PROCEEDINGS OF THE 15TH SIRWEC CONFERENCE, Quebéc City
05 – 07 February 2010
Road climate within a few centimetres above the road is, depending on the weather situation, different from the climate above. In clear cold weather, a strong temperature inversion is created near the road. The road temperature drops significantly and the air immediately above the road cools. The cooling of the air means hoar frost formation on the road, but it also means that the amount of humidity in the air is lower in terms of the dew point value. Therefore the vertical dew point gradient will be positive and some difference between the dew point in 10cm and the dew point in 2m or higher should be expected. The Danish Road Directorate has performed measurements to prove the relevance of measuring dew point temperature 10cm above the road. The results indicate that these measurements should be taken into account when considering possibility of frost formation in certain weather situations. The results are presented and the related weather situations described.
1. It allows to detect the microclimatological specification of each single station, which is especially important with road weather stations.
2. Problems of sensors are encountered immediately by the sophisticated quality control module, so that measures to avoid misinterpretation of data can be taken.
3. The analysis and downscaling module allows to make maximum use of all sorts of additional information like satellite radiances, radar or thermal maps.
4. In the model validation mode the system can detect even minor model derailments and hence allows to adjust short range forecasts towards the observed state.
5. In a climatological mode the system allows to retrieve valuable information for planning purposes with respect to new road routing or weather related road maintainance.
6. In the lecture an outline of the method will be presented together with case studies, which demonstarte the potential of the system for road weather applications.
Friction coefficient is a well defined and unique quantity to describe grip of car tires to road surface. Loss of adequate grip will have dramatic consequencies for maneuverability and stopping distance. A thin layer of ice, which may have developed on road surface either by freezing, precipitation or condensing, can reduce friction coefficient easily by a factor of three to four causing the braking distance to increase by the same factor. It is one of the main tasks of winter maintenance to avoid situations developing to slippery surfaces. Spreading of deicing chemical prior to freezing is a widely proven method to keep friction coefficient high enough for safe driving. Practical measurements of friction coefficient reveal that if deicing chemicals have been applied in due time, a fair part of the solution may still freeze, but in this case the freezing does not cause a dramatic reduction of friction. There is a simple reason for this behaviour. When a solution of water an d deicing chemical freezes, the forming ice is mechanically softer than clean ice. Thus friction coefficient can stay at a much higher level compared to the case without any deicing chemicals. It turns out also that the actual amount of ice can be much higher with soft ice than with hard ice and still the friction is better. Usage of deicing chemicals to prevent slippery surfaces can be optimised by letting surface to partially freeze but still keep friction high. This is done often unintentionally by assuming that the surfaces are just moist or wet without any ice due to the chemicals. Nevertheless, it is essential to know the development of surface friction in these cases to prevent slippery surfaces due to increasing ice amount with dropping surface temperatures causing finally a slippery surface. A demonstration measurement of friction under different surface conditions is presented.
Practical means of measuring friction on highways can be divided to methods of (1) measuring mechanical grip force directly, (2) measuring deceleration while braking or (3) indirect means measuring amount of water and ice. These means are reviewed with a discussion of advantages and limitations including a comparison of measured response of two different acceleration based approaches to measure friction.
1. an air temperature of =- 2°C and a wind velocity of > 5.0 m/s, or
2. an air temperature of = 0.5°C and a wind velocity of > 7.0 m/s.
When meteorological conditions meet these criteria, the transport rate of blowing snow Qt (g/m/s) is calculated using the following empirical expression: Qt = 0.03 * V1 ^ 3, where V1 is the wind velocity at a height of 1 meter (m/s). Accumulated snow transport was derived by summing up the Qt values for the duration of the blizzard. Using this method, the amount of accumulated snow transported during the April 2008 blizzard in eastern Hokkaido’s Attoko area was calculated as 1,540 kg/m. The return period of the accumulated snow transported during this blizzard was then calculated using the Iwai method. The results indicated that the return period for a blizzard of this scale was 1,000 years or more.
The analysis of the results of measurements obtained by remote road surface state sensors, embedded road sensors and observations of an expert is made. The outcome data are assessed on the basis of their suitability for further analysis and the forecast of road conditions, and are also assessed from environmental point of view. It is important to mention that the analysis focuses only on road temperature and condition. The methodology includes the data acquisition, the selection of parameters and the statistic analysis of the data. From January till April 2009 there have been approximately 5000 pairs of measurements acquired and approximately 100 observations made on a selected highway section in Slovenia. The results of the analysis are presented graphically and in tabular form.
The results of the road temperatures show a high degree of correlation, especially in a critical temperature range around 0 °C. Larger temperature deviations occur mostly in the morning. When the correlation with the meteorological parameters was made the results showed that the days with larger temperature deviations are always correlated with a high solar radiation and vice versa – when the solar radiation is low the temperature deviations are smaller.
