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Optima (road weather informations dedicated to road sections)
Optima is a global approach of data fusion and specific road weather algorithms implementation to obtain the best road weather information, according to the state of art, for the whole french road network. Thus, Optima is a tool for road managers to anticipate and follow in real time the meteorological situation on their road sections, especially when dangerous phenomena are concerning their roads. Optima supplies real time meteorological qualifications for road sections (five kilometers resolution) of France between H and H+1h (about 250 000 km) at high temporal frequency (5'). The forecasted parameters inform on the weather conditions along the roads as well as on the state of the roadway.
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Modi-Roma, a complete overview of an entire road network
The concept is to combine and process available data from fixed measuring field stations and floating cars. The combination of different sources of data gives a novel opportunity for efficient monitoring and detection of variations in pavement and road conditions. This approach enables development of maintenance tools for road conditions during various times of a year and various traffic conditions. Data from the CAN-BUS within the car are applicable as a quantitative basis for signal processing and analysis. The following road parameters can be estimated and further studied:
• Quality of the road condition
• Strength of road bed
• Need for winter maintenance
In order to demonstrate its applicability, a Graphical User Interface will be developed, and a pilot will be demonstrated in a test area in Sweden. The results will be evaluated carefully, and cost/benefit analysis made in order to assess the feasibility and benefits of these new methods in road maintenance. Road condition and performance assessment technique which offers new effective tools for monitoring and assessing maintenance needs across Europe. The need for road maintenance, concerning winter roads, pavement condition or other road specific parameters is today decided based on either manually performed observations or collected data using sophisticated measurement devices. Specially designed vehicles are used for some surveillance and others are based on detailed measurements using specially designed instruments. The available technique today may be sufficient but costly for collecting data needed for decision making of different kind. Moreover, data are collected only for predefined road network or section. MOBI-ROMA involves a new concept for efficient and cost effective data collection using mobile technique.
The objective of this research is to introduce a new tool for cost effective road management. The approach is to combine and process available data from fixed measuring field stations and floating cars. The combination of different sources of data gives a novel opportunity for efficient monitoring and detection of variations in surface condition both before and after different maintenance works. This approach enables development of maintenance tools for road conditions during various times of a year and various traffic conditions.
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BiFi - Bearing information through vehicle intelligence
The BiFi project "Bearing information through vehicle intelligence" is an ongoing project in Sweden with the aim of studying if it is possible to map the load/bearing strength of roads by a vehicle-based method. The basic idea is that through combining the vehicle data with weather observations and forecasted weather data it is possible to model and forecast the road status according to bearing strength. The results from the project so fare are based on field tests in a rough and real environment for determining the load-bearing strength of the roads.
Roads with a high load-bearing capacity are essential for harvesting natural resources in and to help keep the countryside open and prosperous. During periods in the spring when the ground frost thaws the load bearing capacity of the forest roads is greatly reduced, leading to road closure. Subsequently it is not possible to use the roads for transporting heavy goods such as lumber. In order to decrease the costly consequences of road closure the lumber industry needs to build up large stocks and to plan their transport in such a way that secondary stocks can be used. It has been calculated by that these measures cost the industry an extra 650 million a year only in Sweden. And therefore there is a need for a tool for judging the load-bearing capacity of the road network in a detailed and dynamic way would considerably help to change the current strategy and possibly save the industry significant amounts.
The results from the BiFi-project have so far been very successful. The technology to use vehicles to detect the bearing strength of gravel roads has been found very promising. In part 1 of the BiFi-project an algorithm has been developed based on collected real-time data from a vehicle's standard sensors. Through data analysis, a method of determining the load bearing capacity of the roads that were driven on with cars was established. To test the algorithm and model - extensive field trials have been carried out together with reference measurements. Using the well proven method based on DCP- Dynamic Cone Penetrometer a comprehensive set of reference data was established. This method was also complemented by measurements using a FWD - falling weight deflectometer. A conclusion from this was that the FWD as a method is not very useful during the thawing period since high water content in the road bed gives rise to errors for the FWD. To ensure the quality from the cars additional sensors were used by reference accelerometers that were fitted to the vehicle in order to give an indication of the quality of the vehicle's own accelerometer data.
Using the information available within modern cars and data from RWIS – road weather information systems makes it possible to find solutions for detection of different kinds of maintenance needs. BiFi and two more models models are presented – SRIS and SSWM.
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EN15518 - a European Standard for Road Weather Information Systems (RWIS)
Road Weather Information Systems are widely spread over the world. The acquisition of measurements and information about pavement status and weather conditions has become a strong decision-making basis for the road maintenance teams in their daily efforts to keep the roads safe during winter conditions. However, the signification of measured parameters, the accuracy with which they are acquired and transmitted may vary greatly from one system to another. The European Committee for Standardization has decided to establish a clear terminology and definition of road maintenance related activities and equipments. Amongst the various standardization activities, a Project Work Group has been tasked to write a standard about Road Weather Information Systems. This document presents the current state of work of this group.
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A study to determine the effects of employing a well maintained RWIS network on accident rates on major highways in the US state of Idaho
Maximizing roadway safety, mobility and efficiency, while minimizing maintenance-related costs, is the common goal of winter maintenance engineers around the globe. Road Weather Information Systems (RWIS) have proven track records in enabling road authorities to meet the aforementioned goals. However poor system maintenance, often due to the lack of reliable budgets, has for a number of authorities led to poor reliance and trust in their systems.
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Monitoring slipperiness on walkways
Finnish Meteorological Institute (FMI) has developed a pedestrian pavement condition model which predicts the level of slipperiness from pedestrians' perspective. The lack of slipperiness observations is a big problem when developing the model and forecasting the expected pavement condition. There are about 500 road weather stations installed along the Finnish road network but those stations are serving car traffic only. The most slippery cases are typically different between roadways and walkways.
FMI has tested Vaisala DSC111 instrument and the usability of the instrument to monitor the slipperiness on the walkways. The instrument is measuring the thickness of water/snow/ice on the surface and estimating the prevailing friction, too. FMI has installed two Vaisala DSC111 instrument to monitor the slipperiness on walkways in Helsinki and the results are presented in this study.
Another device measuring the slipperiness on the walkways is a slipmeter developed by the Finnish Institute of Occupational Health (FIOH). The slipmeter is a mechanical in situ simulator measuring the grip between the pavement and shoe sole. Shoes with different soles can be tested and compared with the device.
The usability of the Vaisala DSC111 sensors from viewpoint of pavement slipperiness is presented in this study. The instrument seems to measure slipperiness pretty well when there is a thin layer of ice or snow on the surface. But in case of much snow or ice on the ground the device is giving continuously very low values for friction without revealing the most slippery cases.
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Further development of RWIS and it's new potential marketplaces in Latvia
In the past, development of road weather information system (RWIS) mainly reflected in gradual expansion of a network of environmemtal sensing stations (ESS) along the main roads in Latvia. Since a concept of common platform of Intelligent Transportation System (ITS) became mature and has a Pan European framework, RWIS as its integral part, got opportunities for more effective usage, but at the same time, new challenges of data accessibility and interoperability occurred. The presentation deals with ongoing projects and future vision about: how existing ESS stations should be built up to a multifunctional road, traffic and environment control points; how RWIS of different authorities and other data sources should be integrated; how to cover road network (including secondary routes) with low-cost sensors and provide proper decision-making tool for antiicing strategy and overall road management; how RWIS data can be used wider and processed for new marketplaces in Latvia.
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Friction meter comparison study 2011
This document describes the tests conducted with various friction meters as well as the results obtained. The friction meter types used were the "traditional friction meter used while braking", friction meters with an internal acceleration sensor, optical friction meters and the mechanical friction meter. The differences in reliability and accuracy between the different types of meters and between different meter manufacturers were much greater than expected.
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Towards real-time skid resistance forecast
For any given road layout or curvature skid resistance as property of pavement surface and tyre is a decisive factor limiting the speed threshold for safe driving. Skid resistance is no fixed value and is significantly lowered in case of precipitation depending on form and rate. A further influence factor in such cases is the macro texture of pavements due to their capacity to hold precipitation while maintaining contact with the tyre. Snow and ice on roads are the main factors for lowered skid resistance resulting in elevated road accident levels and economic costs. With new methodologies in weather forecasting and RWIS – sensors better winter forecasting models for winter maintenance are already available. These current models consider different road materials mainly in order to specify parameters for solar flux absorption in energy budget calculations for real time road surface temperature estimation. The main emphasis of the paper lies in a determination of the impact of macro texture and amount of snow and ice on skid resistance of flexible and rigid pavements. Based on extensive field and laboratory research a connection between different texture and skid resistance determination methods could be established. The field tests consisted of film thickness and skid resistance measures among others for different weather situations. In the complementing laboratory tests the road surface texture was determined using high precision laser scanning on sample cores as well as other test methods. Based on the findings a real time skid resistance prediction for an entire road network becomes feasible.
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Development of mobile optical remote road condition monitoring in Finland
Mobile observing methods of wintertime road conditions have been developed in Finland for the past six years. Key results from several measurement projects are presented, showing the local variability of snow, ice, water, temperature and slipperiness on the road surface in great detail. In addition, it is discussed how mobile and fixed road weather observations and highresolution road condition models are combined in an optimal way for snow removal and slipperiness control.
