Winter Road Conditions Research Articles

(Invited) Atmospheric Corrosion of Multi-Materials on Winter Road Condition in Cold Region

The use of multi-materials such as composites of carbon fiber reinforced thermoplastic (CFRTP) and high-strength steel or CFRTP and aluminum alloy is one of the main strategies for weight reduction in automotive body. When multi-materials are used outdoors, however, they are subject to galvanic corrosion, which increases the risk of failure and shortens their service life. On winter roads in cold regions, automotive bodies suffer significant corrosion due to the large amount of salt particles sprayed as an anti-freeze agent. If the corrosion products of the multi-material are relatively protective, the progression of corrosion can be prevented. Therefore, wet-and-dry cycle test was conducted on a coated multi-material joint consisting of CFRTP/high-strength steel or CFRTP/aluminum alloy plates to simulate winter road conditions in Northern Europe, and corrosion products formed in the crevice of multi-materials joint were investigated using a membrane potential measurement as well as Raman spectroscopy and X-ray fluorescence microscopy.The wet-and-dry cycle test, in which a sample was immersed in a 5:1 mixture of NaCl and CaCl2 solutions for 0.25 h and then repeatedly subjected to a wet atmosphere of 90%RH, 283 K for 4 h and a dry atmosphere of 10%RH, 308 K for 4 h more than a dozen times, was performed according to Kozaki [1]. After several weeks or more of exposure under the wet-and-dry cycle test conditions, corrosion products filled the crevice of the multi-material sample and did not to allow air leakage through the crevice gap. Ion selectivity of the corrosion products in the crevice obtained by the membrane potential measurement was found to be anionic rather than cationic selective in all tests. The products formed in the test using dilute mixture solution were relatively porous hydroxides and oxides and showed larger fixed charge concentrations than those formed in the test using concentrated solution. It was suggested that the density and composition of corrosion products play important roles in preventing atmospheric, galvanic corrosion in the gap of CFRTP/high-strength steel or CFRTP/aluminum alloy joints.[1] T. Kozaki, M. Umeta, J. Surf. Finish. Soc. Jpn. 73 (2022) 384-389.

A Framework for Determining Collision Likelihood Using Continuous Friction Values in a Connected Vehicle Environment

Jurisdictions currently provide information on winter road conditions through qualitative descriptors like bare and fully snow-covered. Ideally, these descriptors are meant to warn drivers beforehand about hazardous roads. In practice, however, discerning between safe and unsafe roads is sometimes unclear due to intermediate RSC classes covering too wide a range of conditions. This study aims at solving this safety ambiguity issue by proposing a framework for predicting collision likelihood within a road segment. The proposed framework converts road surface images into friction coefficients, which are then converted into continuous measurements through an interpolator. To find the best-performing interpolator, we evaluated geostatistical, machine learning, and hybrid interpolators. It was found that ordinary kriging had the lowest estimation error and was the least sensitive to changes in distance between measurements. After developing an interpolator, collision likelihood models were developed for segment lengths ranging from 0.5 km to 20 km. We chose the 6.5 km model based on its accuracy and intuitiveness. This model had 76.9% accuracy and included friction and AADT as predictors. It was also estimated that if the proposed framework were implemented in an environment with connected vehicles and intelligent transportation systems, it would offer significant safety improvements.

Calibration of a Hyper-Spectral Imaging System Using a Low-Cost Reference.

In this paper, we present a hyper-spectral imaging system and practical calibration procedure using a low-cost calibration reference made of polytetrafluoroethylene. The imaging system includes a hyperspectral camera and an active source of illumination with a variable spectral distribution of intensity. The calibration reference is used to measure the relative reflectance of any material surface independent of the spectral distribution of light and camera sensitivity. Winter road conditions are taken as a test application, and several spectral images of snow, icy asphalt, dry asphalt, and wet asphalt were made at different exposure times using different illumination spectra. Graphs showing measured relative reflectance for different road conditions support the conclusion that measurements are independent of illumination. Principal component analysis of the acquired spectral data for road conditions shows well separated data clusters, demonstrating the system’s suitability for material classification.