Overall, the results of the comparison of road condition are in general satisfying. The deviations which occur in critical weather situations have been examined thoroughly and are in the paper also presented through observers’ photos. Most of the deviations between remote road surface state sensors and observers occur when remote road surface state sensors indicate a dry road whereas observers claim that the same road is moist (and vice versa).
The results of the comparison of remote road surface state sensors and embedded road sensors also present their advantages and disadvantages. The advantages of remote road surface state sensors are: they are less sensitive to solar radiation, they take measures on a larger road surface, it is easy to install them. These sensors do not measure salinity but they do give results of a new parameter (grip). At present no direct connection between the grip parameter and the systems which provide information that is needed to make winter road maintenance decisions is possible. The results also show that additional temperature measurements in the depth of the road are required when using remote road surface state sensors in order to get the same forecast of road condition as with a physical model.
While there are numerous ways in which a forecast may be in error, some of these errors pose more of an operational challenge in the area of winter service than others. In particular, errors in prediction of storm start times, of precipitation type (snow, sleet, freezing rain), and surface temperature can have significant impacts on operational actions and on road surface conditions. These operational impacts are explored in detail.
Given the inevitability of forecast errors, it is imperative that decision support systems be sufficiently resilient to allow for the errors. This resilience can be achieved in one of two ways. First, the link between decisions and forecast can be somewhat decoupled, and second, feedback of real time information on road surface conditions can be enhanced. Decoupling the link between forecast and decisions can be done either manually or automatically. If done manually, essentially the supervisor using the decision support system can adjust the forecast so as to examine how small changes in forecast behavior may or may not require radical changes in operational actions. If no radical changes are observed, then small errors in the forecast will likely have little effect. This process can be done automatically, but if so then the forecast must be expressed in such a way that it can be represented by way of a matrix. Then the forecast can be adjusted from that given by moving one “cell” in each direction for each “dimension” of the forecast matrix. This idea is explored further in the presentation.
Feedback on road surface conditions is important for decision support systems for many reasons, not least of which is that such feedback may help to improve forecasts over time. However, in the context of this presentation, the feedback may be of special value since it provides an outcome oriented measure of the accuracy of the road weather forecast. If the forecast is correct and the specified actions have been taken, then the road surface should be in an acceptable condition. If it is not, then either the forecast was lacking or the appropriate actions have not been taken. Either way, corrective action can be taken. However, such feedback should, ideally, be obtained through automated means rather than requiring operator input.
In 2001, the Federal Highway Administration (FHWA) initiated a program in an attempt to address the road weather related challenges associated with winter maintenance operations. Through this program, the Maintenance Decision Support System (MDSS) was created. This system is designed to provide winter maintenance managers and crews with objective guidance regarding the most appropriate treatment strategies to employ during adverse weather events. The MDSS has evolved considerably over the last 8 years and although it is sill being used primarily for winter maintenance, the concepts are now being applied to non-winter decision support systems aimed at helping practitioners make non-winter maintenance decisions, such has when to pave, install new signs, and mow or weed along the roadside.
A key requirement of any road decision support system is the ability to provide accurate, precise, and timely road condition forecasts. For the past 3 years, NCAR’s MDSS has utilized the Model of the Environment and Temperature of the Roads (METRo), which is a physically-based numerical model developed by Environment Canada. METRo has proven to be a reliable and accurate pavement model across most winter situations, but is relatively untested for non-winter operations. With funding from the FHWA, NCAR is now in the process of developing a non-winter decision support system (XDSS) that will utilize METRo to forecast summer pavement conditions.
This paper describes the use of METRo as a pavement model within the MDSS and XDSS framework as well as its use as a tool for determining road-temperature quality control (QC) values. Discussion topics include, but are not limited to, improvements made to the model by the METRo developers, improvements in the implementation of METRo in NCAR’s realtime systems, and the use of METRo in determining quality-check (QCh) values for Clarus road-temperature observations. Verification results will also be presented showing METRo’s performance during both winter and non-winter months. Based on these results, recommendations will be made on how METRo can perform better to the serve the needs of both a winter and non-winter maintenance decision support system.
A winter maintenance decision support system, in the context of this discussion, is an interactive, automated system capable of providing winter maintenance personnel with objective guidance regarding options on how to treat networks and routes when hazardous road weather conditions are imminent or exist. The guidance supplied by such a system is based on best practices for anti-icing and deicing, along with road condition analyses and predictions. Eutectic properties of chemicals can also be used in place of best practices (i.e., estimating and tracking chemical concentration and performance). The system automates the procedure of obtaining, synthesizing, and applying road weather data and information in the decision-making process. Guidance provided by the system can include information regarding treatment timing, rate, location, and type. Optionally, an end user can interact with the system to ascertain the consequences of action or inaction.