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Remote monitoring of ice formation over a runway surface
The reliable detection of ice formation over a runway surface is an important issue for reducing maintenance costs and improving traffic safety. An innovative sensor was developed to detect the presence of ice on its surface, and its repeatability, stability and reliability were assessed in simulations and experiments described in previous papers. Three sensors were embedded, far from each other, in the 4 km long runway of the Turin-Caselle airport to check the state of its surface. Each sensor was equipped with a GPRS modem to send the collected data to a common database. The GPRS based acquisition system was installed more than one year ago showing correct working and automatic reactivation after malfunctions without any external help.
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Tailored and on-time winter weather information for road traffic management
Recent developments are reported on techniques to determine the onset, duration, amount and type of precipitation as well as the snow and icing conditions at the surface. The algorithms, still under development, will be used to forecast the weather in short to medium lead times, i.e. for the next 30 minutes up to a few hours ("nowcasting"). An algorithm aims at detecting potential areas of snow fall by combining reflectivity data of precipitation and surface temperature data from a numerical model as well as surface stations in high spatial resolution. Another approach combines profiling measurements (e.g., meteo data measured by aircraft and polarimetric radar data) with numerical weather forecast products.
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Probabilistic road weather forecasting
The last 30-40 years has seen a step-change in the provision of forecasts to the winter road maintenance sector. The days of simple 'road danger warnings' are now a distant memory and highway engineers have had access to detailed site specific forecasts for over 30 years. More recently, the advent of route based forecasting has added increased detail and sophistication to the approach and further helps to strike the balance between securing winter roads and optimising salt usage. Any improvement to road weather forecasting has ultimately been driven by computational resources. As a result, road weather forecasts have historically been deterministic in nature. However, the potential now exists, as with other sectors, to potentially embrace probabilistic forecasting. All forecasts needs to be accompanied by credibility intervals of some sort to indicate the level of uncertainty and road weather forecasts are no different. The use of a Bayesian framework incorporating an emulator of an existing numerical model is one such means by which this can be achieved and could potentially bring greater confidence to decision making – providing the sector requires and will use such information. Furthermore, the superior computational speed of an emulator will allow models to be run for a greater time period into the future, possibly years.
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A road map towards implementing a probabilistic road weather information system
The German National Weather Service runs a dedicated forecast system for providing detailed information on the road conditions in Germany. This road condition and weather information system, called SWIS (Straßenzustands- und Wetter-Informations-System), was set up about 20 years ago with the objective to support the efficient management of the German road network, especially during winter time. It aims to enable decision-makers to optimize their planning and distribute their resources in the most cost-effective way. Considering the inevitable uncertainties of weather forecasts, and recognizing the potential benefits of incorporating reliable predictions of these uncertainties in the decision-making process, we are in the process of extending our forecast system to incorporate such probabilistic information. Whilst the technical implementation of such a system seems to be a relatively straightforward process, it is expected that the transfer of the probabilistic information into the decision-making processes of the users might pose the greater challenge. This contribution will outline the road map and first user feedback on the implementation of the probabilistic component in SWIS.
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Multivariate data analysis. A new insight for thermal mapping
Thermal mapping has been implemented since the late eighties to determine road ice susceptibility. It consists in measuring road pavement temperature along with some other atmospheric parameters to build a susceptibility indicator to ice occurrence. Measurements are time-consuming. They had to be conducted in some specific climatic conditions during winter. Once these measurements obtained, data is processed to build a risk of ice occurrence, or to use some physical numerical models to build a forecast for pavement surface temperature. The objective of this work was to investigate a statistical approach for thermal mapping. Based on multivariate data analysis, it will forecast surface pavement temperature, and covering a wide panel of weather situations and temperature ranges. Data analysis was done through principal components analysis (PCA). PCA provided a good forecast of pavement surface temperature, covering a range between 5°C and -12°C. A validation was conducted using the large amount of measurements. Investigations indicated that few measurements on the itinerary were necessary to build a proper forecast. Some validations were conducted, and indicated possible junctions and overlaps of data for stretches monitored at different moments.
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An overview of road surface conditions forecasting in Météo-France
During winter, ice or snow presence on the roads might have serious consequences on road traffic and security. So Météo-France had developed decision-making tools for road management in winter using a specific model, ISBA-Route, which permits to simulate the behaviour (temperature, liquid and ice content) within the road and the natural soil under the influence of atmospheric conditions. To improve this model, it was coupled with the detailed snow model CROCUS in aim to simulate the behaviour of a snow layer deposited on a road. These models are the basis of the road surface condition forecasting and they are briefly presented in the first part of the manuscript. Then, in the second part, all the operational road prediction products are presented with associated results. Among these products, a focus is given on the AROME/ISBA-Route system, resulting from the coupling of the ISBA-Route model with the high-resolution atmospheric AROME model, which is the major forecast product of Météo-France. This coupling permitted to improve the accuracy of the forecast. So, at this step, to continue to progress in accuracy and to deliver a complete and coherent information. Météo-France plans to use the human forecasters expertise. These researches in road meteorology are presented in the last part of the paper, and will lead to an improvement of the quality of road conditions prediction during future years.
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Integrating METRo into a winter maintenance weather forecast system covering Finland, Sweden and Russia
Foreca has provided winter maintenance weather forecasts for several clients in Finland, Sweden and Russia for over ten years. METRo is a road forecast software distributed under the GPL license by Environment Canada. In 2010 Foreca started integrating the METRo model into its systems with the aim of improving road temperature and road condition forecasts. We describe how the METRo model was integrated into the work flow of duty meteorologists and how the model was modified to cope with the lack of subsurface temperatures in observations.
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METRo model mesting at Slovenian road weather stations and suggestions for further improvements
Efficient road system has become more important than ever. Reliable road weather forecasts (road surface temperature and condition) are crucial for winter maintenance service to establish efficient anti-icing strategy. The most common approach to forecasting the road conditions is the energy-balance model based on a one-dimensional diffusion equation. METRo is one of the widely used models for road weather prediction. Together with the input of atmospheric forecasts and observations from a Road Weather Station (RWS), METRo produces local road forecast (roadcast).
CGS plus d.o.o. is the leading company for Road Weather Information Systems in Slovenia. The company has developed the Road Weather Information System for DARS (Motorway Company in the Republic of Slovenia) and upgraded the system with the METRo roadcast in winter 2011/2012.
The main objective of this paper is to present the tests of the METRo model and results of its implementation on several Slovenian RWSs in the winter time 2011-2012, focused primary on the road surface temperature. Beside the RWSs data, short-term weather forecasts of good temporal and spatial resolution from the INCA/ALADIN meteorological systems are used. The results show that the RMS error for the road surface temperature predictions is generally satisfactory but can be too high at some sites, especially for the predictions around noon. To solve this problem, some suggestions for further improvements are given in this paper.
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Influence of the pavement type on the road surface temperature
The Danish road authorities have experimented with a new type of asphalt socalled "drainage asphalt". This type of pavement can reduce the noise generated by vehicles. However, it might have different effects on the road surface temperature compared to other types of asphalts. It has therefore been decided to examine how such asphalt affects the surface temperature. It will be studied if the applied road conditions model needs modifications to adapt or improve forecasting skills for this specific type of asphalt. This experiment is done during the road winter season 2011 /2012 and preliminary results are presented. In this study, the focus is on different type of asphalt and how this impacts on the road surface temperature and forecast quality of road surface temperature.
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Experiences of mobile road condition monitoring
A new approach to monitor road surface conditions primarily for winter maintenance purposes has been developed and tested. The sensor is called Road Condition Monitor RCM411 and it is based on optical detection of surface condition by near infrared spectroscopy. A model is used to estimate coefficient of friction starting from the measured surface condition. The design utilizes only solid state electrical and optical components enabling long lifetime in mobile use. The sensor can be installed easily within a few seconds to a passenger vehicle equipped with a ball joint type of a trailer coupling and an electricity outlet. The data is transmitted by a Bluetooth connection to a cell phone near the driver and communicated further to a remote server to be displayed, e.g. on a map interface, or for any other use. The performance of RCM411 was tested during the winter season 2010-2011. The sensor distinguishes all important surface conditions fairly reliably. The measurement range of water layer is from zero to three millimetres with a resolution limit of about 30 µm. The model to estimate coefficient of friction has been tuned to correspond to an accelerometer based friction meter µTEC. This enables verification of absolute friction whenever there is need for accurate values. Standard deviation of the difference in the friction readings of the two sensors is typically on the order of 0.10 units in most wintery surface conditions.
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Use a route based forecast for dynamic gritting
Measurements show large local differences in road surface temperatures along a road, sometimes more than 5C. These large temperature differences are due to local environmental factors. During clear and calm nights when the average road surface temperature is around 0C some parts of a road are safe while others are critical. With a high quality route based forecast system, these parts can be distinguished. This information can be used to make the gritting process more efficient.
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Relation of road surface friction and salt concentration
We have studied the relation of the apparent salt concentration and coefficient of friction between a paved road surface and a vehicle tire as a function of temperature. The study was conducted by occasional friction measurements and by collecting surface samples under various surface conditions. The main result of the research reveals that astonishingly low concentrations of salt are needed to keep friction at acceptable levels even at the lowest temperatures. With Sodium Chloride (NaCl) the limiting concentration is only about three percent at temperatures down to -20 °C. This result may have dramatic consequences to winter maintenance operations. Instead of controlling salt amount to melt a given amount of ice or snow we should control how much salt is needed to prevent surface to become too slippery and use only a fraction of salt as compared to previous practises. Although our result is obtained in a region where studded tires are used, fundamentally the results should be valid with all types of tires with some adjustment of salt concentration to higher readings.
