Winter Maintenance Operations Research Articles

Investigation of winter weather crash injury severity using winter storm classification techniques

This study employs a unique methodology for analyzing seasonal traffic crashes by first identifying adverse winter-weather events using traditional traffic-crash data (from Nebraska) and correlating them to detailed meteorological data for subsequent analyses. Specifically, the novel component of this analysis utilized a state-specific winter storm and seasonal severity index metric in conjunction with classifications of storm type. Both crash and meteorological data were combined to determine if the amount of snowfall and/or storm intensity/severity could be a predictor of the number and severity of crashes. Analysis was performed to identify weather conditions causing highway safety concerns. The important takeaway of the study was that most winter weather-related vehicular crashes in Nebraska were associated with relatively minor winter weather conditions. The reported crashes typically occurred with relatively low snowfall amounts due to residual snowfall on the ground, emphasizing the importance of continuing winter maintenance operations activities and public service announcements long after a storm has passed through the region. Modeling crash injury severity revealed that icy pavements, higher duration of snowfall, work zones, and higher number of trucks and buses involved in a crash were linked to higher injury severity; higher visibility was associated with a greater chance of crashes involving visible injuries but a lower likelihood of crashes involving disabling injury/fatality. Snowfall was associated with more visible injury crashes, whereas snow-depth was linked to fewer visible injury and disabling injury/fatality crashes. The study's limitations include the spatial and temporal aggregation of weather data and a lack of detailed winter roadway maintenance/clearance activity data.

Constraint-Based snowplow optimization model for winter maintenance operations

In the annual winter route maintenance, transportation agencies often deploy multiple fleets of trucks for snow control and removal activities over a vast maintenance area which creates an operational problem in determining the optimal maintenance routes and fleet size. The objective of this paper is to develop a snowplow routing optimization model to enhance the efficiency of snow removal route planning. The routing optimization model was developed using vehicle routing problems, constraint-based programming, and geographic information system. The developed model was applied to optimize the snow removal route planning practice of a District in Kansas, United States, as a case study. The result of this study shows that the optimization model can help minimize the fleet size and increase the level of service for treating snow routes within the selected District. The results of this study are expected to assist transportation agencies in optimizing their snow route removal in winter maintenance operations.

Mechanical, thermodynamical, and microstructural characterization of adhesion evolution in asphalt binder-aggregate interface subjected to calcium chloride deicer

Frigid weather and freezing road conditions in cold regions result in reduced pavement surface friction, compromised vehicle maneuverability, leading to deteriorated transportation safety. Given their effectiveness and low cost, using chloride-based deicing agents such as CaCl2 has become a standard winter maintenance strategy for many highway agencies. However, coupled with freeze–thaw (F-T) cycles, CaCl2 can adversely affect the durability of asphalt pavements and cause complex distresses such as moisture-induced damage, among others. Despite this fact, the mechanisms and the extent of the damage made to the asphalt binder-aggregate interface due to use of chloride salts are not well-understood. This study was undertaken to characterize the mechanisms by which different eutectic concentrations of aqueous CaCl2 solutions and F-T cycles affect the adhesion between asphalt binder and different aggregates through a multi-scale characterization and testing program. Given the rise in use of polymer-modified asphalt binder a PG 64–34 asphalt binder and aggregates of granite, quartzite, and limestone mineralogies were evaluated. More specifically, pull-off strength of the aggregate-binder systems subjected to different aqueous concentrations of CaCl2 and F-T cycles were determined. In addition, surface free energy method as a thermodynamic-based approach was employed to determine the effect of salt concentration on the adhesion and debonding energies, and the moisture-induced damage potential of the binder-aggregate interfaces. Furthermore, atomic force microscopy was employed to evaluate the effect of salt concentration and F-T cycles on surface morphology, nanostructure, and adhesion of the asphalt binder. The applied multi-scale characterization provided invaluable information about the physio-chemical phenomena responsible for the accelerated damage in asphalt binder-aggregate interface when subjected to different aqueous concentrations of CaCl2. Gaining an accurate understanding of the damage mechanism is necessary for developing new materials and methods for combatting salt-accelerated moisture-induced damage in cold regions as a result of winter maintenance operations.