In order for a decision support system to fully meet the needs and requirements of winter maintenance engineers, it should include some fundamental components, as this will enable the system to be optimized for operations. First, the application should have the capacity to retain data about the road network of interest. These data include, but are not limited to, the as-built properties of key routes, available maintenance resources, and maintenance practices unique to the Authority being served. Second, an application should possess the ability to ingest and process observations from environmental sensor stations, as well as forecast data. Together, these data can be used to drive an energy and mass balance road condition model, providing insight into the current and future state of road weather conditions. Treatment recommendations are derived through a separate module that processes and analyses the observations and forecasts in conjunction with winter maintenance best practices. Clearly, there will be a need for any system to access the most accurate data available, both observed and forecast, to ensure that these recommendations are sensible. Finally, the end user should be able to view data and information and interact with the system through an intuitive graphical user interface. A decision support system that uses the aforementioned components as a foundation can effectively act as a “one-stop-shop” for the winter maintenance engineer, since it ingests and processes all the available information to produce succinct, repeatable guidance.
This paper reports on the main conclusions of the state of the art and practice research activities conducted as the Action’s initial major activities during its first year of existence. First, the meaning and conception of “adverse weather conditions” and specific definitions of weather events considered as “adverse” in relation to road traffic are explored. Thereafter, recently compiled research reports and good practice cases are summarized based on the state of the art report covering the perspectives of selected EU countries. Finally, identified research gaps and needs are introduced as basis of future research work and collaboration within the Action TU0702. As an example of the needs, more comprehensive databases are called for to be able to analyze and utilize a wide range of weather event intensities with an objective to identify which types of traffic parameters are affected by given weather events. A further research need deals with the integration of the outcome of such studies into eventual traffic models. More specifically, research effort should be put in designing weather-dependent traffic flow models, i.e. developing basic guidelines on how to integrate weather information into traffic simulation models and, ultimately, into traffic management strategies. Preliminary results of such efforts will be presented.
To find answers to these problems, project ROADIDEA “Roadmap for radical innovations in European transport services” was initiated. It is a European Commission co-funded Collaboration project that started in the end of 2007 and continues until mid-2010, see www.roadidea.eu. The main objective is to study the potential of the European transport service sector for innovations, analysing available data sources, revealing existing problems and bottlenecks, and developing better methods and models to be utilized in service platforms. These will be capable of providing new, innovative services for various transport user groups.
The fourteen partners of ROADIDEA come from Finland, Sweden, the Netherlands, Germany, Italy, Hungary, Croatia and Slovenia. The differences of the existing transport systems and available data sources in these countries are analysed as well as the problems caused by local climate and geography. The innovation process is key activity of the project. It has produced more than 100 ideas during two consecutive brainstorming seminars. Ideas have been analysed and the most potential ones shortlisted for research and development. Best ideas will be described in detail, including e.g. advanced friction models, road condition and fog warning systems, which receive input from hybrid (mobile and fixed combined) observing systems and automated messages from private cars. The Hamburg Port with increasing congestion needs multi-modal traffic model that takes weather into account. New semi-public high-quality ways to travel need to be innovated to reduce the use of private cars in the battle against climate change.
ROADIDEA is also extending outside Europe through an international cooperation project with FHWA and its Clarus initiative in the USA, and Environment Canada. Innovation seminars will be conducted and results disseminated to local stakeholders.
Methodology: With the experience of the DLR clearing house for transport data a data survey questionnaire was designed. This questionnaire was used both to identify data sources which are available in the project member states and which are ascertained and utilized by ROADIDEA project partners. This data source identification covered the following initial main fields:data from vehicles; data from infrastructure; weather monitoring.
More detailed data source analyses took place in Germany, Finland, Croatia and Italy. The focus of these analyses was to compare general data availability in different European countries with special respect to the degree of development in terms of data policies and to determine a data archiving and mediation model. With the data source description among more general elements data access possibilities, licence fees but also data quality matters were considered thoroughly. While describing, comparing and analysing the data a very good understanding of its contexts and its contents were obtained. Based on these analyses a project specific data mediation architecture was set up. It follows a generic attempt to connect available raw data sources with service provision data needs via a data mediating architecture.
Results: This paper emphasizes on the main data related findings and results of the project ROADIDEA, which could be from interest for the SIRWEC activities. During the data source identification 55 data sets have been described, partly supplemented with related documents as codebooks, legends or data samples. Based on the data archive a tailor-made XML data scheme has been generated. This scheme is used within the data mediation architecture. Both the scheme and the mediation architecture are described with this paper. Using the ROADIDEA mediation architecture some simple examples give an idea how collected information have been used for the implementation of project pilots as well as for theoretical models, which have been developed within the project in order to combine traffic and weather data.