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Verification of friction index prediction
Friction on road and runway surface plays a very important role in the safety of road traffic and aircraft taking-off and landing. Loss of friction or skid resistance is closely related to incidents or accidents on the road and at the airport, especially in winter conditions when the surfaces are covered by ice, frost, snow or water. In theory, the friction coefficient (µ) is defined as the ratio of the resistive forces (Fr), a result of the movement between two opposing object surfaces, to the normal force (N) exerted on the surface. In reality, however, the coefficient is complicated and is affected by many factors, such as: tyre pressure, rubber composition, tyre tread configuration, pavement surface material composition, pavement micro- and macro-texture, pavement surface temperature and surface state etc. Such complexity was dealt with in this paper by introducing the concepts of surface texture and friction index (0.0-1.0).
In this paper, we present a numerical road weather and friction index forecast model and results of verification of that model. The data for verification was obtained from Helsinki International Airport. The model was run up to 3 hours ahead, in hourly automatic modes with forecasting output at 10-min intervals. Verification of the model considered both the meteorological variables (pavement surface temperature, water thickness on the surface, air temperature, dew point etc) and the friction index. The comparison between forecasts and measurements, with thousands of samples, showed that both bias and RMS errors of the meteorological variables and friction index forecasts were suitably small; with the overall friction forecasting bias being slightly negative and the RMS error falls in the range of 0.24 to 0.26 for the nowcasts of FI 1 to 3 hours ahead.
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Weather service chain analysis (WSCA) - An approach for appraisal of the social-economic benefits of
improvements in weather services
The so-called Cost-Loss approach for the assessment of the economic benefits of weather forecasts is less suitable for assessing the benefits for user groups among which uncertainty about the uptake and utilization of the information is significant. Most road users can be counted to these groups. Assessment of the benefits of (improvements in) weather services for road users faces several attribution problems which may entail both over- and underestimation of these benefits. In FMI an approach has been developed which aims to account for these uncertainties by means of decomposition of the information flow ranging from forecast generation to benefit realization, i.e. so-called weather service chain analysis (WCSA). This approach can be used both in a quantitative and in a qualitative fashion. The qualitative version is meant to support information management and to identify improvement options in each section of the weather service chain. In the quantitative version the product sum of ratings per step (compared to the maximum score) is established. The quantitative version helps to identify those segments of the chain for which improvements will have the highest social-economic pay-off. It also helps to identify actions that raise the leverage of investments in weather forecast improvement. The WSCA method can be embedded in the economic modelling of particular sectors, such as (road) transport. WSCA also incites to develop regular surveying of user groups and weather service use. The paper will discuss the principles of the method and its links to economic models, as well as show a few applications for road transport.
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Decision support system for variable speed regulation
Recommending appropriate speed limits for roads is important for road authorities in order to increase traffic safety. Nowadays, these speed limits can be given more dynamically, with digital speed regulation signs. The challenge here is input from the environment, in combination with probabilities for certain events. In this paper, we present a decision support model based on a Dynamic Bayesian Network (DBN). The purpose of this model is to predict suitable velocities on the basis of weather data and knowledge about traffic density as well as road maintenance activities. The DBN principle of using uncertainty for the involved variables gives a possibility to model the real conditions. This model shows that it is possible to develop automated decision support systems that take uncertainties in account, for variable speed regulation.
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Road weather forecasts and MDSS in Slovakia
The system of meteorological forecasts, developed for the purpose of winter road maintenance and operated in Slovakia is described in this article. The system consists of several interconnected modules. The basis for preparing weather predictions is the numerical model ALADIN, developed in international cooperation and operated directly at the Slovak Hydrometeorological Institute. Output data from the model, particularly useful for general forecasts are further specified by INCA nowcasting and METRo road forecast modules. The combination of local measurements (professional and road weather stations) and weather forecasts allows now detailed predictions and alerts for each Road Weather Segment (road section with similar microclimatic conditions), that means differentiated danger levels of road conditions are shown on the dynamic map for a given lead time. The collection, combination and visualisation of all the information of this Maintenance Decision Support System give the operator the possibility to make decisions efficiently and to dynamically manage road maintenance and thus he is able to increase the road safety and improve the traffic flow. Therefore the Slovakian Road Authorities decided to implement a Maintenance Decision Support System (MDSS) for the whole Slovakia.
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Road Weather Information System (RWIS) &
Maintenance Decision Support System (MDSS)
Economic Value Tool (EVT)
Maximizing roadway safety, mobility and efficiency, while minimizing maintenance-related costs and reducing environmental impacts, is the common goal of winter maintenance engineers around the globe. Road Weather Information Systems (RWIS) and Maintenance Decision Support Systems (MDSS) (or Road Decision Support Systems) have proven track records in enabling road authorities to meet the aforementioned goals. However there are many more benefits that these systems provide both in economic terms and other less tangible benefits that have largely been overlooked by previous studies. This paper describes a tool whereby local authorities can plug in their known operational and economic parameters to instantly calculate potential benefits of RWIS and Road Decision Support Systems.
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Maintenance Decision Support System (MDSS) ASFINAG / Austria –
Experience of a comprehensive winter maintenance management system
Major goals of road operations are safety, environmental protection, economics and the necessary optimisation of these issues in delivering quality winter maintenance services. For increasing road safety and optimizing traffic flow the needs are high quality prediction and sensor technologies as well as appropriate winter service treatments at the right time. Because of the complexness of meteorological, traffic and winter service processes, winter service needs a comprehensive Winter Maintenance Management System.
The collection, combination and visualisation of all this information of the Maintenance Decision Support System give the operator the possibility to make decisions efficiently and to manage dynamically, which increase road safety and improve traffic flow. The ASFiNAG (Autobahnen- und Schnellstraßen-Finanzierungs-Aktiengesellschaft) is a 100% subsidiary of the Austrian federal government. The ASFiNAG plans, finances, constructs and operates the complete Austrian highway and expressway network. This represents a total length of over 2.100 km. In order to take benefit from synergies, and to reach common, high quality standards - particularly in winter maintenance, the ASFiNAG has decided to launch a specific, company wide project: the MDSS ASFiNAG.
The MDSS system is a common, robust, modular and scalable platform able to support the decisions of all four maintenance companies of the ASFiNAG. It is available internally or externally over a standard web browser. The objectives of the MDSS system are:
1. RWIS Data concentration
2. Integration of weather forecast
3. Combination of RWIS and forecast data
4. Integration of winter service vehicle data
The MDSS system brings together all Road Weather Information Systems (RWIS) distributed over the road network. This ensures a common interface to all users, who will have the opportunity to see data from neighbouring regions. The Austrian road network has been divided in about 215 Road Weather Segments corresponding to micro-climatic regions. Specific weather forecast up to 72 hours will be established for each of these segments.
One of the main benefits of the MDSS system is the combination of local nowcasting data of RWIS with weather forecast of the corresponding Road Weather Segment. The objective is to refine the weather forecast with regards to actual weather conditions on site, and define specific road weather dangers for the upcoming hours. This operation provides the user with an accurate picture of the future pavement status and road conditions, thus allowing him to decide on the best maintenance strategy. Additionally with integrating maintenance vehicle data on the same screen it gives the responsible persons the possibility to combine and control actual situation, predicted situation and measurement activities.
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Area forecast model for winter road maintenance over a road network
The likelihood of frost, ice and snow accumulation varies over a road network due to different influences of meteorological and environmental factors on road surface. These factors include precipitation, cloud cover, exposure, altitude, road construction, traffic, etc. They cause significant variation in road surface temperature and the likelihood in both temporal and spatial domains. Precise prediction of such variation and the likelihood will enable road authorities to salt and grit the road network in where and when salting and gritting is absolute needed in order to minimize winter maintenance costs. This paper presents the results of Vaisala Area Forecast model which makes 3-hour ahead forecasts of surface temperature, surface state, surface friction as well as other basic atmospheric parameters, based on digitised topographical dataset at a 200-metre spatial resolution. The model was run in both rural and urban environments and its forecasts were compared against measurements. The results are fairly accurate and consistent
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Customer satisfaction analysis for evaluating winter driving environment
The deterioration of driving environments in cold, snowy regions during winter causes unfavorable traffic characteristics such as reduced travel speeds, winter-type traffic accidents and lower user satisfaction. Road administrators work to maintain a safe, smooth flow of road traffic in winter by carrying out road maintenance. However, under the constraints of the current financial crisis, there is a need for further improvements in the appropriateness and efficiency of winter road maintenance. To achieve this, it is important to understand how the deterioration of road conditions affects user satisfaction and driving behavior. In this study, the authors sought to evaluate winter driving environments by simultaneously carrying out evaluation of road conditions, a user satisfaction survey and a driving behavior test both in autumn and in winter. As elements of driving behavior, driving speed and longitudinal acceleration were measured. Customer Satisfaction (CS) portfolio analysis was applied for the user satisfaction survey. The test results indicated a tendency for driving speeds and acceleration rates to decrease in winter. However, it remained unclear whether these changes in driving behavior were caused by the deterioration of road surfaces or by the narrowing of roads due to the presence of piled snow. Meanwhile, results corresponding to the characteristics of changes in road conditions were attained in the user satisfaction survey, thereby confirming the effectiveness of subjective assessment.
