time span is 24 h and update frequency is 3h. This model considers the physical characteristics of road surface sufficiently such as the imperviousness, relatively low albedo, low heat capacity, high heat conductivity and nearly no evaporation etc. The model uses the variational data assimilation system to assimilate the observational road surface data and the influences of the anthropogenic heat and urban boundary layer are also considered. The verification results indicate that the fine forecast model can simulate the diurnal variation and the maximum value of road temperature very well comparative to the observation. Using the numerical forecast model can forecast road temperature more accurate especially when the road temperature is extremely high; the error would be reduced about 70-80% comparative to only using the BJ-RUC model. The accurate prediction of the road surface temperature can greatly reduce the occurrence probability of road traffic accident such as the tire burst effectively.

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This presentation will give an overview of the Danish education programme for RWIS users. In Denmark we have a year long tradition in close cooperation between the Road Directorate and the Danish Meteorological Institute and the cooperation also includes the education programme. The main aim of the programme is to make the users understand the basic principles in road meteorology, and to make the users familiar with the RWIS system. The education programme consists of two 1-day courses; one for beginners and one for more experienced users. Each course is a mixture of theoretical lectures and practical exercises with the special designed “RWIS-education” system.

This system provides the opportunity to virtually “be on duty” and handle historic weather situations, while learning to navigate the RWIS-system, scoring points answering theoretical questions and retrieving important and case-relevant information from the RWIS.
You can say, that a “state of the art” RWIS system not worth much, if the end-users doesn’t now how to use the system and interpret and analyse the cascades of information and output. Of course you also have to take this question into account when you design the RWIS system and educate users.

In these years, when increasing amounts of information is freely available on the internet, it is of growing importance to give the users a realistic relation to uncertainties and the quality of numerical weather modelling.

It is of great importance, that the users of the RWIS system quickly become familiar with improvements and new functions in the RWIS system. Such information must reach the users continuously. Feed back from users is each year collected, and on a one-day seminar every year future improvements and developments are discussed with the end-users.

On the other hand it is also important that the meteorologist on duty is familiar with the aspects and issues the end-user have to take into account, when deciding weather to – or not to – initiate actions on the roads. A discussion of special education of forecasters – regarding the operational aspects of road maintenance – is also included in the presentation.

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Due to local effects road surface temperatures can differ several degrees over a very short distance. In order to get more insight in the local temperature behaviour of a road and to develop safer gritting routes, Meteogroup has developed a system for route based temperature forecasting.

Our standard road model is able to create a forecast for one specific location. From infrared measurements, we know that large local differences in road surface temperature can exist on a route. Differences can be up to 5 degrees Celsius over a distance of several hundreds of meters. Based on these measurements, the idea came up to develop a system that forecasts road surface temperatures and conditions for an entire route: route based forecasting. The route is split up into sections with equal properties. For each road section a surface temperature and condition will be calculated.

The main factors that influence the road surface temperature are modelled in this forecasting system:
1. The local weather conditions: temperature, dew point, wind, precipitation, weather type, cloudiness.
2. The sky view: a very sheltered place will receive less radiation during daytime and will emit less radiation during nighttime. For a very open spot, the effects are reversed.
3. The solar view: a road section with trees on the southern side, will receive less solar radiation during daytime than a section without trees on the southern side.

The route based forecast shows, by means of a Google Maps presentation, which sections will be slippery at what time during the coming night. The final goal of this type of forecast, is to make dynamical gritting possible: a variable salt amount and a different gritting route. This will contribute to safety on the roads (colder spots will be treated earlier) and it is also financially interesting (less salt necessary and fewer kilometers to drive).

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There has been much research over the past three decades showing how road surface temperatures are influenced by a wide range of meteorological, geographical and road infrastructure parameters, and rapid advancements in the processing capabilities of computers over the past 10 years has pioneered research into new methods for better parmeterizing some of the key parameters influencing road surface temperature in road weather forecast models. This paper focuses on road construction and investigates an alternative methodology for parameterizing road construction layers in a forecast model using raw data collected from a ground penetrating radar (GPR) survey. This approach has the potential to allow high resolution modelling of road construction on a scale previously not seen within a road weather model, but the potential benefits of using such technology must be fully quantified and outweigh the costs involved if GPR surveys are ever to be used operationally to improve the parameterization of road construction within road weather forecast models.