The analysis shows that weather has an obvious impact on traffic and also that it is possible to build a model with the ability to recognize the weather (with weather history), which affects traffic in a negative way. These findings can be used for future development of new information systems. This paper describes a method for modeling weathers impact on traffic, as well as the results obtained when applying that method. The analysis comprises preprocessing, a method for visualizing the effect of weather on traffic parameters (velocity and speed per time of day) and also model building via a decision tree classifier. The visualization is applied to build a dataset with classified samples; “traffic disturbed by weather” or “normal traffic”. A decision tree classifier is used to train models to recognize the combinations of weather parameters that lead to disturbed traffic. The visualization shows a distinct correlation between precipitation and changes in traffic pattern and the decision tree models have a good/useful performance.
The Finnish road weather station network operated by the Finnish Road Administration covers c. 500 stations in Finland. Almost 100 of them are equipped with optical sensors (as of autumn 2009). The number of these optical devices has increased remarkably during the past few years. The devices in use are Vaisala DSC111 sensors which measure the depth of water, snow and ice on the road surface, producing also an estimation of road condition and friction. Friction is one of the parameters to define the so-called road weather index but until now there have not been any tools to forecast it. Measured friction values and other road weather observations at given observing stations have been used to define a statistical friction model introduced here. The condition of car tires and the road surface may of course have significant impacts on friction but they are not covered in this application. The data for the friction model were the road weather observations measured at the Utti station during winter 2007-2008. Utti is located in southern Finland, c. 50 km from the coastline. A correlation analysis between the observed friction and the thickness of water/ice/snow was performed, and a strong correlation was found under icy and/or snowy road conditions. There was also a small temperature dependence. Water on the surface was found to reduce friction, too, but in cases of wet or damp surface there was no temperature dependence. The resulting model defines friction as a function of the road surface temperature and the thickness of ice and/or snow on the surface, when ice and/or snow exist. For a wet or damp surface, friction is calculated based on the thickness of the water layer, only. The statistical friction model is further linked to Finnish Meteorological Institute’s (FMI) operational road weather forecast model. The initial results look quite promising with the correlation between observed and modeled friction under icy/snowy conditions being 0.85 when adapted to independent data from the Utti station during the following winter, 2008-2009. Under wet/damp road conditions the correlation was as high as 0.93. The model will be further tested in an operational forecasting environment by FMI during winter 2009/2010, when the model output will be forwarded both to duty meteorologists and road maintenance authorities. The forecast model will eventually undergo a comprehensive verification undertaking after the winter season (see the follow-up Abstract by Nurmi et al).
This study was carried out within the EU/FP7 Project ROADIDEA, where the major goal is to develop new and innovative products and tools for traffic and transport sectors.
The visibility predominantly depends on concentration of particulate material in the air above the roads surface that can be well represented by measured PM10. More problematic is to describe the slipperiness since the physically defined friction coefficient does not correspond to the property that is perceived by drivers as the slipperiness. In the framework of Swedish research project SRIS (www.sris.nu) the estimation of the slipperiness was therefore performed by a fleet of expert drivers. For this purpose they have utilized personal feelings during driving, as well as data provided from a network of ABS and ESP sensors. However, such an intuitive estimation leads to a serious problem if we want to develop an automatic information processing system that could forecast the driving conditions based upon weather forecasts. In our approach this problem was solved by development of an intelligent computer program that learns from data obtained by previous observations to estimate the slipperiness from the environment state with a similar accuracy as an expert driver.
The program utilizes a statistical basis of joint data about the state of environment and driving conditions. At a forecasting of driving conditions the computer first obtains new data about the environment and then compares them with the corresponding ones in the data base. Based upon their similarity, the associated stored data about the driving conditions are then accounted in the forecasting of new driving conditions that are non-parametrically expressed by the statistical conditional average. Such a procedure corresponds to an optimal statistical estimation and resembles associative estimation of unknown properties from given sensory and memorized signals in neural networks of intelligent living beings.
The performance of the corresponding forecasting method is characterized by the correlation coefficient r between estimated and really observed data. In the article we demonstrate the performance on the forecasting of driving conditions by using slipperiness data provided by the SRIS project in Sweden and data about PM10 in the Po valley provided by ARPAV, Centro Meteorologico di Teolo, Italy. Relatively high values of correlation coefficient (r~75%) indicate that the proposed method is applicable for prediction of hard winter driving conditions.
This system provides the opportunity to virtually “be on duty” and handle historic weather situations, while learning to navigate the RWIS-system, scoring points answering theoretical questions and retrieving important and case-relevant information from the RWIS.
You can say, that a “state of the art” RWIS system not worth much, if the end-users doesn’t now how to use the system and interpret and analyse the cascades of information and output. Of course you also have to take this question into account when you design the RWIS system and educate users.
