Search

This study examines relationships between the dimensions of burnout and aspects of job satisfaction among road warrior salespeople. Considering the heavy costs associated with replacing these high value salespeople, greater understanding of burnout and job satisfaction among road warrior salespeople is important for both researchers and practitioners alike. The study first examines travel and hotel nights to refine the road warrior definition for salespeople. Next, the study investigates relationships between the burnout dimensions (personal accomplishment, depersonalization, and emotional exhaustion) and four job satisfaction dimensions (policy, pay, supervision, and customer). Findings indicate that emotional exhaustion directly affects satisfaction with policy, thus highlighting the importance and impact of emotional exhaustion on the initial dimension of the job satisfaction model. Then, indirect relationships between burnout dimensions (personal accomplishment and depersonalization) and job satisfaction are examined. Fourth, the study further examines the importance of satisfaction with policy in relation to three of the job satisfaction dimensions (pay, supervision, and customers). [ABSTRACT FROM AUTHOR]

Automated crack sealing could significantly benefit the maintenance of road pavements, but there is difficulty in depositing the correct volume of sealant material into the hidden crack space. A simulated model of the material flow within a crack space would allow the development of a predictive control scheme, such that the repair robot can apply suitable trajectories and operational parameters to accomplish neatly sealed surfaces. For the first time, the position‐based fluid (PBF) method, a computationally cheap and fast but approximate model of fluid flows, is studied for its feasibility for sealant flow simulation in the robotic injection crack sealing scenario. A Real‐to‐Sim experiment is performed, in which a PBF simulation of sealant in a virtual robotic crack sealing environment is mirrored from the physical lab setup. The fluid simulation is tuned to match the real‐world dynamics through comparison with 132 simulation runs, varying the artificial viscosity parameters U (fluid–fluid viscous interaction) and D (fluid–wall viscous interaction). It was found that U had a varied three‐stage influence on the simulation error while D 's negative influence on the simulation error only effectively applied to fluids satisfying 11\unicode{x0003C}U\unicode{x0003C}100. Through comparing the physical and virtual crack sealing results, the simulation was validated with an average fluid level error of 1.26 mm along a 3.1 mm wide, 16 mm deep and 80 mm long artificial crack, which shows the usefulness of the PBF method for robotic injection sealing. The accuracy and computational requirements of the PBF method are also discussed. [ABSTRACT FROM AUTHOR]

This paper develops a dynamic procedural model to understand the day-to-day evolution of road traffic and risky mode choice behaviour. We embed a modified Bureau of Public Roads function and a queueing model into a dynamic traffic flow model to estimate information on travel time and in-vehicle crowding. Then, the estimated information is fed into a risky mode choice model, with the joint consideration of intra- and inter-individual risk attitude heterogeneity. In order to reflect what may be happening in the real market, we establish a close loop with individuals' mode choice behaviours shaped by their travel experiences and historical information, the induced impact on traffic flow, and more importantly, the feedback and interplay between them. Our model delivers realistic and robust results, in which risk attitudes play the most prominent role. [ABSTRACT FROM AUTHOR]

Road defect detection is critical step for road maintenance periodic inspection. Current methodologies exhibit drawbacks such as low detection accuracy, slow detection speed, and the inability to support edge deployment and real-time detection. To solve this issue, we introduce an improved YOLOv8 road defect detection model. Firstly, we designed the EMA Faster Block structure using partial convolution to replace the Bottleneck structure in the YOLOv8 C2f module, and the enhanced C2f module was labeled as C2f-Faster-EMA. Secondly, we improved the model speed by introducing SimSPPF instead of SPPF. Finally, for the head, Detect-Dyhead, chosen to replace the original head, significantly improves the representation ability of heads without introducing any GFLOPs. Experimental results on the road defect detection dataset show that the improved model in this paper outperforms the original YOLOv8, with a 5.8% increase in average accuracy (mAP@0.5), and notable reductions of 22.33% in model size, 23.03% in parameter size, and 21.68% in computational complexity. [ABSTRACT FROM AUTHOR]

Road extraction from high-resolution remote sensing images (HRSIs) is one of the tasks in image analysis. Deep convolutional neural networks have become the primary method for road extraction due to their powerful feature representation capability. However, roads are often obscured by vegetation, buildings, and shadows in HRSIs, resulting in incomplete and discontinuous road extraction results. To address this issue, we propose a lightweight post-processing network called RIRNet in this study, which include an information inference module and a road direction inference task branch. The information inference module can infer spatial information relationships between different rows or columns of feature images from different directions, effectively inferring and repairing road fractures. The road direction inference task branch performs the road direction prediction task, which can constrain and promote the road extraction task, thereby indirectly enhancing the inference ability of the post-processing model and realizing the optimization of the initial road extraction results. Experimental results demonstrate that the RIRNet model can achieve an excellent post-processing effect, which is manifested in the effective repair of broken road segments, as well as the handling of errors such as omission, misclassification, and noise, proving the effectiveness and generalization of the model in post-processing optimization. [ABSTRACT FROM AUTHOR]