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A slip accident at a tunnel mouth frequently occurs in winter and causes serious danger for human life and a heavy traffic jam, because the road surface condition remarkably changes inside and outside a tunnel mouth. For example, a thermal map in winter shows that the road surface temperature often falls below the freezing point near a tunnel mouth, although it keeps positive inside the tunnel. Paying attention to the shallow ground heat inside a tunnel, a Horizontal U-Tube (HUT) road heating system was introduced for the first time in Japan in order to prevent winter traffic accidents associated with road freezing at the west side mouth of Nanaori-Toge tunnel, Fukushima Prefecture 2002. A HUT is normally buried in the depth of 1.0 – 1.3 m in the ground of the central part of the tunnel. The ground heat extracted by the HUT is transported by heat-carrier fluid (HUT fluid) and is injected into the anti-freezing pavement at the tunnel mouth. Since the shallow ground heat has low energy density, a reliable HUT heat transfer model is required to predict the ground heat extracted under many combinations of HUT length and tunnel ground temperature. We developed a two dimensional heat transfer model of the HUT taken account of the sensible heat due to the HUT fluid and the heat transfer between the HUT fluid and the surrounding ground. We carried out a heat extraction experiment in a temperature controlled room. The experiment consists of a miniature HUT (diameter = 20mm, length = 2.0m), a soil box (1.20 × 0.41 × 0.31 m) and a constant temperature bath. The flow rate was ranged from 7.0×10-7 to 47.6×10-7 m3/sec.

The present paper aims at showing the validity of the model by the comparison between the computed outputs and the experimental results. In addition, the applicability of the model is also discussed in this paper. The following conclusions are drawn: The extracted ground heat is uniform in the longitudinal direction of the HUT, as long as the present flow rate is concerned; The relation between the HUT Nusselt number, Nu, and the HUT Reynolds number, Re, is given by a power function and Nu increases with Re. The extracted ground heat is also given by a power function of Re.

The experimental results allowed the proposed model to predict the ground heat extracted by means of the HUT precisely.

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There is a balance between doing to much or too little preventing salting to avoid slippery roads. Both for environmental and economical reasons it pays off to make all possible efforts to find out if salting is necessary. For these reasons an interest for road stretch forecasts has been growing recently. The requirements to make these very accurate and detailed forecasts are huge compared to traditional numerical weather forecasts which are for a fixed grid and not defined for specific points. Furthermore, the spatial resolution is still not high enough for detailed road stretch forecasting even though numerical weather prediction models have improved much, especially when it comes to spatial resolution which now is in the order of 1-5 kilometers. This is the only source of data to predict the future of the atmospheric state. However, it is possible to obtain more local details in the variation of the predicted parameters such as road surface temperature, air temperature and humidity, speed and direction of wind, and water/ice accumulated on the road. Knowing shadows from objects along the road can increase the prediction scores of road surface temperature considerably but also other local effects such as cold air pooling and reduced skyview can cause considerably local differences. One of the main tasks is to know the local conditions. This can be done by manual field observations where measurements of shadows, exact geographical position, vegetation, slop and height of terrain, long time series of temperature observations, terrain type and many other parameters can be derived. For Denmark this is about 30000 points for the main roads and it would be very time consuming to build up such database from routine measurements and always keep it updated. Instead a high resolution database for Denmark the so called Danish Elevation Model will be used to derive the required parameters. This database has a horizontal resolution of 1.6 meter and a vertical accuracy of about 20 centimeters and it exists in to versions. One versions with height of terrain and another version which includes surface objects such as trees, buildings etc. Here it will be illustrated which parameters can be derived from such high resolution databases and how these parameters can be used for road stretch forecasting.

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Few years ago Slovenian Highways were equipped with dozen Road Meteorological Stations which differentiate by age, sensors and producers. Some stations were locally integrated and some not, so at that moment road maintenance rely on relatively small amount of weather data. Finally a decision was made to integrate all of the stations into an integrated and comprehensive Road Weather Information System. The challenge was to create services to support different protocols, to create an unique data model and database and to create an web based application that support final user and all kind of road maintenance activities. In overall system consists from three main parts:
1. Collection – Services for collecting data from different weather station and converting data into appropriate form for input in central system
2. Evaluation and processing – Service for evaluating data against different bounds and alarm conditions, insertion data into central database, informing contractors about triggered alarms
3. Presentation – Web based application for reviewing data (current and history weather data, detailed previews with charts, previews of weather station equipment and its condition, system administration and tools for statistic previews and data exchange)

For the general users the most important data are shown on electronic information boards. Therefore a GeoRSS syndication was developed, which includes current weather data and alarm situation.

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It is necessary for road administrators to identify sections on individual routes where freezing is likely to occur and where intensive measures against frozen road surfaces must be taken. The need for such work is especially high at night when temperatures drop and road surface freezing tends to occur. To this end, thermal mapping is used to identify the characteristics of road surface temperature distribution on individual routes.