In these years, when increasing amounts of information is freely available on the internet, it is of growing importance to give the users a realistic relation to uncertainties and the quality of numerical weather modelling.
It is of great importance, that the users of the RWIS system quickly become familiar with improvements and new functions in the RWIS system. Such information must reach the users continuously. Feed back from users is each year collected, and on a one-day seminar every year future improvements and developments are discussed with the end-users.
On the other hand it is also important that the meteorologist on duty is familiar with the aspects and issues the end-user have to take into account, when deciding weather to – or not to – initiate actions on the roads. A discussion of special education of forecasters – regarding the operational aspects of road maintenance – is also included in the presentation.
Our standard road model is able to create a forecast for one specific location. From infrared measurements, we know that large local differences in road surface temperature can exist on a route. Differences can be up to 5 degrees Celsius over a distance of several hundreds of meters. Based on these measurements, the idea came up to develop a system that forecasts road surface temperatures and conditions for an entire route: route based forecasting. The route is split up into sections with equal properties. For each road section a surface temperature and condition will be calculated.
The main factors that influence the road surface temperature are modelled in this forecasting system:
1. The local weather conditions: temperature, dew point, wind, precipitation, weather type, cloudiness.
2. The sky view: a very sheltered place will receive less radiation during daytime and will emit less radiation during nighttime. For a very open spot, the effects are reversed.
3. The solar view: a road section with trees on the southern side, will receive less solar radiation during daytime than a section without trees on the southern side.
The route based forecast shows, by means of a Google Maps presentation, which sections will be slippery at what time during the coming night. The final goal of this type of forecast, is to make dynamical gritting possible: a variable salt amount and a different gritting route. This will contribute to safety on the roads (colder spots will be treated earlier) and it is also financially interesting (less salt necessary and fewer kilometers to drive).
The present paper aims at showing the validity of the model by the comparison between the computed outputs and the experimental results. In addition, the applicability of the model is also discussed in this paper. The following conclusions are drawn: The extracted ground heat is uniform in the longitudinal direction of the HUT, as long as the present flow rate is concerned; The relation between the HUT Nusselt number, Nu, and the HUT Reynolds number, Re, is given by a power function and Nu increases with Re. The extracted ground heat is also given by a power function of Re.
The experimental results allowed the proposed model to predict the ground heat extracted by means of the HUT precisely.
1. Collection – Services for collecting data from different weather station and converting data into appropriate form for input in central system
2. Evaluation and processing – Service for evaluating data against different bounds and alarm conditions, insertion data into central database, informing contractors about triggered alarms
3. Presentation – Web based application for reviewing data (current and history weather data, detailed previews with charts, previews of weather station equipment and its condition, system administration and tools for statistic previews and data exchange)
For the general users the most important data are shown on electronic information boards. Therefore a GeoRSS syndication was developed, which includes current weather data and alarm situation.
In Japan, thermal mapping has been used to assess road surface temperature properties and identify sections of routes prone to freezing since the technique was introduced in the early 1990s. It is known from the results of such mapping that road surface temperatures vary greatly by section even on the same route, and that the characteristics of this distribution differ widely depending on weather conditions and hour. Thermal mapping results can be divided into extreme, intermediate and damped depending on weather conditions. Past studies have indicated that these three conditions correspond roughly to G, F, E and D of the Pasquill stability classes used to categorize atmospheric stability. In this study, a map of road surface temperature distribution at night was created for each of the Pasquill stability classes (representing atmospheric stability) to enable precise prediction of road surface temperature distribution variations by weather conditions and hour. The level of prediction accuracy was verified by comparing the distribution obtained from these road surface temperature distribution maps and the results of thermal mapping conducted the previous winter.
SESSION 6: WINTER MAINTENANCE / COST BENEFIT
The primary purpose of the mobile control centre is to have a closed loop between measurements before winter maintenance actions (salting, snow removal, etc.), initiation of actions and control of the result of these actions. The secondary purpose is to give the operators in the Winter Maintenance Centre an opportunity to experience the situation for the drivers on the roads both in normal situations and under slippery conditions. In the long term these experiences will give a better interpretation and understanding of the measurements from the stations.
This means that the mobile winter maintenance centre must be capable of measuring the surface condition of the pavement (before and after winter maintenance actions) as well as having installations for initiating winter maintenance actions. The rolling winter control centre is capable of conducting measurements of surface friction, surface temperature and surface state (icy, wet, dry ). In the first stage DSC111 and DST111 sensors are used for these measurements. Once it has been established as a fact that winter maintenance action(s) should be initiated, the rolling control centre has communications tools that allows it to call these actions. These tools are:
1. Wireless broadband connections to the Internet
2. RoadWeather (presentation system for observations and forecasts from measuring stations)
3. Winterman (the Danish Management System for Winter services)
All measurements and actions will continuously be saved in a database as documentation. This paper describing the considerations about design of the mobile winter maintenance centre, and experiences gained during the implementation of the mobile winter maintenance centre. The paper also presents experiences from the first practical use of the mobile winter maintenance centre.
