Search Results (9)

Background: In northern countries, spring requires the removal of large volumes of abrasive materials used in winter road maintenance. This sweeping process, crucial for safety and environmental protection, has traditionally relied on conventional mechanical brooms. Recent technological innovations, however, have introduced more efficient and environmentally friendly sweeping solutions; Methods: This study provides a comprehensive comparative analysis of the environmental and operational performance of these innovative sweeping systems versus conventional methods. Using simulation models based on real-world data and integrating fuel consumption models, the analysis replicates sweeping behaviors to assess both operational and environmental performance. A sensitivity analysis was conducted using these models, focusing on key parameters such as the collection rate, the number of trucks, the payload capacity, and the truck unloading duration; Results: The results show that the innovative sweeping system achieves an average 45% reduction in GHG emissions per kilometer compared to the conventional system, consistently demonstrating superior environmental efficiency across all resources configurations; Conclusions: These insights offer valuable guidance for service providers by identifying effective resource configurations that align with both environmental and operational objectives. The approach adopted in this study demonstrates the potential to develop decision-making support tools that balance operational and environmental pillars of sustainability, encouraging policy decision-makers to adopt greener procurement policies. Future research should explore the integration of advanced technologies such as IoT, AI-driven analytics, and digital twin systems, along with life cycle assessments, to further support sustainable logistics in road maintenance. Full article

Good visibility of lane markings is important for all road users, particularly autonomous vehicles. In general, nighttime retroreflectivity is one of the most challenging marking visibility characteristics for agencies to monitor and maintain, particularly in cold weather climates where agency snowplows remove retroreflective material during winter operations. Traditional surface-applied paint and glass beads typically only last one season in cold weather climates with routine snowplow activity. Recently, transportation agencies in cold weather climates have begun deploying improved recessed, durable pavement markings that can last several years and have very high retroreflective properties. Several dozen installations may occur in a state in any calendar year, presenting a challenge for states that need to program annual repainting of traditional waterborne paint lines, but not paint over the much more costly durable markings. This study reports on the utilization of mobile mapping LiDAR systems to classify and evaluate pavement markings along a 73-mile section of westbound I-74 in Indiana. LiDAR intensity data can be used to classify pavement markings as either tape or non-tape and then identify areas of tape markings that need maintenance. RGB images collected during LiDAR intensity data collection were used to validate the LiDAR classification. These techniques can be used by agencies to develop accurate pavement marking inventories to ensure that only painted lines (or segments with missing tape) are repainted during annual maintenance. Repeated tests can also track the marking intensity over time, allowing agencies to better understand material lifecycles. Full article

Winter continues to witness numerous automobile accidents attributed to graupel and hail precipitation in Japan. Detecting these weather phenomena using radar technology holds promise for reducing the impact of such accidents and improving road maintenance operations. Weather radars operating at different frequencies, such as C- and X-band, prove effective in graupel detection by analyzing variations in backscattered signals within the same radar volume. When particle diameters exceed 5 mm, the study of Mie scattering characteristics across different melting ratios reveals insights. The dual frequency ratio (DFR) shows potential for graupel detection. The DFR presents wider variations with ten-times difference in melting ratios with increased density, offering opportunities for precise detection. Additionally, the DFR amplitude rises with temperature changes. However, for hydrometeor diameters below approximately 3 mm, and within the Rayleigh region, the DFR exhibits minimal fluctuations. Hence, this technique is best suited for diameters exceeding 3 mm for optimal efficacy. Additionally, a “detection alert” for graupel/hail has been proposed. Based on this alert, and with realistic rain/graupel size distributions, graupel/hail can be detected with an approximate probability of 70%. Full article

This study analyzes the sustainability of the circular economy model on winter road maintenance. Winter road maintenance involves plowing snow, spreading abrasives, and then sweeping and collecting the remaining abrasives at the end of the winter season. Traditionally, in the linear approach, the collected sweepings are landfilled, which incurs landfilling costs for resources that could be reused. To address this issue, we consider the option of recycling sweepings for use in the following winter seasons. We develop a discrete-event simulation model that estimates the economic and environmental benefits of this option. Using data from a case study of a highway in Quebec, Canada, the model shows that introducing circular economy practices in winter road maintenance results in less material going to landfills, lower costs, less use of aggregates from virgin materials, and lower CO₂ emissions compared to the linear approach. A subsequent sensitivity analysis reveals that the quantity of sweepings collected greatly influences the outcomes. Full article

During the winter season, snowplowing has a significant effect on road users as it is critical to winter road maintenance and operations. The main goal of this study is to generate optimal routes for snowplowing trucks for efficient road maintenance. In addition to the conventional problem of reducing travel time and distance, this study also incorporates actual operational constraints, such as minimum maintenance standards and driver safety, to improve the overall efficiency of operations. To achieve the objectives, we first implemented the Chinese Postman Problem (CPP) to create Euler circuits from the initial routes and then identified the shortest paths by applying Dijkstra’s algorithm. Then, the Tabu search algorithm was chosen as a metaheuristic algorithm for the optimization process that finds near-optimal solutions by considering operational constraints for snowplow routes. Unsafe turning conditions and minimum maintenance standards were taken into account in the objective function defined for the optimization process. In simulations, the route obtained by our approach was compared to one with the application of CPP only in terms of travel distance, time, turning conditions, and road maintenance priority. Full article