In Japan, thermal mapping has been used to assess road surface temperature properties and identify sections of routes prone to freezing since the technique was introduced in the early 1990s. It is known from the results of such mapping that road surface temperatures vary greatly by section even on the same route, and that the characteristics of this distribution differ widely depending on weather conditions and hour. Thermal mapping results can be divided into extreme, intermediate and damped depending on weather conditions. Past studies have indicated that these three conditions correspond roughly to G, F, E and D of the Pasquill stability classes used to categorize atmospheric stability. In this study, a map of road surface temperature distribution at night was created for each of the Pasquill stability classes (representing atmospheric stability) to enable precise prediction of road surface temperature distribution variations by weather conditions and hour. The level of prediction accuracy was verified by comparing the distribution obtained from these road surface temperature distribution maps and the results of thermal mapping conducted the previous winter.

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Changes in surface roughness around a route can have a major influence on the wind regime at the surface/air interface along a road which in turn has a local effect on road surface temperatures. Traditionally a parameter known as roughness length (Z0) is used as the primary measure of the aerodynamic roughness of a surface, but Z0 is notoriously difficult to estimate. This study takes a new approach to the estimation of Z0 for a road weather prediction model, using high resolution LIDAR data coupled with spatial processing techniques to provide estimates of Z0 which take into account both the prevailing wind direction and the height of the surface elements (buildings, trees etc…) within the upwind fetch of the forecast points. Statistical techniques are used to show how this technique produces Z0 values that differ significantly between land use cateogries and are consistent with published values for similar terrain obtained from detailed boundary layer experiments, thus giving confidence to the technique. The effects of this new Z0 parameterization on forecast road surface temperatures for a mixed urban and rural study route in Birmingham, UK are discussed

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An overview of the Road Weather Information System (RWIS-HC) project for the needs of Hrvatske ceste d.o.o. – HC (Croatian roads, Ltd) is presented in the paper. The basic features of the system architecture, components configuration, application software, as well as some machine-human interface solutions are described. Road weather monitoring is the basis for successful road traffic with respects to the weather condition that significantly affect to the safety, functionality and efficiency of road traffic. To support the work, better use of winter service resources, and to increase traffic safety in winter conditions, The network of road weather stations to measure the local weather conditions on the main road routes are installed. The proposed network covers a wide area of Lika, Podvelebit area, and the Karlovac south area (parts of state roads D1, D8, D23 and D25). Network consists of 10 main and 25 auxiliary weather stations in combination with variable message signs. All weather data are collected by the Information Center of HC and distributes to headquarters and regional winter service centers. They are the basis to present the weather conditions on some roads direction. Also RWIS-HC helps in actions planning to maintain the necessary level of traffic safety, winter services preparedness, timely clearance of snow, sprinkling by chemicals against freezing, issuing warnings and others. Due to rapidly changing technologies and associated increasing costs, implementation of RWIS-HC has been complex. Project team finds that it is important to start with an architecture that allows easy integration of new and build-in technologies, and then gradual expanding to a larger system. This project develops a flexible RWIS model that can seamlessly integrate with the existing systems and can gradually expand to a larger system. The project also focuses on new ways of utilizing the massive amount of weather sensor data collected from RWIS-HC. Traditional ways of using Road Weather Information Systems have been to forecast road icing before its formation for proactive winter-road maintenance. The algorithm of applying artificial neural networks for short-term forecast of surface temperature road is presented in the paper.

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The value and benefits of weather information in the management, planning and operating of transport systems is undeniable. But what is the true value in the light of present body of knowledge? This paper reviews about 50 relevant studies related to the question and attempts to summarise the results. It also sets the framework of generic valuation problem by introducing most applicable valuation methods and techniques. It is argued that in fact the valuation problem wells from the theory on information economics. The background for this paper is in the study where the benefits of the Finnish Meteorological Institute’s information services to the users and to the society as a whole were valued (Hautala & Leviäkangas 2007). A review of more than 100 studies was carried out covering all transport modes. More than three quarters of those studies were focusing on road management and road transport. Most of the last mentioned dealt also with winter road management. This review paper draws the summary of those studies and supplements the empirical material (i.e. the studies) with more recent findings. Four angles of benefits are identified: 1) road users; 2) road operators, service providers and contractors; 3) infrastructure managers; 4) externalities. Hence the review contributes not only to business perspectives but also to policy level decision making. The review also attempts to pick out the best examples of services, processes and operations that seem to have the greatest beneficial value in the light of past investigations, but also where some potential benefits could be gained if the technological possibilities are utilised to the maximum. In many cases also organisational and institutional factors might build thresholds to potentially value-generating services and ways of operation. A very comprehensive list of references is provided in the paper. The reviewed study material is clearly concentrating on Scandinavia, North America and Japan, not surprisingly.