Approximately ten percent (10%) of INDOT’s snow fleet was outfitted with onboard mobile data collection (MDC) units and cameras. Forecasts, recommendations specific to road segments, current weather conditions, truck locations, applicaiton rates, camera images of road conditions, and driver observations were all transmitted in near real time to managers, the Traffic Management Center, radio dispatch, and others by a graphical user interface. Combining all these elements with intensive training and a shift in maintenance culture, a live big picture of the entire state’s maintenance operations was available to employees at all levels. This data, from forecasting to real results, assisted decision makes from the first call out through the final application of materials. By implementing an MDSS to aid management decisions, INDOT decreased its salt usage by over fourty percent (40%) from the previous year, overtime related to snow and ice dropped by twenty-five percent (25%), and diesel fule consumption by nearly fourteen percent (14%). These remarkable results saved over $12 million for the taxpayers of Indiana and placed INDOT as a nation leader in MDSS implementation. This innovation and extraordinary savings combined technology and maintenance personnel to form a new best practice that INDOT is continuing to use and expand.
A system has been designed to detect ice formation on roads. The system uses an infrared thermometer which makes it possible to detect rapid but small changes of the road surface temperature while being non-intrusive. Non-intrusive sensors have the advantage of easier maintenance and installation and do not require the maintenance personnel to operate on the actual road. The system can warn maintenance personnel of hazardous conditions as well as the driving public by the use of active warning signs.
A preliminary study carried out in 2008 marked the start of the overhaul process. The aim of the preliminary study was to produce an overall picture of the system of roadside devices producing information and the administrative model supporting it in the coming years. The study was thus intended to provide a basis for the overhaul project planned for the next few years. The preliminary study was carried out by a consultant who was familiar with the Finnra’s road weather information and traffic management systems. Finnra experts were closely involved in the work through interviews and workshops.
The preliminary study involved the identification and description of the following 12 key elements of the system of roadside devices: Collecting information using roadside devices, collecting information using sensor vehicles, using information produced by information systems of other organisations, exchange of information for the purpose of producing numerical road weather forecasts, producing road and other weather forecasts and radar and satellite images, calculating information for variable road signs, controlling the cameras, database services, archiving information, distributing live camera images, presenting operative information, and reports and statistics.
A number of open questions remain concerning the administrative model. The model should therefore be further discussed and clarified as part of or in connection with the requirement-specification project (next stage of the overhaul process). This is because the work on the administrative model and the system entity must proceed in tandem.
Using the recommendations of the preliminary study as a basis, a project for specifying the information collection and storage requirements was launched at the start of 2009. The six-month project helped to produce an overall picture of the situation by focusing on the technical system entity and its interfaces, by outlining a supervision and administrative model for the service, by describing the life-cycle model and the introduction plan of the service, by determining service-level requirements and by charting impacts on other systems. The project also produced recommendations for the next stages of the project.
The process for specifying the requirements for calculating the information for variable road signs was launched parallel to the earlier project in spring 2009. The project involved the determination of the information-calculation system, which was done by describing the technical architecture, the database and the links with other systems, by charting operational requirements and the manner in which the calculations are carried out and by determining the configuration of the calculations. Furthermore, the amount of data and the number of users and the response time and performance requirements were charted and the stages of the service introduction and administration and maintenance during use determined.
The next stage will involve the specification of the requirements for the presentation and reporting system. It is essential to ensure that the different elements form a well-functioning entity, which works in unison even if the individual elements had been acquired and implemented as independent entities. It is clear that a model which is based on purchasing the system elements as outsourced services poses additional requirements and challenges to the adjustment work.
The first innovation seminar was held on 12-13 May 2008 in Prague. There were altogether 36 participants in the seminar including 3 members of the Steering Committee and the Coordinator of another INFSO project. The seminar was conducted by using various innovation methods, such as group discussion, brainstorming, pub session, Socratic Walking seminar, and deliberative evaluation with basketing (categorisation). The results include 34 fully studied ideas, of which 19 were short-listed after the evaluation. From these, 12 ideas were chosen for further work with dedicated idea teams, consisting of 6 piloting ideas, 3 modelling ideas and 3 general development ideas. All ideas are discussed through dedicated wiki-software in www.roadidea.eu.