The decade of big data has emerged in recent years, which has led to entering the era of intelligent transportation. One of the main challenges to deploying intelligent transportation is dealing with winter roads in cold climate countries. Different operations can be used to protect the road from ice and snow, such as spreading chemicals (here salt) on the road surface. Using salt for de-icing and anti-icing increases road safety. However, the excess use of salt must be avoided since it is not cost-efficient and has negative impacts on the environment. Therefore, the accurate and timely prediction of salt quantity for winter road maintenance helps decision support systems to achieve effective and efficient winter road maintenance. Thus, this paper performs exploratory data analysis to determine the relationships among variables to find the best prediction model for this problem. Due to the stochastic nature of variables regarding weather and roads, a deep neural network/deep learning is selected to predict the amount of salt on the wheel track, using historical data measured by sensors and road weather stations. The results show that the proposed model performs perfectly to learn and predict the amount of salt on the wheel track, based on different metrics, including the loss function, scatter plot, mean absolute error, and explained variance. Full article

The design, construction, and maintenance of steel structures must be carried out in a way that ensures they will be able to reliably operate for the whole duration of their planned service life. To ensure sufficient durability, it is necessary to determine and evaluate the characteristics of the appropriate environment in which the structure will be placed. This submission focuses on the specific environment surrounding roads that are treated with de-icing salts during winter maintenance. It investigates the dependency between corrosive damage to the structure and the relevant parameters of the environment. Basic corrosive factors include temperature, relative humidity, deposition of chlorides and sulfur dioxide, precipitation, the pH of precipitation as well as many other parameters. An accurate estimate of corrosive damage requires an analysis of the long-term trends in concentrations of individual corrosive factors, while respecting their randomly varying attributes. The article, hence, introduces and evaluates stochastic prediction models that are based on long-term programs focusing on the evaluation of the corrosive aggressiveness of the environment, while taking into account random variations of the nature of the input parameters. The use of stochastic prediction models allows us to perform sensitivity analysis that can determine the impact of specific corrosive factors on the corrosive damage caused to the structure. The article is supplemented by sensitivity analysis focusing on an evaluation from the effects of the deposition of chlorides on the corrosive damage to steel bridge structures. The analysis was carried out using data obtained from experimental measurements of the deposition rates of chlorides in the vicinity of roads in the Czech Republic. Full article

A recent body of research in fatigue management indicates that other factors, including in-cab and external equipment, contribute to operator fatigue. The goal of this project was to identify winter road maintenance equipment (in-cab and external) that may increase or mitigate snowplow operator fatigue. To accomplish this goal, questionnaires from 2011 snowplow operators were collected from 23 states in the U.S. Results confirmed previous research that fatigue is prevalent in winter road maintenance operations. Winter road maintenance equipment that produced excessive vibrations, noise, reduced visibility, and complex task demands were found to increase snowplow operators’ self-reported fatigue. Similarly, equipment that reduced vibrations and external noise, improved visibility, and limited secondary tasks were found to reduce snowplow operator’s self-reported fatigue. Based on the questionnaire responses and the feasibility of implementation, the following equipment may help to mitigate or prevent snowplow operator fatigue: dimmable interior lighting, LED bulbs for exterior lighting, dimmable warning lights, a CD player or satellite radio in each vehicle, heated windshield, snow deflectors, narrow-beam auxiliary lighting, and more ergonomically designed seats with vibration dampening/air-ride technology. Full article

Although numerous research studies have investigated the effects of fatigue in commercial motor vehicle drivers, research with winter maintenance (WM) drivers is sparse. This study pilot-tested the feasibility of evaluating WM operator fatigue during winter emergencies using naturalistic driving data. Four WM operators participated in the study and drove two instrumented snow plows for three consecutive winter months. The operators also wore an actigraph device used to measure sleep quantity. As this was a pilot study, the results were limited and only provided an estimation of what may be found in a large-scale naturalistic driving study with WM operators. Results showed the majority of safety-critical events (SCEs) occurred during the night, and approximately half of the SCEs occurred when participants were between 5 and 8 h into their shifts. Fatigue was identified as the critical reason in 33% of the SCEs, and drivers were found to average less sleep during winter emergencies versus winter non-emergencies. However, one participant accounted for all fatigue-related SCEs. Although data were limited to two instrumented trucks and four drivers, results support the approach of using naturalistic driving data to assess fatigue in WM operators. Future on-road research is needed to understand the relationship between fatigue and crash risk in WM operators. Full article