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In road maintenance district Trollhättan in the midwest of Sweden there is an ongoing project about how to make the winter maintenance more energy efficient. Winter maintenance was compared to slipperiness information from the Road Weather information system – RWIS in order to isolate occasions that have the potential to be more energy efficient. Information about maintenance activities was matched against RWIS information to understand how often the maintenance is actually performed in accordance with slipperiness on the road surface. The Road Adminstration of Sweden is very tough on saving money but still the safety is the number one priority. This makes it hard for the contractor managing the maintenance district. In order for them to get payment for the work they perform the RWIS need to have detected one of several slipperiness criteria. It is hence crucial to find a method to detect occasions of maintenance which might not have been needed according to the weather. The results of this method shows that about 12 % of the maintenance occasions where performed when there were no indications of slipperiness from any of the RWIS stations in the region. The method also reveals that in 25% of the occasions there were only some of the RWIS stations in the area that were indicating slipperiness. This would also show a potential for savings in different parts of the region.

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During 2008 and 2009 The Danish Road Institute has developed a concept for an Mobile Winter Maintenance Centre for the road authorities in Copenhagen, the capital of Denmark. The mobile control centre is an addition and not a substitution of the existing Winter Maintenance Centre.

The primary purpose of the mobile control centre is to have a closed loop between measurements before winter maintenance actions (salting, snow removal, etc.), initiation of actions and control of the result of these actions. The secondary purpose is to give the operators in the Winter Maintenance Centre an opportunity to experience the situation for the drivers on the roads both in normal situations and under slippery conditions. In the long term these experiences will give a better interpretation and understanding of the measurements from the stations.

This means that the mobile winter maintenance centre must be capable of measuring the surface condition of the pavement (before and after winter maintenance actions) as well as having installations for initiating winter maintenance actions. The rolling winter control centre is capable of conducting measurements of surface friction, surface temperature and surface state (icy, wet, dry ). In the first stage DSC111 and DST111 sensors are used for these measurements. Once it has been established as a fact that winter maintenance action(s) should be initiated, the rolling control centre has communications tools that allows it to call these actions. These tools are:
1. Wireless broadband connections to the Internet
2. RoadWeather (presentation system for observations and forecasts from measuring stations)
3. Winterman (the Danish Management System for Winter services)

All measurements and actions will continuously be saved in a database as documentation. This paper describing the considerations about design of the mobile winter maintenance centre, and experiences gained during the implementation of the mobile winter maintenance centre. The paper also presents experiences from the first practical use of the mobile winter maintenance centre.

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Due to concerns over revenue and salt supply, Indiana Department of Transportation (INDOT) began statewide implementation of a Maintenance Decision Support System (MDSS) for winter 2008-09. INDOT utilized an MDSS with mobile data collection to better plan, manage, and use materials, equipment, and man hours to offset these concerns and find beter, more cost effective, technology driven ways to do business.

Approximately ten percent (10%) of INDOT’s snow fleet was outfitted with onboard mobile data collection (MDC) units and cameras. Forecasts, recommendations specific to road segments, current weather conditions, truck locations, applicaiton rates, camera images of road conditions, and driver observations were all transmitted in near real time to managers, the Traffic Management Center, radio dispatch, and others by a graphical user interface. Combining all these elements with intensive training and a shift in maintenance culture, a live big picture of the entire state’s maintenance operations was available to employees at all levels. This data, from forecasting to real results, assisted decision makes from the first call out through the final application of materials. By implementing an MDSS to aid management decisions, INDOT decreased its salt usage by over fourty percent (40%) from the previous year, overtime related to snow and ice dropped by twenty-five percent (25%), and diesel fule consumption by nearly fourteen percent (14%). These remarkable results saved over $12 million for the taxpayers of Indiana and placed INDOT as a nation leader in MDSS implementation. This innovation and extraordinary savings combined technology and maintenance personnel to form a new best practice that INDOT is continuing to use and expand.

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Roadway ice formation is a road condition which often leads to potential hazardous travel conditions for the road users. Ice that forms on the roadway due to freezing moisture is often hard to detect due to its resemblance to a wet road, thereby the term “black ice”. Ice formation is also difficult to predict due to its dependence on three factors which are each hard to predict by themselves; surface temperature, presence of water and the freezing point of the surface. The temperature and presence of water are hard to predict due to high variations in the local climate as well as uncertainty in the meteorological forecasts. The freezing point is highly dependent on earlier maintenance actions. Advanced sensors are available to the market with the purpose of describing the road surface state, including black ice. However, as the circumstances are difficult to capture, need for further development exists. The freezing of water is an exothermic reaction, which means it releases heat, thus affecting the temperature of its surrounding. During a freezing event of a road surface, a high amount of heat energy will under a short period of time be released. By closely observing the temperature of a wet surface, a freezing event can be detected without any prior knowledge of the freezing point temperature. This principle is true also for roadways, thus making it possible to detect ice formation on roads by observing the temperature development and rapid changes.
A system has been designed to detect ice formation on roads. The system uses an infrared thermometer which makes it possible to detect rapid but small changes of the road surface temperature while being non-intrusive. Non-intrusive sensors have the advantage of easier maintenance and installation and do not require the maintenance personnel to operate on the actual road. The system can warn maintenance personnel of hazardous conditions as well as the driving public by the use of active warning signs.

