Road Weather Information System Research Articles

Proactive Strategy for Variable Speed Limit Operations on Freeways Under Foggy Weather Conditions

Adverse weather conditions are a critical external factor that threaten traffic safety on freeways. Variable speed limit (VSL) operations can be used as a traffic management tool to harmonize vehicle speed and reduce the likelihood of crashes during adverse weather conditions. This paper proposes a novel VSL operations strategy based on the prediction of weather and traffic conditions, referred to as a proactive VSL operations strategy. The k–nearest neighbors ( k-NN) statistic was used to predict weather and traffic conditions. VSL operations logic, which used visibility distances and safe stopping distances obtained from road weather information systems and average speeds obtained by vehicle detection systems, was developed. The proposed methodology is focused on the restricted visibility distance resulting from foggy weather conditions on freeways. A microscopic traffic simulator, VISSIM, was used to simulate a freeway traffic stream and collect vehicle-maneuvering data. An external application program interface, the VISSIM COM interface, was used to implement the proposed VSL operations strategy. A surrogate safety assessment model was used to derive indirect safety measures to evaluate the effectiveness of the proposed methodology. Total conflicts in the proposed VSL algorithm were reduced in comparison with a system lacking VSL by 19.10% and 27.27% under moderately and severely foggy weather conditions, respectively. These outcomes are expected to be of great use in the development of more effective VSL systems to enhance freeway safety during adverse weather conditions.

Cost–Benefit Analysis and Microclimate-Based Optimization of a RWIS Network

AbstractThe Road Weather Information System (RWIS) has been applied as an effective tool to collect weather and road surface temperature information and to assist operational decisions for snow and ice control activities. Current practices on siting regional RWIS stations rely on the knowledge and experiences of maintenance and operation personnel, which is subjective and inefficient. A two-stage sequential model is developed in this paper. The Stage-1 model optimizes the number and locations of RWIS stations, which intends to maximize spatial coverage with the least overlapping area, considering the variation of regional weather severity. The deployment of the optimized RWIS sites can be prioritized by the Stage-2 model, which maximizes the lifecycle profit subject to limited budget. The proposed methodology is applied to optimize a RWIS network for the State of New York.

Evaluation of possibilities for the climatic distribution of regions from the point of view of road construction

Seeking to reduce a negative impact of unfavourable weather conditions on road traffic, many countries introduce modern technologies allowing to objectively assessing meteorological conditions of roads. The world over, data from the automated meteorological stations of Road Weather Information System have been long ago used on a significantly larger scale than only for the organization of road maintenance works. International experience of introducing Road Traffic Information Systems in European Union and other countries of the world shows that Road Weather Information Systems give good results for increasing road safety, improving the level of road user information and solving the road construction issues. Road Weather Information System is a system of technologies and decision-making using historical and real-time data of roads and weather conditions. The collected and processed multi-year data from meteorological stations is a great assistance in designing or reconstructing road pavement structures. Road pavement structure is highly affected by a negative air temperature and frozen ground. The impact of negative temperature is expressed by the thickness of frost blanket course. The thickness of frost blanket course depends on a frost susceptibility of soil. To determine the thickness of frost blanket course it is necessary to assess the frost impact, therefore it was up to the purpose − climatic distribution of regions the territory of Lithuania according to the distribution of frost impact and the depth of frozen ground. Based on climatic maps compiled, a correction of the thickness of road pavement structure was suggested.

Potential of Intelligent Transportation Systems in Mitigating Adverse Weather Impacts on Road Mobility: A Review

Adverse weather conditions for roads, which cause transportation systems to perform far below capacity, can severely affect society's economic output. As elimination of road weather events is not possible, transportation agencies perform proactive and reactive maintenance activities to minimize adverse impacts to keep roadways in optimum condition. While reactive maintenance activities are conducted to clear roadways after the occurrence of extreme weather events, proactive activities minimize these impacts beforehand. The success of proactive activities solely depends on the availability of accurate road weather information, however. Traditional road weather forecasting techniques rely on governmental weather services, which are not appropriate to predict route-specific road weather conditions. In this paper, the authors reviewed current intelligent transportation systems (ITS)-based solutions for minimizing road weather impacts and possible ITS innovations to incorporate diverse data sources to improve road weather management activities. ITS-based initiatives, such as road weather information system (RWIS), allow transportation agencies obtain accurate road weather assessments. Location-specific infrastructures such as RWIS are cost prohibitive for system-wide deployments. Connected vehicles equipped with weather sensors could enhance mobile road weather data collection. This strategy could improve proactive maintenance programs and reduce adverse effects of weather to the surface transportation system.