The second seminar was held in Dubrovnik on 14-15 May 2009. The participants consisted of 24 members of the ROADIDEA consortium partners and 8 other partners who represented parallel projects, Steering Committee and one industrial designer acting as the chief evaluator. The results of energy scenarios produced by the Millennium Project Global Delphi Process in 2008 were used as alternative futures for operational environment in 2030. The futures workshop consisted of brainstorming in three groups, pub session, group discussions and two evaluation cycles. There were 13 ideas shortlisted and evaluated to find 5 best ideas as radical as possible for the future in 2030. The 5 best ideas can be further developed by using their wikis in www.roadidea.eu.
Recently, a device has been implemented to process the forecasts issued by the three weather centres and store them into the DWH; in the future, the predictions will be used by the RFT which will guarantee the coherence between various forecasts. Finally, an automatic verification of the forecast will be carried out daily.
1. March is the most dangerous month in Sanandaj – Hamedan road in which gales flow (with the speed of more than 10 m/s).
2. January is the most dangerous month for the visibility of less than 1000 m, frost and snowfall.
3. Among the atmospheric instabilities, most accidents have occurred under cloudy weather.
4. The most accidents occurred in March whose rate is 22.4 percent.
5. The most accidental part sheet in Sanandaj- Hamedan road is the first segment (1-5 km from Sanandaj).
The snowfall induced by open water is called sea-effect snow (or lake-effect snow). It occurs quite commonly for example in Japan and especially at the Great Lakes of North America, where huge amounts of snow can be accumulated in the downwind coastal areas by quasi-stationary snow bands. Also in Scandinavia, cold easterly flow over the Baltic Sea can cause significant convective snowfall at the Swedish east coast, bringing trouble to the traffic.
In Finland sea-effect snowfall can occur at the west coast in a northwesterly flow over the Gulf of Bothnia or at the south coast in a cold air advection from southeast over the Gulf of Finland. Usually the accumulated snow amounts are not very big, but for example in the Helsinki metropolitan area, where the traffic is quite heavy, sea-effect snowfall during cold and basically dry conditions can be a surprising event.
In this study two sea-effect snowfall cases were investigated. They occurred on 20 January 2006 and 8 February 2007 and both days were winter’s peak days for traffic accidents in southern Finland. Due to the statistics of Finnish Motor Insurers’ Centre, the number of crashed cars was 371 in the first case and 219 in the latter case in the Helsinki metropolitan area and surroundings (Uusimaa County). Compared to this, the average daily number of crashed cars in the same area was 79 both during winter 2005/06 and winter 2006/07 (November – March). In both cases the sea-effect snowfall occurred in very cold conditions, air temperature being -10-20 oC. The Gulf of Finland was still partly ice-free, so heat and moisture was transferred into the atmosphere from the sea surface, causing cloud formation and snowfall, which hit the coast in a southeasterly air stream.
Sea-effect snowfall is challenging to predict in advance although high resolution numerical weather models can nowadays predict a portion of these snowfalls. Radar and satellite images play a major role in observing these phenomena. A general rule for the occurrence of sea-effect snowfall is that the air temperature at 850 hPa pressure level (at ca. 1.5 km height) should be at least 13 oC lower than the surface (water) temperature. This instability allows convective cloud formation and the resulting cloud bands are typically oriented along the steering wind in the lower troposphere (0-3 km height).
The number of households affected by power failures was estimated to be over 70 000 during the 10 November storm, and at least 41 000 households during the blizzard. The building damage was similar in both cases and included mostly detached roofs and failing scaffoldings. Several people were trapped in elevators during power failures.
During the 10 November storm, the total number of Rescue Services rescue operations was four times normal figures as the storm resulted in 801 rescue operations. In comparison, the 23 November blizzard induced 534 weather related rescue operations resulting in a doubling of the typical total number of operations in the affected area. In both cases 54% of the weather related rescue operations were falling trees on roads. While the 10 November case yielded six traffic accidents of a car crashing into a tree blocking the road, the 23 November blizzard had 20 such cases. In the blizzard case, the number of Rescue Service reported traffic accidents was high, 145 cases, in which altogether 54 persons were injured and one person died. In comparison, during the 10 November case, Rescue Services reported 6 injured persons during the event.
Snowfall is the dominant factor causing slippery road conditions. On 23 November the heavy snowfall caused a rapid decrease in road surface friction. Based on Road Weather Information System data in the Helsinki metropolitan area, the friction values dropped from ca. 0.8 to 0.2 or less and stayed at this level for almost 12 hours in spite of maintenance actions. In addition to the low road surface friction, the visibility was very poor and also the strong wind had a negative impact on driving conditions, likely increasing the number of traffic accidents.
With regards to modern road weather information systems RWIS it is important to be able to rely on a technological approach that combines easy installation, reliability, accuracy and easy service with low live time cost so that the necessary coverage of the measurement grid is ensured.