Developing Statewide Optimal RWIS Density Guidelines Using Space-Time Semivariogram Models

Preventing weather-related crashes is a significant part of maintaining the safety and mobility of the travelling public during winter months. To help mitigate detrimental effects of winter road conditions, transportation authorities rely on real-time and near-future road weather and surface condition information disseminated by road weather information systems (RWIS) to make more timely and accurate winter road maintenance-related decisions. However, the significant costs of these systems motivate governments to develop a framework for determining a region-specific optimal RWIS density. Building on our previous study to facilitate regional network optimization, this study is aimed at considering the nature of spatiotemporally varying RWIS measurements and integrating larger case studies comprising eight different US states. Space-time semivariogram models were developed to quantify the representativeness of RWIS measurements and examine their effects on regional topography and weather severity for improved generalization. The optimal RWIS density for different topographic and weather severity regions was then determined via one of the most successful combinatorial optimization techniques—particle swarm optimization. The findings of this study revealed a strong dependency of optimal RWIS density on varying environmental characteristics of the region under investigation. It is anticipated that the RWIS density guidelines developed in this study will provide decision makers with a tool they need to help design a long-term RWIS implementation plan.

Modeling of dry snow adhesion during normal impact with surfaces

Contamination due to dry snow smoke adhesion is an evident danger for sensor blinding on future autonomous driving cars under winter road conditions. This paper examines at what velocities ice particles and agglomerates, representing dry snow, adhere to surfaces of various materials. Contact models for normal direction, tangential sliding, and tangential rolling that account for the adhesive interaction of spherical particles due to Van der Waals forces are used in the study. Three different scenarios of impacts are presented i) single particle impact, ii) small agglomerate impacts, and iii) large agglomerate impacts. It is shown that by increasing the number of particles in an agglomerate, the velocity at which the agglomerate sticks to the impact wall increases, i.e. the agglomerate is more likely to stick to a surface. It is also shown how material properties influence the tendency of dry road snow to adhere to a surface.

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.

Community vulnerability to changes in the winter road viability and longevity in the western James Bay region of Ontario’s Far North

A network of winter roads that consists of snow-ice roads over land, muskeg, and frozen lakes and rivers has been and continues to be a critical seasonal lifeline in remote-northern First Nation (FN) communities in Ontario’s Far North. This study examines current vulnerability of the Fort Albany community to physical, social, and economic impacts associated with the changing of the viability and longevity of winter roads and its seasons, as well as the river ice regimes. Semi-directive interviews with key informants (n = 8) and structured surveys with winter road users (n = 54) were conducted to gather local knowledge about the evolution of winter roads and climatic and environmental changes in winter road conditions and seasons. Trends in the river ice breakup and flood events for the Moose River, Albany River, and Attawapiskat River were also examined. The results of this study indicate that climatic factors, particularly air temperature and snowfall, have directly affected the construction and maintenance of the James Bay Winter Road. Trend analyses of spring flooding for the three rivers exhibit statistically significant increases (p ≤ 0.01) over the past few decades; thus, flooding in nearby communities has become a more significant threat in recent years. A few short- and medium-term adaptation strategies have been initiated in response to the impacts of climate change on winter roads; however, developing long-term planning and feasible adaptation for remote-northern communities in Ontario’s Far North is necessary.

Reusable road condition information system for traffic safety and targeted maintenance

Driver awareness of current winter road conditions (RCs) is known to affect the frequency of accidents due to sudden changes in these conditions. For example, partially icy roads that appear during autumn in northern areas typically result in collisions and ditch runs unless the drivers are generally aware of the situation. Availing motorists who drive under winter RCs of enhanced information is therefore highly desirable to increase their awareness of hazardous driving conditions. Such conditions need to be predicted ahead of time and presented to drivers before they attempt slippery road sections. Moreover, the identification of slippery RCs should quickly trigger targeted road maintenance to reduce the risk of accidents. This study presents a scalable and reusable collaborative intelligent transport system, herein referred to as an RC information system (RCIS). RCIS provides accurate RC predictions and forecasts based on RC measurements, road weather observations, and short-term weather forecasts. The prediction methods in the context of the distributed RCIS have been tested using a prototype implementation. These tests confirmed that these inputs could be combined into useful and accurate information about winter RCs that can be adapted for different types of users.