Planning a road weather information system with GIS

The road weather information system (RWIS), which collects and monitors weather and pavement surface conditions, has been proven effective to support winter road maintenance by improving safety, mobility, and efficiency. Although the geographic information systems are being widely applied for facility siting, traditional practices of sitting RWIS stations still heavily rely on the experiences of maintenance and operation personnel, which is time-consuming and subjective. This study develops a linear model to determine the optimal RWIS locations subject to three selection criteria: weather, traffic condition, and distance to existing RWIS sites, while considering practical constraints to satisfy specific requirements of various agencies (e.g., different weights of weather/traffic factors, various available budgets, etc.).

Vehicular Networking Road Weather Information System Tailored for Arctic Winter Conditions

Winter conditions are dominating the road traffic platform in the Northern Scandinavia, and the weath er services play an important role in successful winter road traffic operations. Intelligent Road project is contributing towards tac kling this challenge and aims to support the development of bu siness community in Northern Scandinavian region by road safety provision via vehicular networking based services i n Nordic weather conditions. FMI's combined Road Weather Station (RWS)/Road Side Unit (RSU) is acting as a central infr astructural element of such vehicle-to-vehicle and vehicle-to-i nfrastructure communication platform, supported with areal infras tructure and third party vehicles equipped with weather measurement and communication infrastructure. The aim is to employ road weather systems of FMI and furthermore exploit RWS data as well as the data gathered from vehicle instrumentation. The res ulting up-to- date and localized weather data is then delivered t o the stakeholders, tourists and even the vehicles in the operating area in real-time. IEEE 802.11p based vehicular networking is the primary communication channel, supported with traditional Wi-Fi and 3G communications.

Vehicular Networking Road Weather Information System Tailored for Arctic Winter Conditions

Winter conditions are dominating the road traffic platform in the Northern Scandinavia, as the platform availability must be ensured regardless of the amount of snow, surface slipperiness or such kind of events. Weather services play an important role in successful road traffic operations, especially in the Northern areas, where the weather conditions vary substantially. Intelligent Road project is contributing towards tackling this challenge and aims to support the development of business community in Northern Scandinavian region by road safety provision via vehicular networking based services in Nordic weather conditions. The specific objective of the project was the demonstration of sustainable and marketable Intelligent Road System providing location-based short-term road weather information to the road user passing by the area. In this paper we present the service concept, service demonstration and communication field tests of such a system in Northern Finland. FMI’s combined Road Weather Station (RWS)/Road Side Unit (RSU) is acting as a central infrastructural element of such vehicle-to-vehicle and vehicle-to-infrastructure communication platform, supported with areal infrastructure and third party vehicles equipped with weather measurement and communication infrastructure. The aim is to employ road weather systems of FMI and furthermore exploit RWS data as well as the data gathered from vehicle instrumentation. The resulting up-to-date and localized weather data is then delivered to the stakeholders, tourists and even the vehicles in the operating area in real-time, using variety of vehicular networking methodologies available. IEEE 802.11p based vehicular networking is the primary communication channel, supported with traditional Wi-Fi and 3G communications.

The use of road weather information system data for the forecasts of climatic conditions

The article studies possibility by using the data of the Road Weather Information Systems to forecast the beginning of precipitation on the roads maintained by the state road maintenance enterprises. The article discusses the importance of forecasts both to the road users as well as road maintenance enterprises, especially in a cold period of the year. The hazards caused by meteorological phenomena specific of winter season are presented. Research methodology is described which is based on the speed of movement of clouds carrying precipitation. To calculate speed the theoretical background is given assisting in selection of certain parameters for the determination of this value. Knowing the speed of movement of clouds the forecasts have been made. Results of the forecasts are compared to the real parameters and it is determined in what radius the forecasts are advisable to be made.

