Road Weather Information System Research Articles

Performance evaluation of road surface temperature forecasts

This paper describes a study focusing on performance evaluation of road weather information system (RWIS) surface temperature forecasts. To identify the factors influencing the accuracy of forecasts, five research hypotheses regarding climate patterns, geographical attributes, seasonal variations, time of day, and forecast horizon are proposed. Observation and forecast data were provided by four North American provinces, and were processed and stratified by station, hour, and month to quantify their effects. The findings based on two statistical measures; namely, mean absolute error and percent of acceptable forecasts suggested that the maritime climate group had the highest correspondence while the mixed climate group had the lowest. The geographical attributes showed that the forecasting performance of coastal maritime regions was found to have a negative correlation to the distance from nearby large bodies of water. Forecasts for the daytime were found to be less accurate than for the nighttime and deteriorate quickly as the forecasting horizon increased.

An enhanced spatial statistical method for continuous monitoring of winter road surface conditions

To facilitate more efficient winter maintenance decision support, road weather information systems (RWIS) have been widely used by highway agencies. However, the cost of RWIS stations is high, and they have limited monitoring coverage. To address this challenge, this paper presents an innovative framework that applies regression kriging to integrate stationary and mobile RWIS data to improve the accuracy of road surface temperature (RST) estimation. Furthermore, an optimal RWIS network expansion strategy is introduced by incorporating a modified particle swarm optimization method with the objective of minimizing spatially averaged kriging estimation errors. A sensitivity analysis is also conducted to investigate the influence of station densities on model performance. The case study from Alberta, Canada, demonstrates the feasibility and applicability of the proposed method. The findings provide insights for continuous monitoring and visualization of both road weather and surface conditions and for optimizing RWIS network planning.

Use of Topography, Weather Zones, and Semivariogram Parameters to Optimize Road Weather Information System Station Density across Large Spatial Scales

A road weather information system (RWIS) is a combination of advanced technologies which collect, process, and disseminate road weather and condition information. This information is used by road maintenance authorities to make operative decisions that improve safety and mobility during inclement weather events. Many North American transportation agencies have invested millions of dollars to deploy RWIS stations to improve the monitoring coverage of winter road surface conditions. The design of these networks often varies by region, however, and it is not entirely clear how many stations are necessary to provide adequate monitoring coverage under different conditions; substantial gaps remain in knowledge about optimal design. To fill these gaps, an investigation was conducted to determine how optimized RWIS station densities relate to topographic and weather characteristics. A series of geostatistical semivariogram models were constructed and compared using topographic position index (TPI) and weather severity index (WSI) to measure relative topographic variation and weather severity, respectively. The geostatistical approach was then applied to map the optimum number of RWIS stations across several topographic and weather zones. The study area captured varying environmental characteristics, including regions with flat or varied terrain and warm or cold regions. This study suggests that RWIS data collected from a specific region can be used to estimate the number of stations required for regions with similar zonal characteristics. The outcome of this study can be used as a decision-making tool for RWIS network expansion, thus maximizing monitoring capability of RWIS networks using topographic and weather-related zonal classifications.

Cost- effectiveness analysis of the road-weather information system (RWIS)

Knowing the current atmospheric conditions of the roads is of great importance, both for road managers and for road users. RWIS stations record all relevant local atmospheric conditions in real time and transmit this information to control centers. This information has a dominant application in planning activities related to road maintenance in winter conditions. An analysis of the literature relating to the costs and benefits of RWIS stations indicates that their implementation in the transport infrastructure is not only economically profitable, but also offers a number of benefits such as more efficient road maintenance and facilitated organization of available funds, all for the purpose of winter road maintenance. Implementation of this system contributes to the safety of users on the public road network, as well as to the reduction of the number of traffic accidents in which the road and its condition are recognized as the cause. Twenty-eight meteorological stations were installed on the road network of the Republic of Serbia. On the basis of the historical data on the state of the roads from the mentioned stations in the previous period of winter maintenance and an individual implementation cost, the cost-effectiveness analysis of RWIS in the Republic of Serbia was performed.