Feasibility assessment of self-deicing concrete pavements with recycled CFRP reinforcement: Environmental and mechanical performance

Electrically conductive concrete (ECC) with carbonaceous conductive elements has been studied for self-deicing pavements; however, carbon fiber production is costly and resource-intensive. Therefore, mechanically recycled carbon fiber reinforced polymer (CFRP) scraps from plane manufacturing were evaluated as reinforcing and conductive elements for ECC.Evaluation of mechanical properties showed that 5%Vol rCFRP-ECC had approximately 20% higher flexural strength and twofold greater absorbed fracture energy than the reference plain concrete. In a -15 °C environment, this ECC melted ice at a heating rate of 0.05 °C/min and a 3.8 kWh/m2 power consumption.A life cycle assessment modeling of winter maintenance operations for one snowstorm in the Sierra Nevada Mountain regions in California showed that rCFRP-ECC and two other ECCs from the literature have a significantly higher global warming potential from energy consumption than snowplowing, sanding, and salting. Nevertheless, important advantages of ECCs in reducing chloride contamination in water and soil, corrosion in vehicles and infrastructure, and stronger and more durable fiber-reinforced infrastructure materials should be considered in decision-making.

Enhancing Winter Road Maintenance via Cloud Computing

In this study, we developed an AI-enhanced cloud computing framework to enable the autonomous decision-making quality and precision of winter road operations for connected living, namely Smart Maintenance Decision Support System (SmartMDSS). With the support from the Federal Highway Administration (FHWA) and Michigan Department of Transportation, we are among the first to develop AI-enabled tools for practical winter road maintenance decision-making. On the front end, SmartMDSS provides a user-friendly graphical interface showing all the valuable data and winter operations for specific points on the road and sends necessary warnings and notifications. On the back end, SmartMDSS extracts and analyzes data and makes winter road maintenance decisions. Convolutional Neural Network (CNN) has been successfully employed to identify the snow coverage on the road surface. Decision-making algorithms were proposed and implemented to support the road engineers and operators for real-time winter maintenance operations.

Crushing effects on the durability of rocky aggregates used on road surfaces subjected to winter maintenance and extreme climate conditions

• The evolution of the internal structure of rocks regarding their crushing and sizing processes has been analyzed. • The effects of freeze-thaw and thermal stress durability tests with NaCl has been also studied. • Both rocks lost similarly weight and increased their water absorption coefficients after crushing and durability tests. • Durability tests affected more to aggregates than rocks, due to the negative impact of the crushing processes. • The specific area subjected to the agents of deterioration in the durability tests should be consider in future studies. • Thermal stress with salt is the most damaging durability test performed, especially on the aggregates analyzed. The internal structure of a rock is determined by its empty spaces (pores and cracks), which can evolve over time by chemical, physical, or biological processes. In construction, these chemical and physical processes, and their effects in the final products, known as aggregates, are well known, and controlled to estimate their quality and durability. However, the intrinsic properties of the raw material, the rock, and the processes that can affect its quality and durability during the production of the aggregates are poorly studied and considered in construction. For this reason, the evolution of the internal structure of two different types of aggregates used in Spanish roads were analyzed. The aggregates will be also studied in their raw form, rock, by measuring their water absorption coefficient and using mercury porosimetry and fluorescence microscopy. To estimate the influence of the internal structure in the durability of the aggregates, both forms, rocks, and aggregates, will be subjected to the combined effect of temperature variations and the salt used during winter maintenance operations on Spanish roads. The results obtained show that crushing processes in the quarry increase the water absorption coefficient of both materials and generate new micro-porosity caused by the microcracking of weak areas of the rock such as its schistosity or the presence of previous cracks. Thus, this increasing of the porosity produced by crushing processes in the quarry has a negative impact on the durability of the aggregates when they are subjected to winter maintenance operations and extreme climate conditions.

Developing Car-Following Models for Winter Maintenance Operations Incorporating Machine Learning Methods

Car-following models have been explored thoroughly for different vehicle types, such as cars and trucks. Although snowplows can be classified as trucks, their unique physical and operational characteristics impose a distinct following behavior. Michigan Department of Transportation has recently tested a collision avoidance system to reduce rear-end crashes. The incorporated radar in this system provides valuable information, defining two objectives for this study: (1) investigating the impacts of snowplows on car-following behavior considering car–car and car–snowplow vehicle-type combinations; (2) exploring the effects of the proposed collision avoidance system on car-following behavior by comparing car-following models for collected data with and without such a system. Firstly, space and time headway analyses are performed to compare different vehicle-type combinations. Then, the Gipps’ model is calibrated, and two data-driven car-following models are trained incorporating support vector regression and a long short-term memory network. These models are calibrated/trained to evaluate the performance of models with and without considering the heterogeneity of driving behavior among road users. The results indicate that the presence of snowplows leads to statistically significant different car-following models. Besides, it is shown that the collision avoidance system slightly improves the behavior of the following vehicles, which is not statistically significant. Also, it is concluded that considering driving behavior heterogeneity leads to more realistic prediction of the following behavior, compared to assuming homogeneous driving styles in traffic. Finally, the performances of the three developed car-following models are compared. Developing specific models for winter maintenance operations is an early step toward developing microsimulation models for adverse weather conditions.