Road salt application planning tool for winter de-icing operations

Road authorities, who are charged with the task of maintaining safe, driveable road conditions during severe winter storm events are coming under increasing pressure to protect salt vulnerable areas (SVAs). For the purpose of modelling urban winter hydrology, the temperature index method was modified to incorporate ploughing and salting considerations and was calibrated using winter field data from two sites in Southern Ontario and validated using data collected from a section of Highway 401 – Canada’s busiest highway. The modified temperature index model (MTIM) accurately predicted salt-induced melt (R2 = 0.98 and 0.99, RMSE = 19.9 and 282.4 m3, CRM = −0.003 and 0.006 for calibration and validation sites respectively), and showed a demonstrable ability to calculate the Bare Pavement Regain Time (BPRT). The BPRT is a key factor on road safety and the basis for many winter maintenance performance standards for different classes of highways. Optimizing salt application rate scenarios can be achieved using the MTIM with only two meteorological forecast inputs for the storm event – readily available on-line through the Road Weather Information System (RWIS) – and can serve as a simple yet effective tool for winter road maintenance practitioners seeking to optimize salt application rates for a given storm event in salt vulnerable areas.

Road weather information system stations — where and how many to install: a cost benefit analysis approach

This paper presents a cost–benefit based approach to the problem of finding the optimal location and density of road weather information system (RWIS) stations over a regional road network. The novelty of the proposed method lies in the models that can be used to estimate the benefits of RWIS information, including reduced maintenance costs and collisions, thus determining the optimal number and location of a RWIS network. A case study based on the existing RWIS network in Northern Minnesota in the US is used to show the application of the proposed approach. Linear regression models are developed for the annual maintenance costs and the expected number of collisions that could occur on two types of highways delineated by the existence of a RWIS station nearby. The calibrated models are then applied to individual highway segments defined on the basis of a uniform grid system to determine the expected benefit of having a RWIS station installed. These benefits along with RWIS installation and maintenance costs are converted into net benefits and then the net present value, which are then used in determining the optimal number of RWIS stations and prioritizing the candidate locations. It was found from the case study that a total of 45 stations would provide the best return of investment with a 25 year net benefit of approximately $6.5 million and a life-cycle benefit-to-cost ratio of 3.5.

Multiple-Step Traffic Speed Forecasting Strategy for Winter Freeway Operations

Accurate and timely predictions of traffic conditions are required for congestion avoidance and route guidance in real-time freeway traffic operations. Special attention to winter operations is needed because prediction error could be amplified under severe weather conditions involving snow. This study employed a vehicle detection system to propose a speed prediction methodology that used the k–nearest neighbors algorithm. The speed prediction was further evaluated under different weather conditions with a road weather information system. Cross-comparisons of the mean absolute percentage error (MAPE) between three weather conditions (normal, light snow, and heavy snow) revealed that the MAPE tended to increase with increases in the forecasting time step (T) and snow intensity. The marginal MAPE over the time step was larger during heavy snow conditions than under normal and light snow conditions. These findings indicate that for winter freeway operations, the time step should be selected dynamically, depending on the weather conditions rather than with a static strategy for all conditions. To this end, this study proposes a framework to determine a dynamic forecasting T that is associated with weather conditions.

Methodology to Forecast Road Surface Temperature with Principal Components Analysis and Partial Least-Square Regression: Application to an Urban Configuration

A forecast road surface temperature (RST) helps winter services to optimize costs and to reduce the deicers environmental impacts. Data from road weather information systems (RWIS) and thermal mapping are considered inputs for forecasting physical numerical models. Statistical models include many meteorological parameters along routes and provide a spatial approach. It is based on typical combinations resulting from treatment and analysis of a database from measurements of road weather stations or thermal mapping, easy, reliable, and cost effective to monitor RST, and many meteorological parameters. A forecast dedicated to road networks should combine both spatial and time forecasts needs. This study contributed to building a reliable RST forecast based on principal component analysis (PCA) and partial least-square (PLS) regression. An urban stretch with various weather conditions and seasons was monitored over several months to generate an appropriate number of samples. The study first consisted of the identification of its optimum number to establish a reliable forecast. A second aspect is aimed at comparing RST forecasts from PLS model to measurements. Comparison indicated a forecast over an urban stretch with up to 94% of values within ±1°C and over 80% within ±3°C.