There are innovative measurement procedures, like the measurement of the different kinds of precipitation as well as the intensity via a radar-doppler method, which is operating in the microwave-range. This method avoids the disadvantages of optical or mechanical methods, which are otherwise currently used. Microwave-radar methods are also being used when measuring the coverage of the street surface and are reaching accuracies which have not been obtained with the use of other methods.
Apart from the decisive progress of the measurement procedure itself, all relevant atmospheric measurement parameters (air temperature, dewpoint, precipitation, wind and pressure) have now been integrated in one compact and intelligent measurement unit. The same applies to road sensor which is built into the road surface or instruments for non invasive surface detection. That is how it is possible to construct a complete road weather station out of only 2 sensor devices. The intelligent measurement instruments supply all measured data via a digital (serial) standard interface in the required measurement units, which in turn do not need further link processing or interpretation. Through this open standard data communication this new compact and smart technology can be integrated independently from the manufacturer into applications like MDSS or ITS as well as it can be integrated into various systems architectures.
On top of that there are compact energy saving communication modules, which are connected via a field bus and which support all common international data protocols (TLS, NTCIP, XML etc..) and which can be joined via various communication media such as (LAN, WAN, GPRS, UMTS, GSM etc.). The article describes the basic concepts of intelligent measurement procedures and the main technological progress in the area of modern road and weather data collection.
Interactive database project – Meteo4U
K Kwok, G. Fricska
The Meteorological Service of Canada (MSC) at Environment Canada generates and stores a large volume of environmental information everyday. Predefined and widely available products such as public weather forecast bulletins, warning bulletins, and various numerical weather charts are on the Weatheroffice and Datamart websites and are used as our means to communicate a small subset of this information. Aside from these products, the Canadian public currently cannot easily access the majority of the data that is generated by MSC.
With recent technological advances, a more useful method to access MSC data is now being developed. Through the use of an interactive web map interface, currently named Meteo4U, the public will have the ability to choose the products they want and the ability to download data used to generate these products. Users of Meteo4U will be able to display a range of meteorological forecast data (e.g. temperature, wind speed, precipitation) by interactively defining: 1. a geographic location, 2. a travel route, or 3. an area on a map.
This project is one of the first official initiatives to use modern geospatial technologies to interactively share, distribute and disseminate meteorological data at the MSC.
The judgement and analysis of spatial uncertainty caused by the overlapping between meteorological field value and GIS data
H. Hu, C. Zhang, C. Cheng
An expertise centre for the vehicle infrastructure cooperation applied to the mobile road weather information systems
items for data collection;
ways of building and exploring databases;
decision support systems conception;
management support systems conception;
way of sending information to the road users.
The University of Sherbrooke has started up an expertise centre studying exclusively on the applicability of VIC on mobile RWIS. Discoveries could accelerate in the short term improvements for road maintenance managers, and in the longer term for road users.
Several departments of the University have been involved:
Electrical and computer engineering;
At this time, specific projects are in relation with the conception of an intelligent vehicle for road measurements, methods for data mining, the use of communication technology P25, camera images processing for surface state detection and optimal camera images compression. Links are made to include other researchers from other universities in Canada and the rest of the world. In the process of finding research topics, the expertise centre’s staff has always verified discoveries’ potential applicability.
Tools and techniques are covering a very large scope of scientific fields, from image analysis, characterisation of weather phenomena such as rain or fog. Some work has been started on the use of cameras to appreciate the visibility in fog situations. Images analysis of the road as seen from a windshield in many weather situations are treated to build visibility thresholds. The pavements textures are analysed to determine their impacts both for the grip and the induced visibility with water sprayed by vehicles tires. Some of these tools are currently deployed on experimental sites all round the French territory.
Furthermore, numerical tools are currently used to obtain conventional forecast on road surface temperature and surface status. They are either based on usual weather forecasts and observations, and the energy balance analysis. In some cases some statistical and stochastic tools are implemented when data is available and meets quality requirements. A comparison between these tools is currently undertaken to establish their limits, and improve their outputs. As an example, the de-icers incidence will be included in the numerical model, along with some thermal mapping aspects.
All these aspects are developed to evaluate and to quantify hazards that could occur on roads in adverse weather conditions. These hazards are usually identified on specific spots. But the main difficulty is the extension to a whole itinerary. Météo France recently developed OPTIMA, a French Road weather information system (RWIS). It is a precise decision-making tool for anticipation and real-time follow-up of meteorological situations on 1 km road stretch sections. It also provides, every 5 minutes, forecast for the coming hour (time steps of 5 or 10 minutes are available), over the 120 000 km of the French major road network. One of the objective is to implement within OPTIMA the results of the research work, such as grip and visibility alerts so as to inform both road users and managers, and so improve traffic. This would be possible through data transfer and meta-data relative to the road network within the tool.
Adding significant accuracy beyond what weather models alone can achieve