An Automatic Architecture Designing Approach of Convolutional Neural Networks for Road Surface Conditions Image Recognition: Tradeoff between Accuracy and Efficiency

Convolutional neural network (CNN) is a promising image recognition technique for winter road surface condition (RSC), a measure that is crucial for winter maintenance operations. In the past, researchers have designed RSC CNN models that displayed acceptable results but did so focusing solely on obtaining high classification accuracy without any consideration for efficiency. Furthermore, when it comes to model development itself, architecture design requires expertise in CNN as well as rich knowledge in the investigated problem itself. To rectify these issues, this paper proposes an innovative approach to automatically design RSC CNN architecture without compromising classification accuracy. The proposed approach uses a weighted sum method, which provides the freedom of choosing relative importance level between accuracy and efficiency. Once the relative importance has been set, one of the most successful and widely adopted heuristics, namely, simulated annealing (SA), is employed to generate (sub)optimal solutions. Results show that both accuracy and efficiency of the automatically generated CNNs are better or at least comparable to the two selected state-of-the-art CNN models, ResNet50 and MobileNet, achieving as high as 93.44% classification accuracy. Ultimately, the outcome of this study fills the gap in existing CNN design methods that do not consider the tradeoff between accuracy and efficiency while providing insight into the effect varying architectures have on CNN model performance.

Forecasting short-term road surface temperatures considering forecasting horizon and geographical attributes – an ANN-based approach

The ability to forecast road surface temperature (RST) in advance is an important asset for winter maintenance (WRM) operations. It effectively allows for the reduction of WRM cost through more efficient use of their maintenance resources. However, considering extensive road networks that must be monitored and the extent at which RST varies over time, WRM agencies have constantly been looking for better ways to generate accurate road weather forecasts as they strive to optimize their WRM services and maintain safe travels. To tackle this, this study developed RST forecasting models using an Artificial Neural Network (ANN). RST measurements collected by six selected stationary road weather information systems (RWIS) stations in Alberta, Canada were utilized to validate the feasibility and applicability of the proposed method developed herein. The developed models were found to generate highly accurate results with mean absolute error (MAE) values of 0.64, 1.20, 1.59, 2.16, 2.56, and 3.03 °C for 1-h, 2-h, 3-h, 4-h, 5-h, and 6-h ahead forecasts, respectively. The novelty of this study lies in investigating the probable effect of some external factors on model performance, where it was revealed that forecasting horizon and geographical attributes influenced forecasting accuracies. Upon investigating the hypothesis that locational attributes would affect forecasting accuracies, the results confirmed that accuracy improved with increasing latitude, and decreasing elevations – worthwhile findings that can potentially lead to developing more refined models for generating highly accurate location-specific RST forecasts.

Phyto-based sodium chloride hydrogel for highway winter maintenance of porous asphalt pavements

The need of the highway agencies to customize their winter maintenance operations on porous asphalt pavements prompted the authors to develop an innovative anti-icing technology based on the hot liquid spray application of a saline hydrogel. A bio-based (phyto-based) thermo-sensitive sodium chloride (NaCl) brine, that has the ability to form a gel simply by coming into contact with a very cold surface, was conceived. The increase in viscosity and the formation of a gel-like structure would make this anti-icing product able of filling the surface voids without permeating through the mixture (longer residual deicing effectiveness), while maintaining at the same time the pavement frictional resistance. In view of adopting sustainable winter maintenance strategies, this innovative blend was formulated by exploiting the thickening and gelling properties of wall-cell polysaccharides contained in seaweeds fibers. The mechanisms of gel formation were experimentally studied in laboratory, analyzing in detail the thermal and rheological properties of the salt hydrogel during the different phases of preparation, storage and application. On the basis of this characterization, a full-scale validation was carried out on an existing highway porous asphalt pavement. For this purpose, a tank truck prototype was equipped with a heating capacity tank and a customized specific spraying system.