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Finland’s road weather information system was largely built between 1989 and 1999. At the start of the new century, the technical and operational limitations of the system were becoming increasingly evident and it was clear that an overhaul was required. The overhaul was regularly discussed in the Finnish Road Administration (Finnra) at several levels. The Finnra gradually came to the conclusion that the manner in which the different roadside devices produce information should also be carefully examined in connection with the overhaul process. Attention was also drawn to the use of outsourced services in the collection and storage of road weather information.

A preliminary study carried out in 2008 marked the start of the overhaul process. The aim of the preliminary study was to produce an overall picture of the system of roadside devices producing information and the administrative model supporting it in the coming years. The study was thus intended to provide a basis for the overhaul project planned for the next few years. The preliminary study was carried out by a consultant who was familiar with the Finnra’s road weather information and traffic management systems. Finnra experts were closely involved in the work through interviews and workshops.

The preliminary study involved the identification and description of the following 12 key elements of the system of roadside devices: Collecting information using roadside devices, collecting information using sensor vehicles, using information produced by information systems of other organisations, exchange of information for the purpose of producing numerical road weather forecasts, producing road and other weather forecasts and radar and satellite images, calculating information for variable road signs, controlling the cameras, database services, archiving information, distributing live camera images, presenting operative information, and reports and statistics.

A number of open questions remain concerning the administrative model. The model should therefore be further discussed and clarified as part of or in connection with the requirement-specification project (next stage of the overhaul process). This is because the work on the administrative model and the system entity must proceed in tandem.

Using the recommendations of the preliminary study as a basis, a project for specifying the information collection and storage requirements was launched at the start of 2009. The six-month project helped to produce an overall picture of the situation by focusing on the technical system entity and its interfaces, by outlining a supervision and administrative model for the service, by describing the life-cycle model and the introduction plan of the service, by determining service-level requirements and by charting impacts on other systems. The project also produced recommendations for the next stages of the project.

The process for specifying the requirements for calculating the information for variable road signs was launched parallel to the earlier project in spring 2009. The project involved the determination of the information-calculation system, which was done by describing the technical architecture, the database and the links with other systems, by charting operational requirements and the manner in which the calculations are carried out and by determining the configuration of the calculations. Furthermore, the amount of data and the number of users and the response time and performance requirements were charted and the stages of the service introduction and administration and maintenance during use determined.

The next stage will involve the specification of the requirements for the presentation and reporting system. It is essential to ensure that the different elements form a well-functioning entity, which works in unison even if the individual elements had been acquired and implemented as independent entities. It is clear that a model which is based on purchasing the system elements as outsourced services poses additional requirements and challenges to the adjustment work.

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Innovation processes of the ROADIDEA project are based on futures research methodologies Charrette and Futures Workshop as explained by the WFUNA (World Federation of the United Nations Associations) Millennium Project. The Millennium Project is a global participatory futures research think tank of futurists, scholars, business planners, and policy makers who work for international organisations, governments, corporations, NGOs, and universities. According to the Charrette method the innovation processes are continuous and based on digital communication methods, with two major innovation seminars of at least two-day brainstorming and evaluation sessions.

The first innovation seminar was held on 12-13 May 2008 in Prague. There were altogether 36 participants in the seminar including 3 members of the Steering Committee and the Coordinator of another INFSO project. The seminar was conducted by using various innovation methods, such as group discussion, brainstorming, pub session, Socratic Walking seminar, and deliberative evaluation with basketing (categorisation). The results include 34 fully studied ideas, of which 19 were short-listed after the evaluation. From these, 12 ideas were chosen for further work with dedicated idea teams, consisting of 6 piloting ideas, 3 modelling ideas and 3 general development ideas. All ideas are discussed through dedicated wiki-software in www.roadidea.eu.

The second seminar was held in Dubrovnik on 14-15 May 2009. The participants consisted of 24 members of the ROADIDEA consortium partners and 8 other partners who represented parallel projects, Steering Committee and one industrial designer acting as the chief evaluator. The results of energy scenarios produced by the Millennium Project Global Delphi Process in 2008 were used as alternative futures for operational environment in 2030. The futures workshop consisted of brainstorming in three groups, pub session, group discussions and two evaluation cycles. There were 13 ideas shortlisted and evaluated to find 5 best ideas as radical as possible for the future in 2030. The 5 best ideas can be further developed by using their wikis in www.roadidea.eu.