Comprehensive Study of the Performance of Winter Tires on Ice, Snow, and Asphalt Roads: The Influence of Tire Type and Wear

ABSTRACT This work presents a comprehensive study of the performance of winter tires on snow, ice, and asphalt. A set of 77 different winter tires were carefully selected for the study. Of these, 27 were new and 50 were worn from real traffic use. All three tire types for winter conditions (Nordic, European, and studded) were represented. All tires have been tested using a mobile tire-testing device for snow and asphalt and using a stationary tire-testing facility for ice. Both devices recorded the tire forces and motions, enabling a close to complete stationary characterization of the tires. In addition, 42 of the tires were tested on a passenger car, where brake performance was evaluated for the three different road conditions. This enables a comparative study of performance between tire types and wear for various winter road conditions. The results suggest that the recorded data represent real vehicle performance. Some conclusions from the measurements are that the effect of wear is consistent between the tire groups and that the performance degradation is most noticeable on studded tires on ice and on European tires on snow.

Lateral coefficient of friction for characterizing winter road conditions

Real Time Traction Tool (RT3)-Curve was used in this study to evaluate the effect of ice and snow on tire–road lateral friction coefficient, herein referred to as the Halliday Friction Number (HFN). The field experiments for the study were performed in winter 2012–2013 on the University of Alberta’s test road facility in Edmonton, Alberta. Each run was repeated at three target speeds under varied road conditions, bare dry, dry with ice patches, ice, and three levels of snow accumulation. No considerable correlation was found between vehicle speed and the friction measurements for bare dry, ice- and snow-covered conditions. Expectedly, the bare dry asphalt concrete surface had the highest HFN, the presence of ice reduced the dry surface friction by 55%. The accumulation of snow on the dry surface reduced the HFN further than ice, by 69, 75, and 81% for light, moderate, and heavy snow, respectively. A falling trend was observed for friction as more snow accumulated on the ground. Analysis of the effect of number of truck passes over ice at −3.5 and −5 °C showed that ice can become more slippery after each pass of traffic. A similar analysis for snow revealed that more passes over moderate snow will compact the fresh snow into a slippery surface. For light snow, even at low temperatures (<−10 °C), passes of traffic will melt the snow through frictional heat and result in higher friction values.

Winter Road Surface Condition Monitoring

This paper describes the field evaluation of a smartphone-based winter road surface condition (RSC) monitoring system. The system was deployed on eight maintenance and patrol vehicles and tested during the winter of 2013–2014 on a section of Highway 6 in Ontario, Canada. The study focused on data obtained from four of the vehicles. This paper details the evaluation of the system from three perspectives: spot-wise monitoring, route-level monitoring, and system reliability. The study compared the system's RSC monitoring results with manual classifications, patrol records, and the Traveller's Road Information Portal, a public-facing website. The system's consistency with current RSC monitoring methods was assessed, and it was found that the smartphone-based system was capable of providing reliable results in comparison with the current method of patrol reporting for route-level monitoring of winter road conditions. Key performance factors were investigated, and the paper presents recommendations for system improvement.

Polarization resolved classification of winter road condition in the near-infrared region

Three different configurations utilizing polarized short-wave infrared light to classify winter road conditions have been investigated. In the first configuration, polarized broadband light was detected in the specular and backward directions, and the quotient between the detected intensities was used as the classification parameter. Best results were obtained for the SS-configuration. This sensor was shown to be able to distinguish between the smooth road conditions of water and ice from the diffuse road conditions of snow and dry asphalt with a probability of wrong classification as low as 7%. The second sensor configuration was a pure backward architecture utilizing polarized light with two distinct wavelengths. This configuration was shown to be effective for the important problem of distinguishing water from ice with a probability of wrong classification of only 1.5%. The third configuration was a combination of the two previous ones. This combined sensor utilizing bispectral illumination and bidirectional detection resulted in a probability of wrong classification as low as 2% among all four surfaces.