Exploring the associations between winter maintenance operations, weather variables, surface condition, and road safety: A path analysis approach

This paper aims to assess the effects of snow and ice control operations by investigating the interdependency between weather variables, maintenance operations, pavement friction, and collisions. Using a disaggregated event-based and location-specific framework, and employing the statistical techniques of Structural Equation Modeling and Path Analysis, all the significant direct and indirect effects of weather variables and maintenance operations on pavement friction and collision occurrence during snowstorms have been identified. It was revealed that precipitation, extremely low temperatures, and the potential of black ice formation all had significant negative direct effects on pavement friction and significant indirect negative effects on traffic safety. Moreover, the application of anti-icing agents and plowing operations have been shown to significantly improve pavement friction and in return improve traffic safety indirectly. To illustrate how the maintenance operations improve traffic safety, a hypothetical snowstorm example was considered. According to the model, anti-icing application was associated with a 14% reduction in collisions, plowing operations resulted in a 33% reduction in collisions, and combining the two tools reduced collisions per snowstorm by 42%. The findings of this paper can help transportation agencies make more informed decisions to promote an efficient mobilization of the existing winter road maintenance services and resources while improving the safety of the traveling public during the winter months.

Winter road surface condition classification using convolutional neural network (CNN): visible light and thermal image fusion

During winter, road conditions play a crucial role in traffic flow efficiency and road safety. Icy, snowy, slushy, and wet road conditions reduce tire friction and affect vehicle stability which could lead to dangerous crashes. To keep traffic operations safe, cities spend a significant budget on winter maintenance operations such as snow plowing and spreading of salt and sand. This paper proposes a methodology for automated winter road surface condition classification using convolutional neural network (CNN) and the combination of thermal and visible light cameras. As part of this research, 4244 pairs of visible light and thermal images are captured from pavement surfaces and classified into snowy, icy, wet, and slushy surface conditions. Two single-stream CNN models (visible light and thermal streams) and one dual-stream CNN model are developed. The average F1-Score of dual-stream model is 0.866, 0.935, 0.985, and 0.888 on snowy, icy, wet, and slushy, respectively. The weighted average F1-Score is 0.94.

Experimental and theoretical characterization of electrodes on electrical and thermal performance of electrically conductive concrete

Transportation agencies regularly seek innovative, cost-effective, and environmentally-friendly approaches to help manage winter maintenance operations during snow and ice events. The use of an electrically-conductive concrete (ECON) heated pavement system (HPS) is a smart and promising alternative to conventional snow and ice removal methods using de-icing chemicals and snow-removal equipment. ECON is made electrically conductive by introducing conductive agents into the concrete mix and applying electrical current through electrodes embedded in the ECON layer. This study investigates the influence of electrode size and geometry on the resistive heating performance of ECON HPS and identifies the most electrically and thermally-efficient type of electrode through experimental testing and theoretical analysis. Electrode performance was investigated in water, prototype ECON slabs, and through development of a finite-element model, and good agreement was found between the results obtained from these three evaluation methods. A theoretical formulation between thermal performance and electrical and material properties in ECON HPS was first developed. Testing electrodes in water was then found to be a quick, non-destructive, and cost-effective alternative method for monitoring thermal performance of electrodes. Finally, electrode surface contact area was identified as the dominant electrode characteristic with respect to influencing temperature increase. Cost and energy analysis for each electrode type showed that smaller-diameter electrodes exhibited higher cost-effectiveness, and conversely, larger diameter electrodes were found to have higher energy-conversion efficiency. The results of this work will provide guidance for choosing the most cost-effective and optimized type of electrode for design and construction of a large-scale ECON HPS.

Effectiveness of Green Warning Lights with Different Flashing Patterns for Winter Maintenance Operations

Snow removal activities are performed by roadway agencies to enhance winter mobility and safety. Slower travel speeds during these operations, combined with low visibility and reduced pavement friction, mean that safety and collision avoidance remain a persistent concern. Many studies have implemented signing and lighting technologies to improve the visibility of snowplows. Although a few studies have evaluated the use of different colors on snowplows, there is no rigorous study that evaluates the potential impacts of using green warning lights for winter maintenance operations. This study, therefore, investigates the impacts of various warning light configurations on the visibility of snowplows, with the focus on green lights. To this end, 37 warning light configurations are designed using various color combinations (green and amber), and flashing patterns (single and quad) on the back (LED), the top (beacon), or both, of snowplows. These configurations are evaluated to identify the most effective configurations. Three sets of experiments are designed and implemented: static, dynamic, and weather to evaluate the visibility effectiveness in different contexts: day versus night, clear versus snowy weather, and static versus dynamic scenarios. Human subjects are employed to conduct the experiments and the test results are evaluated using statistical analyses. The conspicuity during the day time and glare during the night time are statistically different among various configurations. In addition, adding green lights with a single flash pattern to amber warning lights improves the conspicuity, while keeping the glare at an acceptable level relative to configurations using only amber.