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The altitude of the motorways network extends from 200 m up to 1100 m. Every day, MeteoSwiss provides a forecast for each of the stretches of motorway according to their altitudes and their climatic region. A forecast is issued by the end of the morning and is valid the day after until 12z. For some clients, the forecast is adjusted in the evening. The final product is sent with two different formats according to the measuring system used by the road maintenance centre (Boschung and Vaisala). The old road forecast tool, which has been used for 14 years, had to be updated to be compatible with the new visualization and production tools in function at the MeteoSwiss’s weather centre. The new tool (RFT) is a Java based programme running on PC. A more accurate energy balance model is included and also a better data management. At MeteoSwiss, a central data bank (DWH) stores observations from automatic weather stations, road measuring stations and model data. The RFT tool, extracts the data from the DWH to be processed by the weather forecaster. The forecasted data are subsequently stored in the DWH. The layout of the product is completed by a product generator which is transmited to the clients via a Message Handling System (MHS). Other forecasts can be generated automatically from the forecast data stored into the DWH such as those for railways. The parameters (observed and forecasted) from the DWH can be visualized by internal and external users by a tool called Climap.

Recently, a device has been implemented to process the forecasts issued by the three weather centres and store them into the DWH; in the future, the predictions will be used by the RFT which will guarantee the coherence between various forecasts. Finally, an automatic verification of the forecast will be carried out daily.

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Accidents are one of the bad aspects of each kind of transportation system, especially the road transportation. There are always four elements interring in this disaster: human, vehicle, road and environment. Among the environmental elements the climate and related phenomena are the most effective ones. Although these phenomena are inevitable and even in some cases are out of human’s control and empowerment, we can reduce the effect of some of them to minimum level by helping road designing methods and on time attending of the road maintenance agents in the place. In this paper, first the critical threshold of Sanandaj-Hamedan road is determined as a case study and then the accidents of this road are analyzed in the cold months of the year, in order to check the effect of climatologically parameters on safety of road transportations. The obtained results of the data analysis are as follows:
1. March is the most dangerous month in Sanandaj – Hamedan road in which gales flow (with the speed of more than 10 m/s).
2. January is the most dangerous month for the visibility of less than 1000 m, frost and snowfall.
3. Among the atmospheric instabilities, most accidents have occurred under cloudy weather.
4. The most accidents occurred in March whose rate is 22.4 percent.
5. The most accidental part sheet in Sanandaj- Hamedan road is the first segment (1-5 km from Sanandaj).

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Accident risk increases during wintry weather and snowfall is often the main factor causing problems. Especially in cases when the surface temperature is below -5 oC, anti-icing with salting is not effective and the snow can be packed on the road surface by traffic causing low road surface friction and slippery driving conditions. In addition to this, dense snowfall decreases the visibility substantially. At the forecasting and awareness point of view, large scale low pressures with possible heavy snowfalls are nowadays quite well predicted by numerical weather models. So it is possible to issue a warning for bad driving conditions in advance. But local snowfalls are often more tricky to predict. They can be related to the passage of a minor trough or they can be induced by ice-free sea-areas.

The snowfall induced by open water is called sea-effect snow (or lake-effect snow). It occurs quite commonly for example in Japan and especially at the Great Lakes of North America, where huge amounts of snow can be accumulated in the downwind coastal areas by quasi-stationary snow bands. Also in Scandinavia, cold easterly flow over the Baltic Sea can cause significant convective snowfall at the Swedish east coast, bringing trouble to the traffic.

In Finland sea-effect snowfall can occur at the west coast in a northwesterly flow over the Gulf of Bothnia or at the south coast in a cold air advection from southeast over the Gulf of Finland. Usually the accumulated snow amounts are not very big, but for example in the Helsinki metropolitan area, where the traffic is quite heavy, sea-effect snowfall during cold and basically dry conditions can be a surprising event.

In this study two sea-effect snowfall cases were investigated. They occurred on 20 January 2006 and 8 February 2007 and both days were winter’s peak days for traffic accidents in southern Finland. Due to the statistics of Finnish Motor Insurers’ Centre, the number of crashed cars was 371 in the first case and 219 in the latter case in the Helsinki metropolitan area and surroundings (Uusimaa County). Compared to this, the average daily number of crashed cars in the same area was 79 both during winter 2005/06 and winter 2006/07 (November – March). In both cases the sea-effect snowfall occurred in very cold conditions, air temperature being -10-20 oC. The Gulf of Finland was still partly ice-free, so heat and moisture was transferred into the atmosphere from the sea surface, causing cloud formation and snowfall, which hit the coast in a southeasterly air stream.

Sea-effect snowfall is challenging to predict in advance although high resolution numerical weather models can nowadays predict a portion of these snowfalls. Radar and satellite images play a major role in observing these phenomena. A general rule for the occurrence of sea-effect snowfall is that the air temperature at 850 hPa pressure level (at ca. 1.5 km height) should be at least 13 oC lower than the surface (water) temperature. This instability allows convective cloud formation and the resulting cloud bands are typically oriented along the steering wind in the lower troposphere (0-3 km height).