Frost Depth: General Prediction Model

Accurate frost depth prediction is an important aspect of engineering design for infrastructure including pavement, building and bridge foundations, and utility lines. Actual frost depth is affected by material type, soil thermal properties, and soil water content as well as climatic conditions such as temperature, wind speed, precipitation, and solar radiation. Numerical or analytical modeling techniques could be used to estimate frost depth; however, the required input data are unavailable or expensive to collect. Therefore, the use of numerical and analytical models entails estimating missing data that affect the reliability of the results. The accuracy of three analytical and semiempirical frost depth prediction models (Stefan, modified Berggren, and Chisholm and Phang empirical) were tested with soil temperature data from the Michigan Road Weather Information System. Because none of the models yielded accurate results, revised empirical models developed for different soil types required only daily high and low air temperatures as inputs. The predicted frost depths were more accurate than in previous models. Finally, by using thermal conductivity values for each soil type, the models were combined into one general model that required thermal conductivity and average air temperature as inputs.

Methodology for the prioritization of environmental sensor station installation (case study of South Korea)

A road weather information system is increasingly recognized as a critical proactive tool for winter maintenance because of recent unexpectedly heavy snowfalls and continuously colder temperatures in Korea. Therefore, effective operation of this system is a high priority. Environmental sensor stations are important components in such systems. However, resources do not permit the installation of an unlimited number of these sensor stations so some method of site prioritization must be implemented. To date however, no explicit method has been adopted. Therefore, this study proposes a decision support methodology to enable the prioritization of installation of new environmental sensor stations and then demonstrates the effectiveness of the methodology by applying it to road sections in South Korea which are especially vulnerable to snow. The primary feature of this methodology is that it provides a rational ranking for prioritizing these installations. This method can readily be applied to other geographic locations.

Prediction of Speed by Rain Intensity using Road Weather Information System and Vehicle Detection System data

Intelligent transportation systems allow us to have valuable opportunities for collecting reliable wide-area coverage traffic and weather data. Significant efforts have been made in many countries to apply these data. This study identifies the critical points for classifying rain intensity by analyzing the relationship between rainfall and the amount of speed reduction. Then, traffic prediction performance by rain intensity level is evaluated using relative errors. The results show that critical points are 0.4mm/5min and 0.8mm/5min for classifying rain intensity (slight, moderate, and heavy rain). The best prediction performance is observable when previous five-block speed data is used as inputs under normal weather conditions. On the other hand, previous two or three-block speed data is used as inputs under rainy weather conditions. The outcomes of this study support the development of more reliable traffic information for providing advanced traffic information service.

Novel Pattern Detection Algorithm for Monitoring Phase Change of Moisture on Concrete Pavement Using Surface Temperature Data

The presence of snow and ice on the pavement are major winter hazards for roadway traffic safety. Pavement temperature sensors are usually employed as a standard road weather element in many road weather information systems. A novel pattern detection algorithm has been developed to monitor the phase change of moisture on the pavement surface by analyzing a single surface temperature reading. The results of both indoor weather chamber tests and field tests under realistic highway in-service traffic conditions demonstrate that the event of moisture phase changes (e.g., dry, wet, snowy, and icy states) can be detected by converting a complicated raw sensor time history signal into a simpler binary “spike train” time history signal. These spikes can be tuned by the user to detect only the events of interest that are usually entangled with various known and unknown ambient trends in field sensor data. Since the procedures of the developed data processing algorithm are data driven and not limited to specific sensor types or physical problems, the methodology used in this study potentially can be applied widely in many field-monitoring applications.