Safety Assessment of the Integration of Road Weather Information Systems and Variable Message Signs in British Columbia

Adverse weather conditions create an environment in which it is difficult for drivers to navigate safely. Reducing weather-related collisions is a target for road safety professionals in British Columbia (BC), Canada. This study reports the safety benefits of installing road weather information systems (RWISs) coupled with variable message signs (VMSs) on provincial rural highways in BC. The RWIS/VMS system comprises road and weather sensors, as well as two VMSs. The road and weather sensors collect data on pavement surfaces and weather conditions. Information on adverse road/weather conditions are conveyed to road users via the VMSs. The system had been installed at six different locations on rural undivided highways in BC between 2011 and 2014. The analysis made use of police-attended serious crashes (i.e., fatal + injury) that took place during winter seasons. Depending on the implementation date, three or four winter seasons were available as a before-implementation period, while three to six winter seasons were available as an after-implementation period. An Empirical Bayes (EB) approach was employed to ensure that the evaluation results were reliable and to account for the regression-to-the-mean artifact. Safety performance functions (SPFs) were developed using data collected at similar sites. The EB evaluation results showed an overall statistically significant reduction of 32.7% in all winter serious collisions (WSC). An economic evaluation showed that the systems led to a benefit-cost ratio of 4.8 and an overall net present value of more than Can$12 million. The results of this study may motivate transportation agencies and stakeholders to pursue similar systems for mitigating weather-related safety problems.

Identification and Classification of Slippery Winter Road Conditions using Commonly Available Vehicle Variables

Extreme winter weather conditions severely affect the transportation sector. Technologies such as Road Weather Information Systems provide live data on the road surface conditions to assist the road authorities in providing safe mobility. The main problem is, however, the limited number of such systems that have been deployed, resulting in fragmented information about road conditions. This paper addresses the problems associated with the limited quantity of information concerning slippery winter road conditions by presenting a proof-of-concept for a system that not only detects slippery winter road conditions, but also predicts the type of slippery surface (ice, snow and slush) via vehicle-based systems. The concept demonstrated in this paper makes use of commonly available variables, which are, longitudinal slip ratios, longitudinal acceleration and the ambient temperature to identify such situations. The developed system employs a Fuzzy Inference System that is not only capable of identifying slippery conditions but is also capable of classifying surfaces based on the extent of slipperiness. This provides the road authorities with several moving sensors (vehicles traveling on a particular road) compared with the few fixed sensors currently available. This could deliver a pool of information to assist the road authorities to efficiently handle their staff and equipment so that appropriate equipment reaches the right place at the right time.

On the relationship between the risk of hoar frost on roads and a changing climate in Sweden

Hoar frost, one of the most common types of road slipperiness, can reduce road surface friction thereby adversely affecting traffic safety. The two main causes of hoar frost are warm air advection and radiative cooling. This paper investigates the impact of a changing climate on the formation of hoar frost in Sweden during the winter months due to warm air advection and radiative cooling. The analysis is based on in situ observations from 244 stations in the Swedish Road Weather Information System. The results show that in northern Sweden, hoar frost was mainly caused by warm air advection, while radiative cooling was the main cause of hoar frost in southern Sweden. Given the increase in the air temperature over land due to climate change during the period 2000–2016, the relative frequency of hoar frost due to warm air advection has significantly decreased, while the relative frequency of hoar frost due to radiative cooling has significantly increased. Further analysis showed that the weakened temperature gradient between land and ocean, which leads to weakened warm air advection, is the main cause of the changes.

A Geostatistical Investigation into the Effective Spatiotemporal Coverage of Road Weather Information Systems in Alberta, Canada

Inclement weather acutely affects road surface and driving conditions and can negatively impact traffic mobility and safety. Highway authorities have long been using road weather information systems (RWISs) to mitigate the risk of adverse weather on traffic. The data gathered, processed, and disseminated by such systems can improve both the safety of the traveling public as well as the effectiveness of winter road maintenance operations. As the road authorities continue to invest in expanding their existing RWIS networks, there is a growing need to determine the optimal deployment strategies for RWISs. To meet such demand, this study presents an innovative geostatistical approach to quantitatively analyze the spatiotemporal variations of the road weather and surface conditions. With help of constructed semivariograms, this study quantifies and examines both the spatial and temporal coverage of RWIS data. A case study of Alberta, which is one of the leaders in Canada in the use of RWISs, was conducted to indicate the reliability and applicability of the method proposed herein. The findings of this research offer insight for constructing a detailed spatiotemporal RWIS database to manage and deploy different types of RWISs, optimize winter road maintenance resources, and provide timely information on inclement road weather conditions for the traveling public.