The use of a temporal analogue to predict future traffic accidents and winter road conditions in Sweden

AbstractSlippery roads due to ice and snow are a major cause of road traffic accidents in Sweden during the winter. This paper investigates the hypothesis that as the climate becomes increasingly milder there will be a reduction in the number of accidents in winter. Two months are compared in this analysis; one colder and drier than average, the other warmer and wetter than average. Despite the differences in weather between the 2 years, there was approximately the same number of accidents in both cases, although the exact cause of these accidents varied. It is concluded that using the warmer month as a temporal analogue, the accident rate in Sweden will not fall under current climate change scenarios. This result is attributed to the fact that drivers become more complacent in milder weather conditions where the risk of slippery roads is reduced. Copyright © 2010 Royal Meteorological Society

Handling of buses on slippery roads during the influence of side wind—a study of the effects of different tyres

Accident statistics assembled by the Swedish National Road and Transport Research Institute (VTI) have shown that buses are overrepresented when it comes to accidents on icy and snowy roads. For a better understanding of the problem, the performance of modern summer and winter tyres on winter road conditions had to be assessed. The objective of this work has been to go beyond standard road grip studies, and instead investigate how the driver's possibility to deal with one challenging situation depends on the tyres and tyre configuration. This paper describes a novel methodology where measurements with different tyres on ice in VTI's tyre test facility, enabled a simulator study for evaluating the impact of different tyre characteristics when driving in conditions with strong side wind on a slippery road. The main results of the study are: to deal with strong side wind, good front wheel grip is most important, and while non-studded winter tyres provided little or no improvement over summer tyres, studded winter tyres significantly reduced the risk to run off the road. Furthermore, it is very difficult for the drivers to judge, beforehand, whether a vehicle with a particular tyre configuration will perform good or bad.

Estimating the economic value of ice climbing in Hyalite Canyon: An application of travel cost count data models that account for excess zeros

Recently, the sport of ice climbing has seen a dramatic increase in popularity. This paper uses the travel cost method to estimate the demand for ice climbing in Hyalite Canyon, Montana, one of the premier ice climbing venues in North America. Access to Hyalite and other ice climbing destinations have been put at risk due to liability issues, public land management agendas, and winter road conditions. To this point, there has been no analysis on the economic benefits of ice climbing. In addition to the novel outdoor recreation application, this study applies econometric methods designed to deal with “excess zeros” in the data. Depending upon model specification, per person per trip values are estimated to be in the range of $76 to $135.

Stream Chloride Monitoring Program of City of Toronto: Implications of Road Salt Application

Abstract In cold regions, winter road safety is a major challenge for municipalities and provincial highway transportation agencies. Road salt is widely used to improve winter road conditions, but concerns have been raised about the effects of road salts on the environment. This paper describes a water quality monitoring program designed to measure both background chloride concentrations and the effects of road salt application on stream water quality in four watersheds (Humber River, Don River, Highland Creek, and Morningside tributary of Rouge River) located within the City of Toronto boundary. The effect of road salts on stream water quality was evaluated based on chloride concentration because of its conservative nature. A bilinear correlation was developed to transform measured specific conductance levels in stream water to chloride concentrations. There are no Ontario aquatic fresh water quality guidelines for chloride, but chloride concentrations in almost all the monitored streams in Toronto periodically exceeded chronic and acute chloride threshold levels of the United States Environmental Protection Agency. The City of Toronto has been proactive in its efforts to implement management practices to reduce the impact of road salt application on the environment while maintaining safe driving conditions for its road users. Normalized salt application rates in Toronto have been on a gradual declining trend in the last decade from about 0.08 to 0.07 tonnes of salt applied per centimetre of snowfall per kilometre of lane. With public safety in mind, further reductions in salt application rates are being considered to reduce the adverse environmental effects to acceptable limits.