Bicycle rolling resistance under winter conditions

In many cold regions of the world, the percentage of trips made by bicycle drops drastically during the winter months. To facilitate increased bicycle usage during the winter, we studied the effect of typical winter conditions on bicycle rolling resistance and cycling comfort. An instrumented bicycle was used to measure bicycle rolling resistance under various winter conditions on streets and cycleways in Trondheim, Norway. The rolling resistance was estimated by first measuring propulsive and resistive forces on a moving bicycle and then solving the force equilibrium. Simultaneously, the test cyclist subjectively evaluated the level of cycling comfort, and video recordings were made to document the conditions. Data were collected on 103 road sections, including three levels of service (maintenance standards). The results showed that rolling resistance increased significantly in accordance with increasing loose snow depths. Dry and wet snow leads to a higher rolling resistance than slush does at the same depth. Similarly, increased rolling resistance correlates with reduced cycling comfort. Rolling resistance coefficients (Crr) higher than 0.025 noticeably reduce cycling comfort. The road sections that were maintained with a bare road winter maintenance strategy (using anti-icing chemicals, brushing and/or plowing) provided significantly lower rolling resistance and higher levels of cycling comfort than the sections maintained with a winter road strategy (only plowing and sanding). This study shows that rolling resistance measurements may be used to estimate winter cycling comfort indirectly. Therefore, rolling resistance may be useful for improving winter maintenance operations and controls. Better winter maintenance is essential for increasing bicycle usage in the winter.

Long-term behavior of the micro-texture of aggregates used on roads subjected to extreme climate conditions and winter maintenance operations

The micro-texture of fine and coarse aggregates used on roads is one of the main factors responsible for the skid resistance of asphalt pavements. However, the long-term behavior of this property regarding the effects of traffic and climate conditions has been poorly studied. Therefore, in this paper, two types of aggregates, schist and amphibolite, commonly used on roads that are subjected to winter maintenance actions and high temperatures in summer have been studied. The micro-texture of the aggregates has been characterized with an optical roughness tester, fluorescence microscopy and the Polished Stone Value test. The extreme climate conditions have been simulated in a laboratory with durability tests (freeze-thawing and thermal stress cycles) including the NaCl used as deicer during winter maintenance operations. Results have shown that the wearing mechanisms of the aggregates’ surfaces are different regarding their composition and internal texture. On schist aggregates, weather conditions applied do not significantly deteriorate their micro-texture. However, the combined effect of traffic and extreme climate conditions with NaCl, double the decrease of the surface roughness measured when these aggregates are subjected to extreme climate conditions in the laboratory, improving, in some cases, their skid resistance. Conversely, the external factors considered have the same effect on the micro-texture of the analyzed amphibolite aggregate.

Leveraging Telematics for Winter Operations Performance Measures and Tactical Adjustment

The Indiana Department of Transportation (INDOT) maintains 29,000 lane miles of roadway and operates a fleet of nearly 1100 snowplows and spends upwards of $60 million annually on winter maintenance operations. Since winter weather varies considerably, allocation of snow removal and deicing resources are highly decentralized to facilitate agile response. Historically, real-time two-way radio communication with drivers has been the primary monitoring system, but with 6 districts, 29 subdistricts, and over one hundred units it does not scale well for systematic data collection. Emerging technology such as real-time truck telematics, hi-resolution NOAA data, dash camera imagery, and crowdsourced traffic speeds can now be fused into dashboards. These real-time dashboards can be used for systematic monitoring and allocation of resources during critical weather events. This paper reports on dashboards used during the 2020-2021 winter season derived from that data. Nearly 13 million location records and 11 million dash camera images were collected from telematics onboard 1105 trucks. Peak impact of nearly 1570 congested miles and 610 trucks deployed was observed for a winter storm on February 15th, 2021 chosen for further analysis. In addition to tactical adjustments of resources during storms, this system-wide collection of resources allows agencies to monitor multiple seasons and make long-term strategic asset allocation decisions. Also, from a public information perspective, these resources were found to be very useful to agencies that interface with the media (and social media) during large storms to provide real-time visual updates on conditions throughout the state from pre-treatment, through cleanup.

