PROCEEDINGS OF THE 16TH SIRWEC CONFERENCE, HELSINKI, FINLAND
23-25TH MAY 2012
SESSION 1: RWIS SENSORS AND EQUIPMENT
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.
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 vehicles 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 vehicles 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.
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
K. Greening, D. Johns, P. Bridge, R. Koerberlein
Abstract | PDF | Presentation not available
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.
SESSION 2: WINTER ROAD FORECASTING TECHNIQUES
Multivariate data analysis. A new insight for thermal mapping
M. Marchetti, I. Durickovic, G. Derombise, J. Bouyer, M. Moutton, S. Ludwig, F. Roos, F. Bernardin, M. Colomb
Abstract | PDF | Presentation
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.
SESSION 3: WINTER MAINTENANCE METHODS
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.
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.
SESSION 4: DECISION SUPPORT SYSTEMS
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.
SESSION 5: BEST PRACTICE AND USER EXPERIENCE
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.
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.
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 Researchs (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.
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.
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.
Route- based winter road weather forecasting method by using GIS
Y. Hu, T. Gustavsson, J. Bogren
How to keep track on energy use within road maintenance operations
Abstract | Paper not available
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 analyzing 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.
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.
FMIs 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 modeled road surface temperature is compared to road weather observations in the winter of 2011-2012. Road weather temperature modelled by SURFEX and FMIs road weather model are studied. Also, the modeled 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.
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.
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 authoritys 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.
FMI has developed a pavement condition model which a special version of the FMIs 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.
Features of snow cover distribution in the southern Ukraine
Abstract| Paper not available
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
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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