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Two damaging storms affected southern Finland in November 2008. The storm on 10 November caused up to 29 m/s wind gusts over land areas. On 23 November the measured wind gusts reached 27 m/s, but it was accompanied with heavy snowfall, up to 30 cm in 24 hours. These cases offered a chance to study wind storm impacts on Finnish society, but also to compare the storm impacts with and without heavy snowfall. For both cases, we studied the Rescue Services rescue operations, the Finnish Motor Insurers´ Centres statistics of paid compensations and media reports, and compared them to the observed weather. Road weather observations were also analyzed in both cases. To develop preparedness for future storms, we derived wind storm and blizzard general impact descriptions for Finland. Finally, in co-operation with the Emergency Services College, we established localized call-to-action statements and general guidance that can be distributed to public within storm warning messages.

The number of households affected by power failures was estimated to be over 70 000 during the 10 November storm, and at least 41 000 households during the blizzard. The building damage was similar in both cases and included mostly detached roofs and failing scaffoldings. Several people were trapped in elevators during power failures.

During the 10 November storm, the total number of Rescue Services rescue operations was four times normal figures as the storm resulted in 801 rescue operations. In comparison, the 23 November blizzard induced 534 weather related rescue operations resulting in a doubling of the typical total number of operations in the affected area. In both cases 54% of the weather related rescue operations were falling trees on roads. While the 10 November case yielded six traffic accidents of a car crashing into a tree blocking the road, the 23 November blizzard had 20 such cases. In the blizzard case, the number of Rescue Service reported traffic accidents was high, 145 cases, in which altogether 54 persons were injured and one person died. In comparison, during the 10 November case, Rescue Services reported 6 injured persons during the event.

Snowfall is the dominant factor causing slippery road conditions. On 23 November the heavy snowfall caused a rapid decrease in road surface friction. Based on Road Weather Information System data in the Helsinki metropolitan area, the friction values dropped from ca. 0.8 to 0.2 or less and stayed at this level for almost 12 hours in spite of maintenance actions. In addition to the low road surface friction, the visibility was very poor and also the strong wind had a negative impact on driving conditions, likely increasing the number of traffic accidents.

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Road surface conditions in winter are a main cause for accidents and traffic jams, which apart from causing human distress also lead to high economic costs. The collection of reliable data directly from the street in order to support maintenance decision process of the winter road service is a substantial contribution for improving safety and mobility.

With regards to modern road weather information systems RWIS it is important to be able to rely on a technological approach that combines easy installation, reliability, accuracy and easy service with low live time cost so that the necessary coverage of the measurement grid is ensured.

There are innovative measurement procedures, like the measurement of the different kinds of precipitation as well as the intensity via a radar-doppler method, which is operating in the microwave-range. This method avoids the disadvantages of optical or mechanical methods, which are otherwise currently used. Microwave-radar methods are also being used when measuring the coverage of the street surface and are reaching accuracies which have not been obtained with the use of other methods.

Apart from the decisive progress of the measurement procedure itself, all relevant atmospheric measurement parameters (air temperature, dewpoint, precipitation, wind and pressure) have now been integrated in one compact and intelligent measurement unit. The same applies to road sensor which is built into the road surface or instruments for non invasive surface detection. That is how it is possible to construct a complete road weather station out of only 2 sensor devices. The intelligent measurement instruments supply all measured data via a digital (serial) standard interface in the required measurement units, which in turn do not need further link processing or interpretation. Through this open standard data communication this new compact and smart technology can be integrated independently from the manufacturer into applications like MDSS or ITS as well as it can be integrated into various systems architectures.

On top of that there are compact energy saving communication modules, which are connected via a field bus and which support all common international data protocols (TLS, NTCIP, XML etc..) and which can be joined via various communication media such as (LAN, WAN, GPRS, UMTS, GSM etc.). The article describes the basic concepts of intelligent measurement procedures and the main technological progress in the area of modern road and weather data collection.

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The Meteorological Service of Canada (MSC) at Environment Canada generates and stores a large volume of environmental information everyday. Predefined and widely available products such as public weather forecast bulletins, warning bulletins, and various numerical weather charts are on the Weatheroffice and Datamart websites and are used as our means to communicate a small subset of this information. Aside from these products, the Canadian public currently cannot easily access the majority of the data that is generated by MSC.

With recent technological advances, a more useful method to access MSC data is now being developed. Through the use of an interactive web map interface, currently named Meteo4U, the public will have the ability to choose the products they want and the ability to download data used to generate these products. Users of Meteo4U will be able to display a range of meteorological forecast data (e.g. temperature, wind speed, precipitation) by interactively defining: 1. a geographic location, 2. a travel route, or 3. an area on a map.
This project is one of the first official initiatives to use modern geospatial technologies to interactively share, distribute and disseminate meteorological data at the MSC.