Color durability of pigmented slurry surfacing after artificial aging tests simulating weathering and traffic

In the new vision of complete street, the use of colored pavements has been promoted in recent years to improve road user safety and to enhance the reorganization and aesthetic of the urban environment. But, besides several benefits these pavements undergo environmentally and traffic induced changes in appearance, faster than the traditional ones, a few months after installation. Thus, the laboratory-scale investigation analyzed the color durability of pigmented slurry seals, which were prepared using a clear synthetic emulsion and mineral pigments, subject to different artificial accelerated aging procedures. They simulated the exposition to sunlight at high temperature (QUV weathering tester), the aggression of chemical products (salt brine bath) used for winter maintenance operations and the dynamic actions of vehicle tires (loaded wheel tester). The analyses of color change measured with a spectrophotometer highlighted the extreme sensitivity of pigmented thin surfacing to physical and mechanical stresses. Specifically, the thermal and photo-oxidation of the binder film induced by the UV exposure at high temperature and its wear caused by the dynamic passage of rubber tire produced a large supra-threshold color difference on the samples’ surface. These results suggest how a correct mix-design, both in the materials choice and ingredients dosage, should be properly calibrated on the basis of the pavement end-use and of the prevailing climatic conditions to ensure color consistency throughout the pavement lifetime.

Developing Roadway Safety Models for Winter Weather Conditions Using a Feature Selection Algorithm

Inclement winter weather such as snow, sleet, and freezing rain significantly impacts roadway safety. To assess the safety implications of winter weather, maintenance operations, and traffic operations, various crash frequency models have been developed. In this study, several datasets, including for weather, snowplow operations, and traffic information, were combined to develop a robust crash frequency model for winter weather conditions. When developing statistical models using such large-scale multivariate datasets, one of the challenges is to determine which explanatory variables should be included in the model. This paper presents a feature selection framework using a machine-learning algorithm known as the Boruta algorithm and exhaustive search to select a list of variables to be included in a negative binomial crash frequency model. This paper’s proposed feature selection framework generates consistent and intuitive results because the feature selection process reduces the complexity of interactions among different variables in the dataset. This enables our crash frequency model to better help agencies identify effective ways to improve roadway safety via winter maintenance operations. For example, increased plowing operations before the start of storms are associated with a decrease in crash rates. Thus, pretreatment operations can play a significant role in mitigating the impact of winter storms.

Life cycle assessment of winter road maintenance

Winter road maintenance in the Nordic climate is demanding due to challenging weather conditions, high precipitation, and icy conditions. As a leading country in the transition to low-emission transport, Norway must work to reduce their emissions while providing a safe level of service through winter maintenance operations. This article investigates the environmental impacts of winter road maintenance (WRM) in Norway both today and under a climate change scenario predicted for 2050. Life cycle assessment (LCA) is used to evaluate the environmental impact of the functional unit “average winter road maintenance in Norway on national and county roads per km.lane.” The ReCiPe (hierarchy) method was used to identify and categorize emissions related to WRM to show how different factors affect the system and to reveal hidden emissions hotspots. Real-time data from WRM vehicles were used to determine how fuel consumption is affected by gradient and weather. Producers and operators provided other relevant information on WRM vehicles. Official reports supplied information on deicer quantities used and the total distance driven by WRM vehicles in Norway. The quantity of deicer used is the main source of emissions contributing toward all impact categories. The effect of deicer is likely to be even higher in certain impact categories. The environmental impact of the deicer after application is not included. The representation of WRM in existing emissions data is limited despite the considerable amount of deicer applied and the long distances that WRM vehicles travel. The results document how energy use throughout the system is another important source of emissions. Various parameters, such as road gradient, vehicle properties, driver behavior, and weather, affect the fuel consumption of WRM vehicles, with weather being the most important of these. Significant potential for emissions reductions from WRM was found, and WRM operations should be included in cold-climate road LCA studies. The environmental impacts of deicer application are especially high compared to the mechanical clearing of roads and contribute strongly to impact categories such as terrestrial, freshwater, and human toxicity and to the formation of particulate matter.