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New findings in winter maintenance and their implementation in Austria
Winter maintenance is essential to provide a high accessibility of regions with extensive snow fall or long winter periods. Typical decisions on a strategic level include choosing between de-icing agents, defining minimum treatment intervals, route quality targets, generalized winter maintenance strategies and specific application rates. On a practical level the winter maintenance personal has usually not enough time or information for an optimal consideration of all relevant factors in a changing environment. The winter maintenance personal is first in line if accidents happen and thus they tend towards a "safe approach" that differs from optimal application rates or timing of treatment. The comparison of typical winter maintenance situations with practical application rates showed a high potential of savings by preventing unnecessary salting. This leads to the main research question of optimized application rates and winter maintenance strategies for all typical situations that can still be applied in winter maintenance practise. To answer these questions the federal states of Austria, the Austrian highway operating company (ASFINAG) and the federal ministry of Transport (BMVIT) funded a research project at the Vienna University of Technology. Based on extensive field and lab testing at the Institute of Transportation the complex mechanical and physical impact of all main factors and their influence on winter maintenance could be determined. Practical testing and persistent discussion with selected personnel completed the verification and implementation of the findings in a comprehensive winter maintenance model. Further results include a compact practical winter maintenance guide and training courses for winter maintenance personnel. The presented paper gives a short overview of the findings with special emphasis on the contents of the winter maintenance guide.
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Better winter road weather information saves money, time, lives and environment
Weather service systems that collect, refine and distribute information to road users as well as network and maintenance operators can generate significant benefits to the stakeholders and the society. For maintenance operators improved efficiency through timely reactions, smaller amounts of de-icing materials and better personnel utilization offer clear potential for reduced costs. In the future, better weather models will result in more accurate longer-term forecasts thus increasing the potential for benefits. New technology enables new kinds of services (e.g. decision support systems). This paper provides a structured view of the benefits of weather information services to different stakeholders. A framework on the impacts of weather information on winter road management will be presented.
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Road weather management – Recent research results from the United States
The Federal Highway Administration Road Weather Management program has recently completed a number of research projects that advance the state-of-the-knowledge on the collection and use of road weather information for improved highway operations. The purpose of this presentation is to provide a synopsis of these research efforts. Specific projects include the Clarus Regional Demonstrations, where four decision support tools that use the Clarus data were developed, tested and evaluated. Clarus is an integrated database of North American environmental sensor station observations. These tools include a seasonal load restriction tool, enhanced road weather content for travel advisories, a non-winter maintenance decision support tool, and a multi-state control strategy tool. In addition, eight smaller-scale projects were recently completed that explore innovative uses of the Clarus data. Weather responsive traffic management is another area where significant advancements have been made. Specific projects that have been completed and will be summarized include the development of: a self-evaluation tool for integrating road weather information into a traffic operations centers, weather-sensitive traffic models, and design guidelines for road weather messages and dissemination methods. All of these projects will be reviewed, highlighting the key findings and lessons learned.
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The evolution of optimized and sustainable pro-active winter operations
Climate change is becoming a reality, making headlines world-wide and bringing record setting winter storms with some of the lowest temperatures and heaviest snowfalls and ice storms experienced in modern times. These massive storms caused major impacts to the economy, resulting world-wide in billions of dollars lost. However, it could have been much worse without the right snow removal equipment, advanced RWIS technology and a trained workforce. Fortunately, snow and ice control operations world-wide have undergone major changes in the past two decades. Prior to the mid-1990s, snow and ice control operations in the U.S. were mostly reactive in nature. Brute force combined with heavy applications of grit and chemistry were the standard operating practices for restoring mobility after a winter storm. However, research findings, from the comprehensive U.S. Strategic Highway Research Program (SHRP), combined with discoveries from the International Winter Maintenance Technology Scanning Tours produced better ways to accomplish winter maintenance, improve transportation safety and reliability and enhance winter hazard mitigation. This paper documents the courses of action used to take these research findings from theory to a pro-active operational state-of-the-practice. Although these pro-active snow and ice control operations are more efficient and effective, their negative impact to the receiving natural environment remains a concern. This paper addresses how those negative impacts are being minimized by using more comprehensive road/weather forecasts and treatment recommendation decision making processes, identifies pathways that show promise for optimizing all aspects snow and ice control operations, and illustrates how these optimized operations will evolve into more sustainable solutions that will integrate into the PIARC B-5, Winter Services Committee, "triple bottom line" (economic concerns, societal interests, and environmental protections) concept models currently being developed for world-wide use.
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Evaluating the degree of visibility deterioration perceived by drivers during snowstorms
Road closures and multi-vehicle collisions often result from poor visibility caused by snowstorms in cold snowy regions. It is therefore important to grasp visibility conditions in snowstorms. Currently, visibility meters are used to determine visibility based on the MOR (meteorological optical range) value calculated from atmospheric extinction coefficients or scatter coefficient measurements. However, in road traffic environments, delineators and various other visual guides are installed along routes, and the visibility perceived by drivers in snowstorms may vary even for the same MOR value. Despite this, no parameter has yet been devised to support comprehensive evaluation of visibility deterioration. Accordingly, this study investigated the relationship between MOR values measured using a visibility meter and visibility/driving difficulty as perceived by drivers, and also examined a method for the rating index of visibility deterioration.
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Automated road condition forecasts on a public map service
Purpose of this abstract is to introduce a new fully automated and revolutionary road condition forecast system available for all road users in Finland. The system was delivered for the Finnish Transport Agency as a turnkey solution by the Finnish GIS-company Karttakeskus. The forecasts and weather models are produced by the Finnish Meteorological institute. The aim of the system is to reduce work load of road weather professionals on duty by offering user-friendly road condition information accessible from any location. In addition, all road users can plan and schedule their travel routes according to prevailing road conditions at no extra cost. Road conditions are updated real-time in 15-minute intervals and weather conditions once an hour. The service also provides up-to-date pictures from about two hundred road weather cameras.
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Optimized traffic control with benchmarked road weather data
Within section control systems on motorways, road weather data are used in order to improve traffic safety during adverse weather conditions. Variable message signs (VMS) are displayed to warn road users of various conditions including slippery road surfaces or fog. However, the quality of acquired road weather data and traffic control measures is not checked on a regular basis, if at all. Within the German Test Site for Road Weather Stations [5], plausibility checks were developed, tested and improved by a German working group. These plausibility checks can be used to detect erroneous measurements and thus improve the quality of traffic control measures. If the speed limits and warnings displayed via VMS correspond to the prevailing traffic and weather situation, the acceptance by the road users will be high. Therefore the input data must be of a high quality. The plausibility checks for road weather data are published within a German Technical Bulletin [4]. In order to maximize benefits from these plausibility checks, operators and traffic engineers must decide how to interpret and react to the results of the checks. Facilitating and improving this decision are the goals of this paper.
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Road weather data presentation in BUFR format
Well arranged presentation of road weather data and information is a basic tool for composing specialized road weather forecasts as well as its verification. The forecasters on central and regional forecasting centres are utilizing resources processed by sophisticated presentation system Visual Weather. The system works on the basis of unique database and is capable of decoding various data formats. For the purpose of road meteorology the "Unified road weather data format" - BUFR is used. As a superstructure over this system the System for forecast of the road surface on the territory of the Czech Republic which is currently under development is planned.
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Enhanced road weather warnings and improved communication strategies within Central Europe as part of the INCA-CE project
Among the main goals of the EU-funded INCA-CE project is to improve risk management standards and methodology in the field of road safety. In a transnational Central-European cooperation the INCA analysis and forecast system is advanced and refined scientifically and for applications in different sectors. Also, communication lines between developers, weather services, stakeholders and local authorities will be improved.
The INCA analyses and forecasts that are mostly widely used by Austrian road management authorities are 2m-temperature, ground temperature, precipitation and precipitation type. Moreover, criteria have been defined for automatically generated SMS warnings for snowfall and black ice. Also, INCA forecast fields have been compared to the Canadian METRo model, and it was found that INCA ground temperature fields generally meet the reality quite well. However, in case of rapidly changing road temperatures, METRo can refine the forecast quality in the first 1-12 hours.
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Forecasting of traffic jams on high-ways caused by adverse weather
Adverse weather generally decreases visibility and increases slipperiness that can cause development of traffic jams on high-ways. The article presents a new model for prediction of the jam length based upon information about a proper speed limit at the critical road sector. For the prediction the model also applies a time series of traffic flow at this sector that is forecast by an intelligent module based upon records of traffic activity in the past. The forecast flow is converted to traffic jam characteristic by integrating the number of cars that are stopped due to decreased road capacity while more detailed description of the jam properties is given by the solutions of partial differential equations. Performance of the corresponding computer program is demonstrated by forecasting the evolution of the traffic jam during rush-hour at the point of maximal traffic activity of high-ways at Ljubljana in Slovenia where inconvenient influences of weather are often observed.