Sophistication of Road Infrastructure Management in a Snowy Cold Region —— Development of a Forecasting Method for Winter Road Conditions and Maintenance Support Systems ——

Sophistication of Road Infrastructure Management in a Snowy Cold Region —— Development of a Forecasting Method for Winter Road Conditions and Maintenance Support Systems ——

Influence of Winter Road Conditions and Signal Delay on Pedestrian Route Choice in Japan's Snowiest Metropolis

Investigation of pedestrian route choice behavior on icy surfaces is important for the effective improvement of walkways in winter. The objective of this research was to investigate pedestrian route choice behavior in winter. Field surveys and questionnaire surveys were conducted to fulfill this objective. Video cameras were used in the field surveys to clarify the movements of pedestrians. How pedestrians chose their routes was investigated by observing their movements. According to the field survey, when the signal was green, the probability that the pedestrian would cross became extremely high, regardless of the road surface conditions. However, when the walkway surface was icy, the probability that the pedestrian would wait for a green signal decreased by a considerable value. This indicates that when the wait becomes long, the probability that the pedestrian will cross becomes low during the snowy season. A questionnaire survey was also conducted to clarify the factors affecting pedestrian route choice behavior. The questionnaire asked about different road surface conditions. The results from the survey indicate that even if part of a road section has a good surface condition, it has a strong influence on route choice behavior. It indicates that pedestrians feel uncomfortable in walking on slippery walkways and they prefer to choose bare walkways. On the basis of the data from the field survey and questionnaire survey, logit models were developed to express quantitatively the route choice behaviors of pedestrians. These models can be used to predict the probability that a pedestrian will select a route as a function of pedestrian delay at signalized intersections and the road surface conditions in winter.

Influence of Winter Road Conditions and Signal Delay on Pedestrian Route Choice in Japan's Snowiest Metropolis

Investigation of pedestrian route choice behavior on icy surfaces is important for the effective improvement of walkways in winter. The objective of this research was to investigate pedestrian route choice behavior in winter. Field surveys and questionnaire surveys were conducted to fulfill this objective. Video cameras were used in the field surveys to clarify the movements of pedestrians. How pedestrians chose their routes was investigated by observing their movements. According to the field survey, when the signal was green, the probability that the pedestrian would cross became extremely high, regardless of the road surface conditions. However, when the walkway surface was icy, the probability that the pedestrian would wait for a green signal decreased by a considerable value. This indicates that when the wait becomes long, the probability that the pedestrian will cross becomes low during the snowy season. A questionnaire survey was also conducted to clarify the factors affecting pedestrian route choice behavior. The questionnaire asked about different road surface conditions. The results from the survey indicate that even if part of a road section has a good surface condition, it has a strong influence on route choice behavior. It indicates that pedestrians feel uncomfortable in walking on slippery walkways and they prefer to choose bare walkways. On the basis of the data from the field survey and questionnaire survey, logit models were developed to express quantitatively the route choice behaviors of pedestrians. These models can be used to predict the probability that a pedestrian will select a route as a function of pedestrian delay at signalized intersections and the road surface conditions in winter.

Analysis of station locations in a road weather information system

AbstractMany northern countries use a road weather information system (RWIS) with a network of stations to monitor winter road conditions. Present station locations were selected afterfield investigations of micro‐ and local‐climate conditions (e.g. using thermal mapping). This paper describes an approach to optimally locate and equip the stations in order to best identify conditions hazardous to road transport. This is achieved using multiple regression analysis of observed data and correlation with location meta‐data. A geographical information system (GIS) is used to develop quantitative and objective descriptions of station locations by using knowledge of local and regional climate variations. Road climate is described using a slipperiness classification, in which weather situations are classified into ten types of slipperiness from the meteorological variables collected at RWIS stations. The relationships between quantified locations and data on road slipperiness in southern Sweden during one winter are analysed. The results show that the spatial patterns for different types of slipperiness are significantly related to local parameters. The three most prevalent types are analysed in detail: snowfall on a frozen road surface, hoarfrost and low visibility, and strong formation of hoarfrost. Copyright © 2001 Royal Meteorological Society