Winter hoar frost conditions on Swedish roads in a warming climate

As one of the most common reasons for slippery roads in wintertime, hoar frost can reduce surface friction and affect traffic safety. The risk of winter road hoar frost is subjected to changes in the warming climate. A better understanding of these changes could lead to improved forecasting of hoar frost risk and provide information to policymakers in making climate adaptation strategies. In this work, the decadal variation in winter road hoar frost risk between 2000 and 2016 in Sweden was examined by using in situ observations from 244 stations in the Swedish Road Weather Information System. Results show that hoar frost risks have decreased in the south of Sweden (south of 59°N), whilst increasing in central Sweden (approximately 59°–65°N). Hoar frost risk tends to increase (decrease) in regions where there is a relatively high (low) mean number of hoar frost risk days. Further analysis indicates that the strengthened winter North Atlantic Oscillation (NAO) over the last few decades, which resulted in warmer and wetter winters in Sweden, is the main cause of the changes. During strong positive NAO winters, the frequency of blocking anticyclones centred to the south‐west of Sweden significantly decreased and led to more warm and moist air from south‐west being transported to Sweden. The reduction in hoar frost risk in the southern part of Sweden is mainly due to an increase in road surface temperature, while the increasing hoar frost risk in central Sweden is dominated by the increase in relative humidity, which favours the occurrence of hoar frost.

Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data

IntroductionDriving environment, including road surface conditions and traffic states, often changes over time and influences crash probability considerably. It becomes stretched for traditional crash frequency models developed in large temporal scales to capture the time-varying characteristics of these factors, which may cause substantial loss of critical driving environmental information on crash prediction. MethodCrash prediction models with refined temporal data (hourly records) are developed to characterize the time-varying nature of these contributing factors. Unbalanced panel data mixed logit models are developed to analyze hourly crash likelihood of highway segments. The refined temporal driving environmental data, including road surface and traffic condition, obtained from the Road Weather Information System (RWIS), are incorporated into the models. ResultsModel estimation results indicate that the traffic speed, traffic volume, curvature and chemically wet road surface indicator are better modeled as random parameters. The estimation results of the mixed logit models based on unbalanced panel data show that there are a number of factors related to crash likelihood on I-25. Specifically, weekend indicator, November indicator, low speed limit and long remaining service life of rutting indicator are found to increase crash likelihood, while 5-am indicator and number of merging ramps per lane per mile are found to decrease crash likelihood. ConclusionsThe study underscores and confirms the unique and significant impacts on crash imposed by the real-time weather, road surface, and traffic conditions. With the unbalanced panel data structure, the rich information from real-time driving environmental big data can be well incorporated.

A multi-objective optimization approach to the location of road weather information system in New York State

ABSTRACTInclement weather is a threat to the safety of transportation systems as well as the efficiency of their operation. A road weather information system (RWIS) is a network of environmental sensor stations (ESS) that collect a range of real-time data about weather and pavement conditions. These systems can support highway officials and civilians in making more informed transportation safety decisions, particularly in times of adverse weather, by giving them more accurate and localized weather information. This enables the proper maintenance activities to be executed and safety to be restored while using minimum resources. However, because of the range of network characteristics and geographical factors affecting the implementation of ESS, no widely adopted guidelines exist that outline where to implement ESS in a network beyond taking into the physical criteria of an appropriate site, although several methods have been suggested. This paper aims to take a practical approach to solving the location problem of RWIS by proposing a unified multi-objective optimization methodology that takes into account vehicular accident data, vehicle miles traveled, area coverage, access to power and maintenance, and existing ESS. This study produces an exact solution method that produces a Pareto set of multiple efficient solutions. The proposed methodology is applied to a real world case study focused on the deployment of additional ESS in the existing RWIS network across New York State. Further, a sensitivity analysis is conducted to examine the effects of different parameters and a nonpreference solution is proposed.

Spatiotemporal variability of road weather conditions and optimal RWIS density — an empirical investigation

This paper presents a study aiming at understanding the relationship between the spatiotemporal characteristics of road weather conditions and a number of road weather information systems (RWIS) stations using real-world case studies. Semivariogram models are constructed to determine the spatial variability of road weather conditions, especially, autocorrelation range which describes a separation distance at which the measurements are no longer correlated to each other. An optimal RWIS density is then determined through an optimization process that minimizes the total inference errors across the underlying road network. The findings suggest that the regions with less varied topography tend to have a longer spatial correlation range than the regions with more varied topography. The study further reveals that the range of spatial autocorrelation is related to the optimal density of RWIS network — the region with a longer range requires fewer RWIS stations, than the region having a shorter range.