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Modifying winter performance criteria using a storm severity index
The challenge of any given winter storm, from the point of view of the service provider, is a direct function of how severe that storm is. Therefore, using a performance criterion that does not take into account the severity of the storm provides an incomplete measure of how well a given agency has performed during any specific storm. The challenge is to normalize performance as a function of the severity of the storm. This paper will describe a method for this normalization. A previously developed winter storm index expresses the severity of a storm as a number between 0 and 1. The severity is a function of six variables: the type of precipitation in the storm, the pavement temperature during the storm, the early storm behavior, the wind speeds during the storm, the pavement temperature trend after the storm, and the wind speeds after the storm. The algorithm developed to measure storm severity in this way was previously evaluated by a group (about 40) of road managers who provided input to the process. Measuring winter service performance can be done in a number of different ways: for example, pavement friction, visual observation of the pavement condition, average vehicle speeds, and average traffic volume. Each of these methods have benefits and drawbacks. The challenge addressed in this paper is to take one of the measures (average vehicle speed) and use the storm severity index to normalize the performance measure.Studies have shown that vehicle speeds drop during winter storms. The amount of speed reduction is a function of the road type as well as the storm severity. Examination of historical data indicates that there is a maximum expected speed reduction under the most severe conditions, beyond which the road essentially becomes closed (either by direct agency action, or as a result of crashes and traffic jams). This maximum expected speed reduction can be considered a target value for the most severe storm (i.e. one with a storm severity of 1). As an example, on a typical US 2-lane highway, the maximum expected speed reduction is 18 mph.Since most storms are not so severe, this speed reduction can be simply modified by multiplying it by the storm severity. So, in a case where the storm severity is 0.50, the expected speed reduction would be 9 mph (18 x 0.5). If average speeds on a given road segment during a storm are seen to drop more than this, then additional resources are needed and can be supplied prior to the end of the storm.
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Connected vehicle safety network and road weather forecasting – The WiSafeCar project
Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications are novelty key technological approaches to improve road transport efficiency and safety. The European WiSafeCar project focused on developing an intelligent hybrid wireless traffic safety network between vehicles and infrastructure. Vehicle based sensors and observations were exploited to generate intelligent real-time services on a service platform. WiSafeCar considers both the urban environment and rural areas, where no high-density telecommunication base station network is available. Traffic safety is enhanced by providing accurate and timely information about accidents and adverse weather. The application provides a platform for true bi-directional Internet-like networking tailored for connected vehicles.
In order to exploit platform capabilities and to analyze system capacity, several pilot services were developed. Accident warnings and various kinds of incident warnings as well as some driver convenience services were among the 10 initiated services. The most unique and specific application for the Finnish environment is the wintertime road weather service. Input generator for this service is the road weather forecasting system of the Finnish Meteorological Institute (FMI). The application provides detailed and customized, local en-route information on potential adverse weather conditions and events like road surface slipperiness and poor visibility in the form of real-time observations and short-range forecasts.
The communication platform designed for the project was based on IEEE 802.11p and 3G communication protocols. A pilot system was developed to showcase the platform and the operability of its services. A public pilot was established in Tampere in January 2012, where the services were demonstrated in a real operational environment. Additional IEEE 802.11p communication field measurements and large-scale simulations have ultimately validated our general impression that the WiSafeCar platform is a potential solution for a comprehensive future vehicular communication entity.
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The use of connected vehicle observations in weather applications for various highly impacted users of the roads
The United States Department of Transportation's (USDOT) Federal Highway Administration (FHWA) and Research and Innovative Technology Administration (RITA) are jointly working to promote safety, mobility, and the environment on the nation's surface transportation system through a new connected vehicle initiative. This initiative is a multimodal effort to enable wireless communications among vehicles, the infrastructure, and passengers' personal communication devices. It will enhance Americans' safety, mobility, and quality of life, while helping to reduce the environmental impact of surface transportation.
In the near future millions of vehicles (both public and private) will be connected and the logistical, mechanical, and environmental data from these vehicles will be communicated (vehicle-to-vehicle and/or vehicle-to-infrastructure), collected, and stored in order to provide diagnostic information of weather impacts to the surface transportation community. These data will include, but are not necessarily limited to, the following observations, which will likely change with changing weather:
1. Directly Measured – air temperature, barometric pressure
2. Mechanical – wiper status, Anti-lock Braking System (ABS) status, traction/stability control, differential wheel speed, steering angle
3. Logistical – speed, location, elevation, heading
4. Directly Measured - pavement temperature, friction, salinity, freeze-point
Since 2009, the University Corporation for Atmospheric Research's (UCAR) National Center for Atmospheric Research (NCAR) has worked with FHWA and RITA to develop the Vehicle Data Translator (VDT) software which ingests, parses, processes, and quality checks mobile data observations (e.g., native and/or external) along with additional ancillary weather data (e.g., radar, satellite, fixed observations, and model data). The first two versions of this software were developed with data collected from vehicles in the Developmental Testbed Environment (DTE) during the winter and spring seasons of 2009 and 2010. The third version (VDT 3.0) is currently under development and applications for several highly impacted end-user groups are being considered for development and will be discussed in this paper.
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Observed and future changes of extreme winter events in Europe with implication for road transportation
Extreme weather events can have remarkable impact on the transport sector, causing severe damages and large economic losses. The mobility and safety of road transport is mostly affected by extreme winter conditions that might lead to significant disruptions, increased accident risk and costs of damage. Extremes are likely to continue to occur in the future due to the projected climate change; consequences of changes may be both negative and positive for all the transportation sectors.
This study provides an overview of changes in the extreme and adverse winter phenomena that are most likely to affect the European road network focusing on present climate and the projected future climate (1970-2070). Individual phenomena, such as heavy snowfall, freezing temperatures, strong wind gusts and also their combination, blizzard is considered. The estimation of the recent and past events is based on the observed data available from the E-OBS dataset and the ECMWF ERA-Interim re-analysis dataset. The analysis of the relevant hazardous weather phenomena takes into account the ranking and impact threshold values defined from the viewpoint of the infrastructure and different transport modes such as road, rail, aviation and waterways. Future changes in the probability of severe events are assessed based on six high-resolution regional climate model simulations produced in the ENSEMBLES project. A range of statistical methods are applied to define the features of the extremes such as their probability, changes in the spatial extension, intensity and temporal duration.
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Road friction monitoring
Slippery roads cause thousands of accidents each year. One reason for accidents is the difficulty to evaluate correctly the road friction. A study made in Finland indicates that almost half of the persons misjudge the friction. Some equipment is available for the friction detection but thus far their results are not reliable enough for this task. In this paper, we study the use of near-infrared (NIR) camera for reliable road condition detection. The obtained results indicate feasibility of the system. However, the camera is sensitive for varying lighting conditions so the system should be advanced further.
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The role of weather information in sustainable winter services
There has been growing interest in applying the principles of sustainability to the provision of winter services. For example, the Winter Services committee of PIARC (committee B-5) has made this an area of emphasis for their activities over the past five years. In spite of this interest, there has, to date, been relatively little work done on applying the principles of sustainability to transportation operations in general and to winter services operations in particular. This is beginning to change, as for example the FHWA sustainability tools include some attempts to measure the sustainability of various operational practices, including winter maintenance. However, and perhaps inevitably given the broad scope of FHWA interests of which winter services are but a small part, the FHWA sustainability tool includes only a handful of rating questions relating to winter services. A more comprehensive review of the sustainability of winter services has been conducted based on six general areas of operations: levels of service, materials usage, equipment selection and operation, performance measurement and continuous improvement, strategic (annual) operations, and tactical (in-storm) operations. While none of these six areas include weather information in the title, it is apparent that such information is critical to sustainable winter operations. This paper will discuss how weather information can enhance and improve the sustainable nature of winter services. Further, it will show that without a comprehensive, pavement-based approach to winter storms, it is very difficult to be sustainable in winter service operations. While the importance of meteorological information to good winter service has been known for many years, it is of value to note that when new paradigms (such as sustainability) are brought into the analysis of winter services, the importance of excellent weather information remains central. Quantifying that importance in ways that show its contribution to the "triple bottom line" of sustainability will be an important task going forward.
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Finnish ITS future scenarios
Finland has actively been deleoping a new era of safer, cleaner and smarter transport system – based on intelligent systems. The pioneering work of the Finnish Ministry of Transport and Communications to build up a national ITS strategy, first in Europe, has enabled concrete actions at national level – also as an European forerunner. Well focused strategy work has built a backbone for ITS innovation and implementation – over the years to come. We have a good opportunity to joint a new way of thinking of transport system and a forthcoming European ITS event in 2014 in Helsinki to show what we have achieved. Widely agreed - the Finnish way for ITS - is taking us towards the next generation of transport systems utilizing sustainable ITS solutions. The road to future is paved with well balanced public-private partnerships serving the various markets.
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An environmental method against icing for road pavements: Development of test equipment and procedure
In cold regions, snow and ice in winter times can have serious threats on the road surface and driving conditions. Icing always occurs on road in winter. It hindrances traffic and causes traffic accidents. The grip is lessened between wheel tires and pavement surface by icing. In these cases, traffic accident risk on icy road pavement surfaces is very high. In general, mixture of abrasive granular materials and chemicals as salts are used to keep away snow and ice from bonding to the pavement. The aim of this work is to introduce a new low-cost test equipment to evaluation the abrasion materials. The test equipment, Ice Melter Equipment Mechanism (IMEM), on the evaluation of the abrasive granular materials of snowy or icy pavement surfaces has been proposed for the first time. Working procedure of the equipment is also given in the study.
