Abstract Heated pavement systems effectively prevent snow and ice accumulation on pavement surfaces. This type of pavement is designed to enhance pedestrian safety and mobility. However, the substantial energy requirements associated with operating such systems have raised critical concerns regarding their economic viability and long-term sustainability. Thus, assessing energy consumption is crucial for evaluating different winter maintenance strategies in pedestrian areas. Previous studies have shown that climatic factors significantly influence heated pavement energy consumption. Precipitation has received the least attention among the various climate factors. Therefore, this study investigates the correlation between precipitation, temperature expressed in freezing degree hours, and the energy consumption of heated pavement systems. Based on detailed data from two locations in Trondheim during the 2023–2024 winter season, a prediction model to estimate energy use as a function of precipitation on a logarithmic scale and freezing degree hours is developed. The prediction model is relevant to the development of winter operation models, supporting the formulation of winter maintenance strategies for pavement areas and considering local climate conditions. By incorporating energy consumption as a key performance indicator, the value of this study lies in its ability to estimate energy consumption using readily available local climatic data, enabling energy consumption predictions and thereby cost predictions for operating heated pavement systems for locations beyond Trondheim.

This paper evaluates the detection quality of an automated road surface condition monitoring system tested in a fully automated pilot study. The system integrates multiple image-processing modules, including a road damage detection model based on YOLOv8 and a road surface segmentation model using DeeplabV3. The system was deployed in an automated pilot study setup, from March 2024 to Sept 2024. To assess the system's performance, a subjective evaluation of the model's performance during pilot study was conducted with the help of road maintenance professionals using a five-point qualitative scale with categories "very poor", "poor", "satisfactory", "good" and "excellent". The results showed that 65% of the analysed images were rated as satisfactory or better for detection quality. In contrast, an F1-score of 0.45 was achieved by the YOLOv8 model during finetuning. The study also identifies key errors and limitations in the system's automated detection, with a primary detection error identified as confusion between visually similar objects.

Despite its cruciality for road mobility and safety, Winter Road Maintenance (WRM) is highly expensive and environmentally impactful. This suggests that it needs to be optimized. Simulation of WRM operations might help in optimizing these services. This study focuses on upgrading "Effort Model," a regression-based model used to estimate WRM operations, including salting, plowing, and combined plowing-salting efforts, across Norway's state road network. This model would be the computational core for a WRM-simulation tool. The earlier version, a Generalized Linear Regression (GLR) model, showed limitations in capturing the spatial variability of operations due to Norway's diverse climate and topography. To address this, the authors adopted the Multiscale Geographically Weighted Regression (MGWR) method to upgrade three sub-models for salting, plowing, and plowing-salting efforts. MGWR allows for different spatial scales of explanatory variables. The current proposed models are calibrated using three winter seasons (2020-2023) and include both weather and non-weather variables, such as cycle time, average annual daily traffic (AADT), snow days, and cold days. Findings showed that the MGWR approach significantly improved estimation accuracy compared to the GLR, with higher adjustedR2 and lower Akaike Information Criterion (AIC) scores. Based on the results, the spatial variation of coefficients is not the same; while some variables like cycle time behave more globally, others such as cold days show localized impacts. Despite the improvements, the model still needs additional refinements in terms of predicting an unseen winter (2023-2024).

Pavement condition monitoring is an important aspect of an efficient Pavement Management System (PMS). However, the monitoring of road sections is a tedious work. Therefore, research communities have come up with deep learning based automatic methods for detecting and classifying road damages. But for a highway authority overlooking a large network of roads, an aggregate measure of road condition is more useful. Therefore, in this work, the performance of different methods for quantifying road condition has been tested with respect to subjective rating at network level. The road condition was quantified using Damage Count (DC), Damaged Area (DA) and Weighted Damage Count (WDC) approaches using algorithms based on YOLOv8 and DeepLabV3 models for detection and segmentation, respectively. An experiment was followed by expert ratings to determine Mean Panel Rating (MPR) for 36 sections on a selected road stretch in Norway. Proposed models to determine Section Condition Rating (SCR) based on the damage count and weighted damage count approaches were able to predict MPR with an R2 value of 0.93. Further, the SCRWDC was found to have a reasonable correlation with International Roughness Index (R2 equal to 0.79).

The complexity of the load response on a modern cross-country ski makes it difficult to address the individual macroscopic parameters' influence on ski-snow friction. In this study, a custom adjustable ski was developed to isolate the effect of normal force, apparent contact area, spacing and load split on the coefficient of friction. These parameters were tested in a ski-snow tribometer at relevant sliding speeds, normal loads, slider sizes and snow conditions for cross-country skiing. At cold air temperatures (-10 degrees C) the friction was governed by the average contact pressure, whereas at warmer air temperatures (-2 degrees C and + 5 degrees C) the friction was governed by the apparent contact area. Additionally, the effect of load split between the front and rear slider showed different trends depending on the temperature. Smaller spacing between the two sliders led to reduced friction across all temperatures. These findings provide new insights for optimizing cross-country ski gliding performance in various snow conditions.