Prioritization Scheme for Proposed Road Weather Information System Sites: Montana Case Study

Weather presents considerable challenges to highway agencies both in terms of safety and operations. From a safety standpoint, snow, ice and other forms of precipitation may reduce pavement friction, increasing the potential for crashes when vehicles are traveling too fast for the conditions. From an operations standpoint, heavy snow storms may affect the connectivity of the highway network due to closures that need to be cleared in an efficient and timely fashion. Further, travelers should be informed about unusual pavement conditions and road closures on time to minimize the effect of adverse weather on the safety and mobility of the traveling public. For the aforementioned reasons, road weather information has become increasingly important for highway agencies particularly in regions that experience harsh winter weather conditions. Road Weather Information System (RWIS) consists of the hardware, software, and communications interfaces to collect and transfer road weather observations from or near the roadway to a display device at the user’s location (1). Today, most environmental sensor stations (ESS) for RWIS include various atmospheric sensors, some form of pavement sensor, and camera imaging. Additional sensors are also being added to some ESS locations to measure traffic volumes, traffic speeds, and vehicle classifications and weights (2). Road weather information has been used by highway agencies in many applications such as winter maintenance, traveler information, and other weather-related intelligent transportation systems (ITS) applications. Data adequacy, reliability, and geographic coverage are critical attributes to consider for these transportation applications. Highway agencies are often faced with the challenge of selecting a limited number of ESS sites from a larger pool of proposed sites given the limited budgets available. This process has been largely subjective in practice, relying on the expertise and judgement of the agency staff involved. Therefore, there is a need for an objective prioritization scheme for proposed ESS stations, which should guide future RWIS expansion and ensure maximum utility (benefits) from new ESS installations.

Crash Frequency Analysis Using Hurdle Models with Random Effects Considering Short-Term Panel Data.

Random effect panel data hurdle models are established to research the daily crash frequency on a mountainous section of highway I-70 in Colorado. Road Weather Information System (RWIS) real-time traffic and weather and road surface conditions are merged into the models incorporating road characteristics. The random effect hurdle negative binomial (REHNB) model is developed to study the daily crash frequency along with three other competing models. The proposed model considers the serial correlation of observations, the unbalanced panel-data structure, and dominating zeroes. Based on several statistical tests, the REHNB model is identified as the most appropriate one among four candidate models for a typical mountainous highway. The results show that: (1) the presence of over-dispersion in the short-term crash frequency data is due to both excess zeros and unobserved heterogeneity in the crash data; and (2) the REHNB model is suitable for this type of data. Moreover, time-varying variables including weather conditions, road surface conditions and traffic conditions are found to play importation roles in crash frequency. Besides the methodological advancements, the proposed technology bears great potential for engineering applications to develop short-term crash frequency models by utilizing detailed data from field monitoring data such as RWIS, which is becoming more accessible around the world.

Location Optimization of Road Weather Information System (RWIS) Network Considering the Needs of Winter Road Maintenance and the Traveling Public

AbstractThis study presents an innovative approach to the planning of a critical highway sensor infrastructure ‐ road weather information system (RWIS). The problem is formulated to minimize the spatially averaged kriging variance of hazardous road surface conditions while maximizing the coverage of accident‐prone areas. This optimization framework takes explicit account of the value of information from an RWIS network, providing the potential to enhance the overall efficacy of winter maintenance operations and the safety of the travelers. Spatial simulated annealing is used to solve the resulting optimization problem and its performance is demonstrated using a real‐world case study from Minnesota, United States. The case study illustrates the distinct features of the proposed model, assesses the effectiveness of the current location setting, and recommends additional stations locations. The findings of our study suggest that the proposed model could become a valuable decision‐support tool for planning a new RWIS network and evaluating the performance of alternative RWIS expansion plans.

Crash Frequency Modeling Using Real-Time Environmental and Traffic Data and Unbalanced Panel Data Models.

Traffic and environmental conditions (e.g., weather conditions), which frequently change with time, have a significant impact on crash occurrence. Traditional crash frequency models with large temporal scales and aggregated variables are not sufficient to capture the time-varying nature of driving environmental factors, causing significant loss of critical information on crash frequency modeling. This paper aims at developing crash frequency models with refined temporal scales for complex driving environments, with such an effort providing more detailed and accurate crash risk information which can allow for more effective and proactive traffic management and law enforcement intervention. Zero-inflated, negative binomial (ZINB) models with site-specific random effects are developed with unbalanced panel data to analyze hourly crash frequency on highway segments. The real-time driving environment information, including traffic, weather and road surface condition data, sourced primarily from the Road Weather Information System, is incorporated into the models along with site-specific road characteristics. The estimation results of unbalanced panel data ZINB models suggest there are a number of factors influencing crash frequency, including time-varying factors (e.g., visibility and hourly traffic volume) and site-varying factors (e.g., speed limit). The study confirms the unique significance of the real-time weather, road surface condition and traffic data to crash frequency modeling.