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An Environmental Method against Icing for Road Pavements: A new icy abrasion material and analysis
With more heavy snow and the freezing temperatures, road pavements become very slippery, and some may be difficult to drive on. Drivers should go slow and expect delays. In this context, in cold regions, winter maintenance practices include plowing, sanding to improve traction and applying winter anti-icing liquids. Winter liquids are used to melt snow and ice on the road surface to help prevent snow and ice from sticking to the road and in conjunction with sand to help the sand stick to icy roads. The combination of practices used at each site may vary to accommodate the different climate, traffic and storm conditions. While road salt is also an effective tool for melting snow and ice, it also causes severe rust damage to vehicles, degrades the road surface, corrodes bridges and may harm roadside vegetation. Therefore, abrasive granular material to be used has important role to prevent snow and ice from sticking to the road surface. But generally used sand tends to be easily trafficked from the road surface. Here, a new icy abrasion material not to be easily trafficked from road surface is introduced and performance test is applied by using new testing equipment, "Ice Melter Experiment Mechanism" (IMEM) and test results are analyzed.
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A scalable, modular back-end IT architecture for a RWIS implementation
RWIS (Road Weather Information System) implementations vary significantly in size and complexity. The number of stations can be from a few to several hundreds, as well as the number of users. A RWIS typically includes a wide spectrum of different application modules, for example, to calculate derivative sensor values, to provide interfaces to other IT-systems, to guide variable message sign systems, etc. Due to considerable variation in configurations and functional requirements, the back-end IT architecture of a RWIS system must be highly scalable and modular with open interfaces to other systems.
The architecture designed here is scalable, so that the number of road weather and traffic information stations and cameras can be increased without compromising the overall system performance. The scalability is based on multiple independent servers to collect the data.
The architecture provides easy access to the existing information via open interfaces. Typically these interfaces are used by different end user applications for road maintenance professionals, traffic control systems, maintenance and metadata management of the stations and cameras, other RWIS systems, road weather modeling, analysis and forecasting applications and value added application providers.
The RWIS software management and monitoring is based on a centralized management server, which contains all operational parameters of the back-end applications and their log information. A XML/HTTP interface is provided for a browser-based management application.
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Route- based winter road weather forecasting method by using GIS
Traffic accidents due to weather happen more frequently in winter time than in other seasons, and this is caused by the severe road condition. Winter road maintenance engineers always need to decide when and where to plough or spread salt on the roads. Proper road maintenance performance could not only save money and time, but also decrease the amounts of accidents. However, these decisions are difficult to make due to the complex and fast weather changes. To help winter road maintenance engineers with effective road maintenance, many researches that focus on forecasting of road surface conditions have been carried out. Here, a case study of route-based road surface temperature and condition forecasting by using geographical information system (GIS) techniques in Borĺs, Sweden will be presented. In this study, different geographical parameters, such as altitude, landuse etc., will be obtained from Digital elevation model and landuse data. Together with meteorological data (i.e. solar radiation), road weather information system (RWIS) station data, the influence of geographical parameters on road surface temperature and road surface condition will be analyzed. In this study, different road segmentations based on topographical dataset will be calculated in GIS. After field validation, all the segmentation results will be imported into road surface condition forecasting model- Support System for Winter Maintenance (SSWM) and spatial distribution of road surface temperature will be predicted. By comparing the forecasting results with thermal mapping and RWIS station data in the coming winter, the performance of the forecast techniques will be evaluated, and the segmentation technique will be improved and finally used for predicting road surface temperature and road surface condition. The study result will be used for helping winter road maintenance engineers in decision making and thereby save both lives and money.
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The effect of snowfall and low temperature on road traffic accident rates in Southern Finland
Adverse weather conditions have a negative impact on road traffic, especially when icing and snowfall are concerned. Ice and snow on the road reduce the friction effectively. Poor visibility caused by snowfall or drifting snow further worsens the driving conditions. Accident rates typically peak in Finland during days with plenty of snowfall, although people should be used to driving in snowy conditions due to Finland's northern location. Drivers are also warned in advance about the approaching snowfall and roads are generally well maintained. This study deals with the effect of snowfall and low temperature on traffic accident rates in Southern Finland, Kymenlaakso County being the compact study area. Daily weather observations (mean temperature and precipitation) and Finnish Motor Insurers Centre's accident data from winters 2002/03 – 2007/08 were used in the investigations. A relative accident rate was calculated for each individual day by dividing the accident amount by the mean value of the day of the week in question. The results indicate that there appears to be, on average, an increasing trend in the relative accident rate in function of the daily precipitation in temperatures equal or below 0 oC, although the variability is large. For example, a snowfall exceeding 10 cm results in a double amount of accidents, on average, compared to the daily mean value during wintertime. When studying the cases with highest relative accident rate ("top ten ranking"), it appeared that most of those cases were linked to daily snowfall amount of ca 5 cm or more.
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Operational OpenRoad verification.
OpenRoad is a weather forecasting package that helps road maintenance decision makers to plan, manage and minimise the effects of winter weather by the use of accurate weather forecasts. Road travel on a timely basis is essential for economies globally in the modern world. Cold winter climates impact on safety with dangerous travelling conditions causing delays. Therefore, the prediction and prevention of ice formation is imperative to mitigate against these.
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Trends of winter climate conditions in cold snowy regions of Japan
In recent years, global warming has caused fluctuations in snowfall amounts and temperatures in cold snowy regions of Japan. Accordingly, when considering long-range plans for snow and ice control, it is necessary to identify trends related to snow and ice environments. Against such a background, this study involved a survey to clarify the trends of snowfall and snow depth based on data from the past about 30 years. The items surveyed were seasonal maximum snow depth (cm), the number of days with continuous snow cover, accumulated seasonal snowfall (cm), maximum 24-, 48- and 72-hour amounts from the onset of snowfall, and the frequency of 24-hour periods with snowfall exceeding 40 cm in winter. The results revealed that, although accumulated seasonal snowfall showed an overall decreasing tendency, the seasonal maximum snow depth and the frequency of 24-hour periods with snowfall exceeding 40 cm in winter were on the rise in eastern Hokkaido, which faces the Sea of Okhotsk and the Pacific Ocean. In general, this region is considered to have little snowfall as long as stable winter pressure patterns continue, while snowfall increases when low pressure develops over the Pacific Ocean and the Sea of Okhotsk. Changes in snowfall patterns and in the distribution of areas with short-term heavy snowfall were seen.
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Remote sensor technologies for RWIS Applications
There is currently considerable interest among road authorities of remote, also called noncontact or non-intrusive, sensor technologies. This interest is based on the fact that remote sensors are easier to install than road mounted sensors. The remote sensors are usually infrared (IR) temperature sensors or IR road status sensors. The road status sensors usually differentiate between dry, wet, icy or snowy road surfaces. This paper describes the techniques used for IR thermometry and road status detection. Furthermore, the pros and cons are discussed with this new sensor technology
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How to keep track on energy use within road maintenance operations
This study is part of a PhD-project about energy efficiency at the University of Gothenburg, in collaboration with the contractor Skanska. Energy efficiency is somewhat new as a concept within winter road maintenance as the expression efficiency so far mainly has been used in terms of total maintenance rather than on energy. Looking historically in previous years' SIRWEC presentations there are many studies and projects that most likely led to energy efficiency as a by product but the energy was never considered in those cases. Economic crises, rising fuel prices and increased environmental concerns in the wake of the climate change debate have however led to an increased willingness to not only reduce costs but to become more energy efficient as well. Energy efficiency is all about sustaining the same outcome while using less fuel, at least when thinking in terms of winter road maintenance. It is hence important to know how much fuel the different winter road maintenance activities are using, to be able to estimate the efficiency of various improvements to the operations. One way of doing this is to let every driver report how much fuel each activity have used. This is time consuming and brings about the factor of finding willing drivers to take on such a task. Another more manageable way of doing this is to use calculated standards. Present study has used preset calculations for fuel use and the number of hours per winter that was used for different winter maintenance operations to calculate a comparable energy use within the operations in different climatic regions in Sweden. This will however only give so much information; hence a test will be set for the winter of 2011/2012 where the actual fuel usage will be monitored on a few vehicles in a maintenance district. The results will hopefully give a more accurate index for fuel use in the different regions.
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New approaches for modelling of weather impact on traffic flow
The knowledge about the influence of certain weather conditions on traffic flow and driving behaviour become more and more an important issue for transportation authorities as well as for road users. The accuracy of travel time prediction and traffic condition forecast decreases significantly without considering the weather and road surface condition. A lot of research works are published in the past covering different isolated aspects of weather conditions and the influence on traffic or driving behaviour, such as aquaplaning, tire friction, effect of rain etc. But there is no comprehensive model available, describing the impact of all weather aspects on the traffic flow. The aim of the work was, to fill that gap. The model provides the parameters of a complete fundamental diagram of highway lanes due to certain weather parameters, which could be obtained from weather forecasts. The model approach was developed in two ways. First by using theoretical driving dynamic relationships with the key parameters tire friction and visibility for the structure of the model. And second by analysing real weather and road surface condition data in correlation with acquired traffic flow data. An innovative description model for the fundamental diagram based on the velocity and traffic density level was used. Therefore the fundamental diagram can be described by a few basic parameters e.g. free velocity, critical traffic density and velocity and maximal density. These parameters can be expressed in dependency to the weather condition. The paper describes the basic principle of the model approach and shows results of the analysis and also demonstrates applications for winter maintenance decision and for traffic information services. The model approach was also used for estimation of capacity reduction of highway lanes under adverse weather conditions. The capacity reduction factors associated to weather condition classes have proven there reliability in field tests under the framework of a research project "congestion prevention on winter conditions" by order of the German federal highway institute. Existing road weather information operation platforms can be enhanced in order to give outputs to weather calibration of traffic forecast models.