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The meteorological field value embodied by the grid can’t be aligned with the geographical object precisely in spatial, when overlapped with GIS data for querying the value of meteorological element by gird-cell, and the spatial uncertainty is arose by the surveying method of the Near Estimated Value (NEV) and the Quartering Grid (QG). So how to control the uncertainty caused by overlapping is the key to develop meteorological GIS application. The paper adopt the Cross Entroyp (CE), the Mean Square Error (MSE), and the Root Mean Square Error (RMSE) to measure the uncertainty. It can be demonstrated that the NEV only meet the requirement of the application under higher spatial resolution such as the 1KM grid-size, but can’t do so well in low resolution, however, the co-grid QG take the interpolation to promote the measurement accuracy, its uncertainty is less than that of the other method. The QG constitutively meet the meteorological GIS application when overlapped the meteorological field value in GIS.

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Road transport allows our society’s goods and persons to circulate. Preserving traffic fluidity is very important for the well being of the economy and social activities. The arrival of Intelligent Transport Systems (ITS) in several sectors gives tools to better manage information. This will impact designs of some vehicle or infrastructure components and our way of driving. Transport Canada asked the University of Sherbrooke to discover the latest state of the art technologies, to look into new opportunities of research, and to assess all research topics in the particular field of Vehicle Infrastructure Cooperation (VIC) within its application with mobile Road Weather Information Systems (RWIS). We know that several road organisations have begun projects to improve road maintenance. Their needs are related to every aspect of information treatment process:
sensors design;
items for data collection;
communication networks;
ways of building and exploring databases;
decision support systems conception;
management support systems conception;
way of sending information to the road users.

The University of Sherbrooke has started up an expertise centre studying exclusively on the applicability of VIC on mobile RWIS. Discoveries could accelerate in the short term improvements for road maintenance managers, and in the longer term for road users.
Several departments of the University have been involved:
Electrical and computer engineering;
Computer sciences;
Applied geomatics;
Business management.

At this time, specific projects are in relation with the conception of an intelligent vehicle for road measurements, methods for data mining, the use of communication technology P25, camera images processing for surface state detection and optimal camera images compression. Links are made to include other researchers from other universities in Canada and the rest of the world. In the process of finding research topics, the expertise centre’s staff has always verified discoveries’ potential applicability.

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A 4-year joint research program has been initiated in 2009 between the Laboratoire Central des Ponts et Chaussées (LCPC), some Laboratoires Régionaux des Ponts et Chaussées (LRPC) and Météo France. The main idea is to evaluate some tools, techniques and solutions that could be used to improve the safety of road users, and to help road managers in poor road weather conditions.

Tools and techniques are covering a very large scope of scientific fields, from image analysis, characterisation of weather phenomena such as rain or fog. Some work has been started on the use of cameras to appreciate the visibility in fog situations. Images analysis of the road as seen from a windshield in many weather situations are treated to build visibility thresholds. The pavements textures are analysed to determine their impacts both for the grip and the induced visibility with water sprayed by vehicles tires. Some of these tools are currently deployed on experimental sites all round the French territory.

Furthermore, numerical tools are currently used to obtain conventional forecast on road surface temperature and surface status. They are either based on usual weather forecasts and observations, and the energy balance analysis. In some cases some statistical and stochastic tools are implemented when data is available and meets quality requirements. A comparison between these tools is currently undertaken to establish their limits, and improve their outputs. As an example, the de-icers incidence will be included in the numerical model, along with some thermal mapping aspects.

All these aspects are developed to evaluate and to quantify hazards that could occur on roads in adverse weather conditions. These hazards are usually identified on specific spots. But the main difficulty is the extension to a whole itinerary. Météo France recently developed OPTIMA, a French Road weather information system (RWIS). It is a precise decision-making tool for anticipation and real-time follow-up of meteorological situations on 1 km road stretch sections. It also provides, every 5 minutes, forecast for the coming hour (time steps of 5 or 10 minutes are available), over the 120 000 km of the French major road network. One of the objective is to implement within OPTIMA the results of the research work, such as grip and visibility alerts so as to inform both road users and managers, and so improve traffic. This would be possible through data transfer and meta-data relative to the road network within the tool.

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One of the most important inputs into a winter road maintenance decision support system is an accurate weather forecast. A common source of weather forecasts is raw model data which has limitations in forecast accuracy when compared to weather forecasts edited by a meteorologist. Combining the knowledge a meteorologist with an intuitive system to edit a weather forecast has outperformed what weather models alone can achieve. This presentation identifies key limitations of models in precipitation and pavement temperature forecasting. It then explains methods that have been proven to overcome these limitations to significantly improve forecast accuracy over what weather models alone can provide. One of the key methods is the use of observational data, including RWIS data, to enhance forecast accuracy. Quantitative analysis of these methods will be provided, and the practical benefits for road maintenance organizations will be brought out as well.

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