Optimizing Environmental Sensor Station Locations for Road Weather Management: Overview and a Generalized Modeling Framework

Adverse weather poses a significant threat to transportation safety. Road weather information systems (RWIS) are used to mitigate the impact of adverse weather. Because of the lack of detailed, unified guidelines and because of diverse weather conditions across the United States, state and city transportation agencies follow different practices for choosing locations for environmental sensor stations. This paper proposes a comprehensive cell-based methodology that optimizes overall benefits of RWIS based on weather-sensitive crash characterizations and using both normal and adverse weather crash data. The proposed approach is especially suited to optimizing regionwide environmental sensor station locations involving many road segments. A case study concerning RWIS deployment in Austin, Texas, was conducted with data from a crash records information system for 2010 to 2013.

Expanding the Road Weather Information System for Avalanche Support

Transportation agencies use road weather information systems to support operations, maintenance, and informed travel decisions. Road weather capabilities have been expanded through the FHWA Weather Responsive Traffic Management’s advisory, control, and treatment strategies to address weather-related roadway impacts. Monitoring conditions that precede avalanches offers a new opportunity to leverage the road weather information system’s infrastructure. Wet avalanches, particularly wet slab avalanches, pose considerable risk. They can occur simultaneously across an extensive area, encompass a greater mass of material, create considerable energy, cause significant road closures, and lead to major injuries and fatalities. Roadside and at-altitude avalanche indicators can be monitored by adding sensors to existing road weather environmental sensor stations, deploying new environmental sensor stations at both roadside and at-altitude locations, and creating new, collaborative partnerships. The traditional sensor suite of the road weather information system can be augmented with new meteorological and snowpack sensors to monitor preavalanche conditions. Calibrated present weather detector sensors and heated precipitation gauges can provide improved estimates of the precipitation’s liquid water equivalent. Thermistor strings and lysimeters can provide snowpack stability evaluations. Adding environmental sensor stations, both at roadside and at altitude, can provide strategic observations of the atmospheric and snowpack conditions. Increased coordination between agencies on site selection, sensor choices, and data distribution can expand the role of road weather information systems to support avalanche forecasting.

Assessing Driver Speed Choice in Fog with the Use of Visibility Data from Road Weather Information Systems

Fog can create a significant safety hazard for motorists. If motorists drive faster than visibility permits, severe multiple-vehicle crashes could occur. In Virginia, sections of I-64 and I-77 in mountainous parts of the state experience significant recurring fog events. These locations have also experienced several chain-reaction crashes during fog, involving more than 50 vehicles. Those crashes were typically caused by drivers traveling too fast for the conditions. To improve safety on the I-77 corridor, the Virginia Department of Transportation is constructing a variable speed limit system that will provide dynamic speed limits based on the available visibility. Before the system is activated, it is important to understand existing driver speed choice behavior during low-visibility conditions. It is possible that posting a variable speed limit based only on stopping sight distance could create significant speed variance and decrease safety if drivers are driving much faster than conditions warrant. In this study, speed and visibility data were examined at several locations on I-64 and I-77, where there were recurring fog events. The models developed showed that while motorists are slowing down in low visibility, there is still a significant differential between observed speeds and the safe speed calculated from the stopping sight distance. The results indicate that some additional smoothing will need to occur to ensure a safe transition from current speed choices to desired safe speeds.

Connected Vehicle Solution for Winter Road Surface Condition Monitoring

A connected vehicle–based winter road surface condition (RSC) monitoring solution that combines vehicle-based image data with data from a road weather information system is described. The proposed solution was intended as an improvement to a smartphone-based system evaluated in previous research. Three machine learning classification methods—namely, artificial neural networks, random trees, and random forests—were evaluated for potential application in the connected vehicle–based system for RSC monitoring. Field data collected during the 2014–2015 winter season were used for model calibration and validation. Results showed that all models improved the accuracy of the smartphone-based RSC classification substantially, with random forest having the highest classification performance. The models, however, were found to lack transferability; therefore, individual models will require local calibration before being used at any location.