Evaluation of alternative criteria for determining the optimal location of RWIS stations

This article presents a comprehensive framework for determining the location of road weather information system (RWIS) stations over a regional road network. In the proposed methodology, the region is divided into a grid of equal-sized zones which are considered as the minimum spatial unit for allocating a candidate set of RWIS stations. These zones are ranked according to a set of pre-specified criteria that reflect the needs for, and potential benefits from, real-time road weather information, including road surface temperature variability, precipitation, network traffic, and collision patterns. A case study based on the existing RWIS network in the province of Ontario was conducted to illustrate the major features of the proposed method and evaluate the implications of alternative location selection criteria. The findings of the study suggest that it is feasible to develop a systematic process for locating RWIS stations using an integrated location criterion to capture multiple factors being considered in practice. The study has also revealed the need to establish quantitative models for estimating the benefit of real-time information from RWIS stations, which is the foundation of a cost–benefit-based RWIS location optimization model.

Possibilities for the use of RWIS Data in a Building Sector

Though the territory of Lithuania is not large (65 300 km2) it represents different climatic conditions that are described by geographers and climatologists. The Lithuanian Hydrometeorological Service for already several hundred years has been collecting data about climatic factors of the country. The up-to-date measuring and information technologies allow implementing new systems the data of which is reliable, presented with expedition and comprising large territory. One of those systems is the Road Weather Information System (RWIS) owned by Lithuanian Road Administration under the Ministry of Transport and Communications of Lithuania. The system has been operating since 1999. Until 2011, the system had 49 measurement stations. In 2011, the same number of more stations was installed. RWIS measures air and soil temperature, air humidity, wind direction and intensity, precipitation type and amount, also records various atmospheric phenomena. This data supplements the data of Hydrometeorological Service when design-ing, building and maintaining various buildings in the different regions of Lithuania.

The dual role of weather forecasts on changes in activity-travel behavior

A deeper understanding of how human activity-travel behavior is affected by various weather conditions is essential for both policy makers and traffic managers. To unravel the ambiguity in findings reported in the literature, the main objective of this paper is to obtain an accurate assessment of how weather forecasts trigger changes in Flemish activity-travel behavior. To this end, data were collected by means of a stated adaptation experiment, which was administered both on the Internet and via traditional paper-and-pencil questionnaires. To address the main research question of this paper, two statistical techniques were adopted. The first technique is the computation of Pearson chi-square independence tests. The second approach is the estimation of a GEE-MNL-model. The results from both techniques underscore the dual role of weather forecasts on changes in activity-travel behavior. On the one hand, the results clearly illustrate the significant effect of forecasted weather; the likelihood of changes in activity-travel behavior significantly depends on the weather forecasted. On the other hand, different methods of acquiring weather information (exposure, media source, or perceived reliability) do not impact the probability of behavioral adaptations. This duality may be partially attributable to the discrepancy that exists between weather forecasts and true traffic and roadway conditions. Therefore, the implementation of a road weather information system that is directly linked to the weather forecasts is recommended.

What spatial resolution do we need for a route-based road weather decision support system?

Since their implementation, road weather information systems have mostly relied on point measurements from outstations to initiate and verify daily forecasts. Initially, spatial extrapolation was achieved by thermal mapping, but this is gradually being replaced by route-based forecasting techniques. Both techniques are similar in the sense that they use a point measurement, often taken from an outstation, to provide a spatial forecast of road surface temperatures around the road network at varying resolutions. A substantial research effort has been undertaken to understand and model the complex environmental conditions and mechanisms responsible for the variation in road surface temperatures around the road network. In particular, the interaction of varying geographical parameters around the road network (e.g. altitude, land use, road construction, topography, etc.) has been used to develop local climatological models and route-based forecasting products. By considering the needs of winter maintenance engineers, this paper reviews the current state of the art and takes a critical look at the embedding of forecast products into decision support systems. This is achieved by considering a case study of how road surface temperature and condition vary across the width of a road profile, instead of just lengthways along a road. It is shown that temperature and condition both vary significantly across the profile, which immediately raises questions about the validity of current surveying and modelling practices. This has implications for both the resolution of route-based forecasting products as well as user confidence in automated decision support systems.