The prosperity of urban life is dependent on its infrastructure. Urban underground infrastructure components (assets) are aging and need regular monitoring, maintenance and rehabilitation. These assets are often placed under pavements and in close vicinity to each other. Managing them in a coordinated way is rational considering costs and disruption of services and communities caused by each intervention on the different assets. Recently, interest in practice as well as in research has grown to manage urban infrastructures in a coordinated way. This article reviews journal articles and grey literature to evaluate managing these infrastructures in an integrated way (i.e. the highest level of coordination) and describe possible obstacles for doing so. This article identifies seven main challenges of integrated multi-infrastructure asset management (IMAM) that need to be addressed by practitioners and researchers. These challenges are related to: (i) dependencies and interdependencies, (ii) data quality, availability and interoperability, (iii) uncertainties in modelling and decision-making, (iv) comparability, (v) problems of scale, (vi) problems of fit and (vii) problems of interplay. This article provides details on these challenges and discusses future research and practical directions.

Winter Road Maintenance (WRM) ensures road mobility and safety by mitigating adverse weather conditions. Yet, it is costly and environmentally impactful. Balancing these expenses, impacts, and benefits is challenging. Simulating winter maintenance services offers a potential new tool to find this balance. In this paper, we analyze Norway's WRM of state roads during the 2021-2022 winter season and propose an effort model. This model forms the computational core of the simulation, predicting the number of plowing, salting, and plowing-salting operations at any given location over the road network. This is a multi-linear regression model based on the Gaussian/OLS method and comprises three sub-models, one for each of the aforementioned operations. The key explanatory variables are: 1) level of service (LOS), 2) road width, 3) height above mean sea level, 4) Average Annual Daily Traffic (AADT), 5) snowfall duration, 6) snow depth, 7) number of snow days (fallen snow and drifting snow), 8) number of freezing-rain days, 9) number of cold days and 10) number of days with temperature fluctuations. The overall effort prediction accuracy for the winter season 2021-2022 was 71 %. The independent variables, the model's outcomes, and its results when applied to simulate the effects of LOS downgrading on a particular road stretch and estimating CO2 emission over the whole network, are discussed.

Pavement rutting poses a significant challenge in flexible pavements, necessitating costly asphalt resurfacing. To address this issue comprehensively, we propose an advanced Bayesian hierarchical framework of latent Gaussian models with spatial components. Our model provides a thorough diagnostic analysis, pinpointing areas exhibiting unexpectedly high rutting rates. Incorporating spatial and random components, and important explanatory variables like annual average daily traffic (traffic intensity), asphalt type, rut depth and lane width, our proposed models account for and estimate the influence of these variables on rutting. This approach not only quantifies uncertainties and discerns locations at the highest risk of requiring maintenance, but also uncover spatial dependencies in rutting (millimetre/year). We apply our models to a data set spanning eleven years (2010-2020). Our findings emphasise the systematic unexplained spatial rutting effect, where some of the rutting variability is accounted for by spatial components, asphalt type, in conjunction with traffic intensity, is also found to be the primary driver of rutting. Furthermore, the spatial dependencies uncovered reveal road sections experiencing more than 1 millimeter of rutting beyond annual expectations. This leads to a halving of the expected pavement lifespan in these areas. Our study offers valuable insights, presenting maps indicating expected rutting, and identifying locations with accelerated rutting rates, resulting in a reduction in pavement life expectancy of at least 10 years.

Heated pavements can be an effective method melt snow and ice on location where traditional snow plowing device is not practical or feasible. However, the energy use during operation limits the application of the technology. Desired innovations to further optimize the operational energy use may be stimulated by a sound understanding of the magnitude of the involved heat fluxes. In this study estimated these heat fluxes in a lab-oratory set-up and placed in a large walk-in cold room. Snow was melted on a heated asphalt concrete specimen at an air temperature of-5 degrees C. The inclination angle of the specimen was varied between 0 degrees, 2 degrees and 4 degrees to vary the run-off of meltwater. The melting time, drying time, run-off and energy consumption was recorded and the different heat fluxes were quantified. It was found that the snow melting time did not vary much between the different inclination angles. However, increased the inclination angle from 0 degrees to 2 degrees and 4 degrees reduced the drying time decreased by 30% and 47%, respectively. In resulting total energy use was reduced by 34% and 49%, respectively. The energy reduction was mainly contributed by using less energy for evaporation of meltwater, which was at 0 degrees the dominating heat flux. Although the relative contributions of the heat fluxes can vary between our study and different real-life snow melting systems, this laboratory study illustrates the importance of effective run-off to maximize the efficiency of heated pavements. Several innovative approaches to minimize the evaporative heat losses are discussed.