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2D and 3D road climatic models
The method for the creation of special 2D and 3D road climatic models for solving problems of works control on the winter road maintenance is provided in the article. Road and climatic information are used for modeling. 3D models are created on the maps basis. The procedure of constructing 2D and 3D models with the help of software system CREDO is described. The method of processing meteorological data for reception of blizzards settlement parameters is considered. 2D model calculation results are shown on distribution maps of total snow transfer volume and blizzards duration. The method of creation line road climatic graphs is suggested. The task list of management work on roads winter maintenance, where the modeling results can be used, is shown.
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The effect of weather on transportation: Assessing the impact thresholds for adverse weather phenomena
Transportation is strongly affected by adverse weather. This was experienced in many parts of Europe during the cold winter 2009/10, when snow, ice and sub zero temperatures occasionally paralyzed road and rail transport as well as aviation. The interannual variability in weather conditions is significant, especially in Central and Northern Europe and harsh winters occur occasionally. The probability distribution of adverse and extreme weather events can be assessed using a suite of indices. For this purpose, threshold indices for different weather phenomena were defined taking into account the impact of weather and climate extremes on all transportation sectors. This procedure enables a European wide analysis of probabilities of hazardous events in the "present climate" as well as in the projected future climate using climate model simulations. The investigations were carried out in the EU/FP7 project EWENT. Here we concentrate mainly on road transport and winter conditions. Strong wind, heavy snowfall and low temperature typically result in traffic jams and increased accident rates in spite of effective maintenance operations. Thus, blizzard is a good example of an event that is hazardous for road transportation (as well as for other transportation means). Based on this study, it is suggested that the impact thresholds for the blizzard are: 24h snowfall = 10 cm, daily mean temperature = 0oC and maximum wind gusts = 17 m/s. With these criteria, the frequencies of occurrence (probabilities) of blizzards in different parts of Europe can be estimated, in the present and future climate.
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SURFEX compared to FMI's road weather model and road weather observations
SURFEX is a physically-based model of air-surface interactions, included in the HARMONIE numerical weather prediction system employed at Finnish Meteorological Institute (FMI). Urban and built-up surfaces are treated by a town energy balance model, where the temperature, water reservoir and snow cover of road surfaces are calculated.
FMI's road weather model (RWM) is an energy balance model calculating what happens on the road surface due the weather. The model gives information e.g. if the road surface is covered by ice or snow and it calculates the road surface temperature. The influence of traffic is taken into account as well.
In this study the modelled road surface temperature is compared to road weather observations in the winter of 2011-2012. Road weather temperature modelled by SURFEX and FMI's road weather model are studied. Also, the modelled air temperature is compared and presented.
Three locations are chosen as test sites. The criterion for the selected places is that the points need to be urban and there need to be a road weather station to measure road surface temperature. The selected places are Helsinki, Tampere and Rovaniemi which all present different kind of climate area.
The main goal of this study is to find out the reliability of the road surface temperature calculated by SURFEX model. In Helsinki SURFEX and RWM perform equally well, but in the colder conditions of Tampere and Rovaniemi RWM is clearly superior.
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Intelligent road side platform for future applications
Imagine an ITS infrastructure where vehicle, road side units and sensors can connect and communicate wireless with each other and work together. With more computer power in the road side units, it's possible to analyze and process data in the field and make faster decisions. This new infrastructure can be used to build more automated and intelligent ITS systems to get faster and safer transportations, while reducing the load on the environment.
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Fine-scale road stretch forecasting: Applications of high resolution terrain and thermal mapping data
The system, which can provide operational forecasts of the road surface temperature, air temperature and dew point temperature for any road stretch in Denmark, has been developed, tested and verified. Detailed local characteristics influencing shadowing effects for road stretches were obtained from the high resolution Danish databases on terrain, obstacles, height, land-use, etc. The thermal mapping data taken during road winter seasons of 2008-2011 were used for verification. It was found that the forecast quality for the road stretches was comparable to the forecast quality for the road stations.
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New possibilities by combining passive and active measurement surface sensors
Detection of the de-icing chemical concentration in terms of Freeze Temperature is still an important key parameter for winter maintenance decision support systems. There are many reasons for choosing passive measurement methods for the detection of freeze point, for example the quick response time, low power consumption, and the application of well know type of salt mixture and so on. But there are also many reasons for choosing active measurement methods, for example at airports, where often non dissociating de-icing chemicals are used and so on. An active sensor is only capable in measuring the freeze temperature and also not able to provide accurate measurement of surface temperatures because of the influence of the necessary cooling and heating energy. And also detailed surface condition classes are not supported by active sensors. A well know manufacturer and developer of passive and active surface sensors recently now introducing an innovative solution in combination of passive and active measurement transducers. The combination is able to provide all useful parameters for winter maintenance decision support and also for traffic control purposes. The Parameters are for example active Freeze Temperature, detailed surface condition classes, salt concentrations, surface temperature and waterfilm depth and a lot more. The paper describes the basic technical solution and field application results. There is also a comprehensive comparison between the different surface condition detection methods in-pavement and non-invasive technologies.
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Improved weather information for the road rector (INCA-CE project) INCA surface temperature forecasting
This paper presents the preliminary results of INCA weather forecast (nowcast) pilot implementation in winter road maintenance in Slovenia. The purpose of the study was to evaluate the INCA surface temperature forecasts and to provide possible stand-alone use of these forecasts for predicting road surface temperature. Another objective was to test a simple linear regression approach for 6-hour road surface temperature predictions, using the surface temperature and incoming solar flux forecasts and measurements from road weather stations as a learning data and only surface temperature forecasts for testing data. Results showed that INCA forecasts could be used stand-alone for predicting road surface temperature at locations with simple topography. To increase the accuracy, simple linear regression equations can be used to achieve the average RMS error around or below 2°C. However, the results are preliminary and could be speculative, so more road weather stations should be taken into account to confirm them and decide whether accurate weather forecasts with high spatial and time resolution are suitable for use as a stand-alone or in combinations with simple regression models for point-based/route-based road surface temperature predictions.
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Meteorological conditions of ice and snow accretion formation in the Ukraine
Meteorological conditions of formation of icing events, including glaze, hard and soft rime, wet and frozen wet snow for 25 sites of the Ukraine are examined on the basis of surface observation in the 2002–2009 period. To provide the information about the maximum expected amount of accreted ice at a certain site, a site icing index is calculated by using icing frequency, duration and intensity. For all stations degree of severity of local icing conditions varies from 'occasional' to 'moderate'.
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Road weather forecasting for a ring highway
Road weather forecasting tool has been developed in the framework of MeteoTrassa system to provide information for winter maintenance of a ring highway in Saint-Petersburg. The ring highway is a complicated engineer object which stretches over 112 km under the influence of different local climate conditions and consists of flyovers, bridges, low and high sections. Road surface temperature and condition are influenced by a wide range of meteorological, geographical and road construction parameters. From thermal mapping, we determined large local differences in road surface temperature up to 5 deg Celsius over a distance of several hundred meters. The system forecasts road surface temperatures and conditions for an entire route for 4-6 hours ahead based on a local atmospheric boundary layer numerical model, thermal mapping and road construction data. Doppler weather radar data enables us to nowcast and to forecast precipitation in terms of onset and cessation of snowfall, its intensity, amount of snowfall for separate sections of the highway. The system takes an advantage of existing weather data sources (25 road weather stations, Doppler weather radar, regional and mesoscale forecasts), combines data to present integrated road weather observations and forecasts for the entire highway. Road weather station network of high density gives basis for forecast verification.
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Decision support system for winter maintenance - research and practice
Maintenance of roads and bridges in winter is a serious challenge for road services with roadway safety, economic efficiency and low environmental impacts bearing in mind. The most important problem is the timely making of effective decisions for treatment types and timing of treatment. In order to support winter maintenance personnel, an interactive, automated decision-support system MeteoTrassa for winter road maintenance has been developed. The system uses a variety of data: road weather station network, weather radar, mesoscale model, road weather and surface condition forecast model, and thermal mapping. It collects and processes information from various sources and makes recommendations on road treatments based on a predetermined algorithm tailored to the particular road authority's resources. Four algorithms for different types of de-icing agents and operational practices have been implemented. The system generates specific guidance based on current weather and forecasts which includes information regarding treatment procedure, timing, rate and location. Also an user can interact with the system to correct the treatment plan. The system includes a prediction module to forecast temperature and state of the road and precipitation for tactical time period (0-4 or 6 hours). Road surface temperature and condition forecast is based on the atmospheric boundary layer model. Precipitation forecast is based on actual weather radar data and velocity and direction of radio echo movement. System has been successfully used in operational practice by road services in several regions of Russia with different climate.