This study investigates the effect of moving the binding and adjusting the normal load on friction for a modern cross-country ski. The binding was moved 10 cm forward and backward from the normal position. The results showed that moving the binding backward on the ski reduced the friction at cold (-10 degrees C) and warm air temperatures (+5 degrees C). At intermediate temperatures (-2 degrees C), neither changes to the binding position nor the total load affected the friction coefficient. Measurement of pressure zone profiles revealed that moving the binding backward reduced the apparent contact area, increased the average contact pressure, increased the peak pressure in the rear zone and increased the spacing between the front and rear contact zones of the ski.

Residential segregation as a known consequence of rapid urbanization in developing countries is a complicated socio-economic phenomenon. Quantified description, analysis, and prediction of urban dynamics as a complex system have always been challenging issues. The recent intensive developments of multi-agent simulations as a solution to these problems still lack enough real-world examples. Our purpose in this research is to develop a spatially explicit model of residential segregation in urban space which accounts for a specific city’s infrastructure. The proposed agent-based simulation of the residential dynamics of Tehran over a 20-year period between 1996 and 2016 is based on the GIS datasets provided by Tehran Municipality and the Statistical Center of Iran.This is the first effort at presenting an agent-based model for Tehran’s residential segregation. We revised the “Schelling” segregation model in an attempt to customize it and arrive at an acceptable fit for Tehran. In addition to Schelling’s parameters, which comprise purely social factors, we identified several important socio-economic and spatial-environmental criteria, in turn categorizing them based on the AHP method. A certain number of the parameters like “neighborhood prestige” specifically belong to Tehran and are suggested for the first time. The proposed expert-based model was implemented in Netlogo. Validation of the resulting pattern using the Kappa indicator showed that the model simulated Tehran’s segregation pattern at a rate higher than 62%.

This study conducted friction experiments of three elastomer block materials on ice (low pressure injection of polyurethane (PU), injection moulding of a thermoplastic polyurethane (TPU) and vulcanization of rubber (RU)), with a linear tribometer operating under four different ambient air temperatures (-10 to 0 degrees C) and six different sliding velocities (0.3-5 m/s). At low temperatures (-10 degrees C and-5 degrees C), the RU material showed the highest dynamic and static friction. However, at high temperatures (-2 degrees C and 0 degrees C) the PU revealed the highest dy-namic friction and highest static friction at 0 degrees C. We discuss the physical phenomena's of elastomer friction on ice and in relation to the application of footwear slipping on ice.

Small differences in ski-snow friction results in large time gaps, and glide testing is therefore an important part of racing. To test ski-snow friction without the influence of changing weather, snow conditions and skier position is therefore valuable. In this study, a full-scale ski-snow tribometer was developed and we investigated the degree of precision obtainable for different snow types, speeds, between separate ski tracks and snow surface preparations. The precision within new snow test tracks was 1.45%, and changing between parallel tracks added another 1.03% to the precision. Measurements across several dendritic snow testbeds were associated with a further 2.39% contribution to the precision. On aged snow, better precision was obtained within and between tracks on the same snow surface.

Anti-icing chemicals are used during hoar frost situations to avoid slippery road conditions. During hoar frost situations, humidity is transported from the air to the road surface. This leads to continuous dilution of the applied solution, until it eventually freezes. The protection time for a certain anti-icing application during hoar frost situations is therefore limited, and depends both on the dilution process and the freezing process. In this work, a laboratory setup has been used to study the effect of three commonly used chemicals, with different hygroscopic properties on the dilution process. An increased mass transfer rate up to 30% was seen for all chemicals in the start of the dilution process. The effect quickly diminished and after only 60 min it returned to a similar rate as without any chemical present. (C) 2022 American Society of Civil Engineers.

[This corrects the article DOI: 10.3389/fspor.2022.894250.].

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.

Snow and ice on roads often lead to increased rolling resistance that makes roads less accessible and less attractive for cyclists. Introducing a minimum requirement for rolling resistance in winter maintenance of cycleways may increase the attractiveness of winter cycling. To control the rolling resistance level, an objective measurement method is needed. This article presents a new method for measuring rolling resistance for cyclists by using an instrumented bicycle. The new method utilizes measurements of pedaling power and resistive forces from gravitation, acceleration, and air drag to estimate the rolling resistance. Test results show that the method can measure the coefficient of rolling resistance, C-rr, with a precision, represented as the standard error of the mean, between +/- 0.005 (1 Hz, n = 9) and +/- 0.001 (1 Hz, n = 220). The accuracy of the method was verified in a test with known rolling resistance and the results yielded a mean accuracy of 96.5%.