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Utilization of panoramic images as an alternative to fish-eye photographs
for documentation of road weather stations
The poster will present samples of pictures made from photos taken around road weather stations. A simple technique of merging photos was used to catch the vicinity of the measuring site. Some examples of temperature curves will be shown to illustrate site properties. Such documentation is useful for students and forecasters and for training of road maintenance managers. In the case of Czech Republic it can also be used by researchers in verification process during parametrization of energy balance models.
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Statistical data of slipping injuries happened in the winter time
Finnish Meteorological Institute (FMI) issues warnings about highly slippery pedestrian weather when extra attention should be paid to the choice of footwear and slip guards should be worn. Slipperiness often occurs when snow or water layer is lying above the ice layer. There may be wide local variations in walkway conditions, depending on factors such as maintenance practices and the volume of pedestrians.
FMI has developed a pavement condition model which a special version of the FMI's road weather model. The model determines the condition existing on pavements from the viewpoint of pedestrians. It differs from the road weather model mainly with respect to the description of wear of ice, snow, etc. and in the interpretation of weather details. In addition, the determination of the forecast initial state uses data from a longer observation simulation period.
The lack of observations is a big problem when developing the pavement condition model and doing the verifications. There are only two weather stations measuring prevailing road condition on walkways. Slipping injuries have been used as an indicator of slippery days. Especially, accidents happening on a way to and from work have been found to be among the best sources indicating the most slippery days for pedestrians.
This study presents statistic of the slipping accidents. The most slippery days can be found from the statistic as a peak days of slipping injuries. Also, accidents statistically compared to given slipperiness warnings for pedestrians are compared and presented in this study. Comparison reveals that during the most peak day cases a warning of slippery pavements is given but not always.
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Features of snow cover distribution in the southern Ukraine
Snow is one of the most common natural phenomenon that actively affects the society and economy in many parts of the world. Influence of snow on society multifaceted and includes complex physical, social, economic and psychological aspects. Thickness, density, moisture content and strength of snow cover are the main physical parameters that are taken into account when using snow and deal with it. The thickness of snow cover and duration of its occurrence have social and economic significance and impact on the environment. Various domains of economics are in the highest degree of vulnerability when a strong wind, low temperatures and freezing rain accompanied or followed by heavy snowfalls. In addition, it is important to time broke out snowstorm. For example, the most unpleasant consequences of a storm can result in peak hours or during harvest. Processing and analysis of source data made it enable to characterize the distribution of snow cover in the Odessa region. Maximum number of cases of snow cover observed at stations Lyubashivka (887) and Rozdilna (634). Minimum number of cases with snow cover falls to the station Vilkove (345), which is located in the southern area in question. Snow cover height often reaches to 5cm on all rails. The height of snow cover > 35 cm is observed at stations of Zatyshya, Lyubashivka and Serbka, i.e. those stations located in the north of Odessa region and away from the coastline. The degree of snow coating of station is usually 10 points for all stations in question. The maximum number of cases has the nature of occurrence of snow cover "uniform snow cover on frozen ground" (digit 0) and "uneven snow cover on frozen ground" (digit 3). From the analysis of temporal distribution of the number of days with snow cover at the stations of the Odessa region can conclude that the longest winters are observed in 1996 and 2002 - 2003. Shortest winters occurred in 2001 and 2007. The coefficients of asymmetry and excess are positive values at all stations of the Odessa region, suggesting an increase in the height of snow cover from the beginning to the end of the study period. For the longest winter the typical synoptic situations - southern cyclones and north-western and western anticyclones. In general, for the period under review 109 observed synoptic situations that contributed to the formation and storage of snow cover in the Odessa area, including the cyclone 64 and 45 anticyclones. Most of the synoptic processes observed in the winter of
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Road surface information system
In order to classify the road condition, dry asphalt and asphalt covered with water, ice and snow a technique using a sensor called Road eye is presented. The Road eye sensor uses three wavelengths and one photo detector to determine the intensities that are reflected from the road surface and is then able to estimate the road condition. By linking the Road eye sensor to a GPS and a Mulle, a miniature wireless Embedded Internet System, the road conditions can be associated with the correct road position, making it possible to use the information in many different applications.
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Use of real time weather variables to operate rural interstates in Wyoming
The State of Wyoming experiences severe weather conditions including strong winds and fast moving, winter weather events that create significant safety hazards for travellers. The Wyoming Department of Transportation (WYDOT) views the use of Intelligent Transportation System (ITS) applications as promising methods for improving safety along these roadways. The roadways selected for ITS deployment are rural roadways with limited services and long distances between travel decision points. The primary purpose of these applications are to increase roadway safety by providing better real time weather information and through weather responsive roadway operations. Recent ITS projects utilize real-time weather data to help operate the ITS components along segments particularly hazardous rural interstate segments to help determine DMS message sets, set variable speed limits, and to warn commercial vehicles of hazardous wind conditions. Another current project is also investigating the use of real time data to develop a road condition travel index system to better inform travellers of the current roadway conditions.
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Calibration of road surface temperature forecasts
At FMI there is a long tradition to forecast physical road conditions. The first attempts date back to 1970's and nowadays an operation road weather model is used to provide guidance for forecasters and customers.
,br> The present operational road weather model is a 1-dimensional energy balance model, which calculates vertical heat transfer in the ground and at the road-atmosphere interface, taking into account the special conditions prevailing at the road surface and below it.
The meteorological forcing variables are ambient temperature, relative humidity, wind speed, short-wave radiation, long-wave radiation (mostly from clouds) and precipitation. The forcing data is from operational HIRLAM model or from ECMWF model.
A calibration test with Kalman filter has been made using local road surface temperature observations and corresponding forecasts for several road weather stations in Finland. The focus of the work is to find optimal ranges of measurement noise and system noise for a practical application. A simple adaptive estimation algorithm for system noise is presented. The results have been promising so far and show strong support for operative application.
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MTQ save millions $ by relining culverts with long life spend pipes
Too many culverts have surpass their design life spend. The irrigated bottom of those structures, change the fluidity of the original design system, to a point that they cannot face the storm designs anymore. The failing structures also permit wash-out of the soil support of the road, creating voids between the road and the culvert.
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The FMI road weather model
A road weather model has been developed to forecast specific road weather related variables. The main aim in model development has been to provide FMI meteorologists with tools to determine the need for traffic condition warnings. The model not only predicts the road surface temperature but also produces a road condition interpretation which it further processes to give a three-level traffic condition index to issue traffic warnings. Moreover, road surface friction is determined. The model development has led to various spin-offs, including novelty model versions for road maintenance, pedestrian sidewalk condition forecasting and intelligent traffic applications.
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The application of METRO model to the Czech road data – preliminary results
The goal of this paper is to adopt the METRo model to Czech road data and to present the first experiences. The METRo, a physically based model developed by the Meteorological Service of Canada, produces a 30-hours forecast of road conditions and its temperature. The METRo requires measurements from the road weather stations and forecasts of a numerical weather prediction model as an input data. This first test was performed with road data for the Svojkovice station located at 70.3 km of the motorway D5 (Prague-Plzen) and the ALADIN-CZ NWP model forecasts were used. The test was performed for data from the winter 2009/2010. The forecasted surface temperature yielded higher values comparing to the measured ones during the daytime and lower values during the night time. These differences were more pronounced when considering the beginning (October) and the end (March) of the winter season only as a probable impact of high insolation. The accuracy of the forecasted road conditions expressed by the code specifying road conditions ranged between 65 and 80% for all lead times of the forecasts.
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Assessing the urban waterlogging risk under rainfall condition in Shanghai
This paper focuses on the effect of weather and drainage conditions on urban waterlogging risk. The main objective is to examination whether or not weather rain condition impacts urban area and road in Shanghai. This general examination is a contribution that allows policymakers to assess the appropriateness of local traffic management strategies. To achieve this goal, the paper analyzes urban accumulated water and drainage data of over 260 different parts in shanghai with a historical series of ten years to find out the relationship between rainfall and ponding depth at the different traffic locations with the heterogeneity of the weather effects. Thus, six levels of urban waterlogging risk are set up with six different colours. With the statistical relationship between rainfall and ponding depth, AWS rain gauge data, radar QPE and NWP rainfall data are engaged in to predict urban waterlogging risk condition in Shanghai. The results indicate that rainfall increases the traffic intensity, and the perdition or warning information of urban waterlogging risk is useful to diminish traffic intensity. Further findings might be possibly achieved by studying rainfall impacts on local roads and by shifting the focus of research toward local human travel behaviour.
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Wind gusts and local gust forecast for Icelandic Roads
At present the development of gust forecasts is underway at the Icelandic Road Administration. The goal of the project is to analyse dangerous wind locations, especially by steep mountains and to increase the public service. By forecasting gusts the public will get better information of local winds at specific locations, and a forecast for the next few hours for the places where dangerous katabatic winds are common during certain meteorological conditions. Over 60 places of strong wind gusts are investigated. Wind measurements are available for more than 10 years for some of these locations. The wind data compared with winds higher aloft, mainly at the 850 hPa level are the base to a probabilistic gust model (30 m/s) for 9 locations.
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