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%. [ABSTRACT FROM AUTHOR]

Detection of pavement diseases is crucial for road maintenance. Traditional methods are costly, time-consuming, and less accurate. This paper introduces an enhanced pavement disease recognition algorithm, MS-YOLOv8, which modifies the YOLOv8 model by incorporating three novel mechanisms to improve detection accuracy and adaptability to varied pavement conditions. The Deformable Large Kernel Attention (DLKA) mechanism adjusts convolution kernels dynamically, adapting to multi-scale targets. The Large Separable Kernel Attention (LSKA) enhances the SPPF feature extractor, boosting multi-scale feature extraction capabilities. Additionally, Multi-Scale Dilated Attention in the network's neck performs Spatially Weighted Dilated Convolution (SWDA) across different dilatation rates, enhancing background distinction and detection precision. Experimental results show that MS-YOLOv8 increases background classification accuracy by 6%, overall precision by 1.9%, and mAP by 1.4%, with specific disease detection mAP up by 2.9%. Our model maintains comparable detection speeds. This method offers a significant reference for automatic road defect detection. [ABSTRACT FROM AUTHOR]

The complex geological conditions in tunnels pose a huge challenge to the reliability of tunnel boring machine (TBM). However, existing reliability studies typically focus on core structures such as cutters and cutterheads, with less consideration given to the rest of the components that frequently fail. In this study, the reliability analysis and dynamic evaluation of TBM components with high failure rates are carried out relying on the Shanxi Central Yellow River Diversion Project. The life distribution and reliability variation characteristics of TBM components under different rock mass classes are investigated in terms of tunnelling time and tunnelling distance as two types of life data indexes. And the life index which is more suitable for the reliability evaluation of TBM components is identified by comparison. On this basis, a dynamic evaluation method for the reliability of TBM components under the condition of multi‐classes surrounding rock is proposed. This method can quickly evaluate the current reliability of TBM components and serve as the basis for preventive maintenance. The results of this study play a certain role in supplementing the reliability research of TBM and also provide a scientific basis for optimizing the design and maintenance strategy of TBM components. [ABSTRACT FROM AUTHOR]

Overloaded vehicle traffic accelerates road degradation, leading to early failures and high maintenance expenses. Identifying critical roads in areas like Baghdad, Iraq, is crucial for effective maintenance planning. Road maintenance involves systematic management to uphold road quality and functionality. However, the absence of weighing stations and awareness about critical roads in Baghdad exacerbated maintenance costs. This study aims to present a robust framework for identifying critical roads that necessitate weighing stations and subsequently determining the optimal locations for these stations in Baghdad. Through statistical analysis of responses collected from 61 road maintenance engineers, 22 influential factors for road criticality and weighing station location were identified. Some key factors emerged as crucial in identifying critical roads, including truck traffic volume, maintenance and construction costs, geopolitical importance and lane count. Furthermore, significant considerations for weighing station placement were identified, such as proximity to industrial hubs and drainage structures. Validation of the model through case studies affirmed its efficacy. Policy implications include the need for feasibility studies before weighing station implementation, data-driven decision-making, road criticality assessment and strategic weighing station placement. This research contributes to improved road management practices, aiding in decision-making for transportation infrastructure enhancement in developing countries like Iraq. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate the features and properties of stone mastic asphalt in the context of its potential for practical application in road repair works, aiming to create a high‐quality road surface and replace outdated structural materials with newer alternatives. The leading approach combines a systematic analysis of the issues submitted for consideration and an experimental study of the properties of stone mastic asphalt, which are important in the context of the practical use of this building material to ensure high quality parameters of the roadway and the timeliness of its repair, if necessary. Within the framework of this study, results were obtained that indicate a significant variety of approaches to determining the characteristics and properties of the material under consideration, clearly demonstrating current solutions to the problem of improving the performance of stone mastic asphalt by introducing special admixtures into its composition. The findings and conclusions of this research are valuable for road service employees and developers of modern construction materials, aiding in high‐quality road surface repair and creating durable asphalt for safe road use in the future. [ABSTRACT FROM AUTHOR]

For road repair, a rapid method is much desired to minimize traffic disclosure. This study proposes a new method for granular materials to gain strength rapidly. Using microbial carbonation for reactive MgO cement (RMC), a pre-bio-carbonation method is proposed to allow RMC treated materials to gain 80% of the final strength in 2 h when 8% RMC content is used. In this proposed method, a biosolution that has been premixed several hours before application is applied together with RMC for soil treatment. The carbonates that have already been generated from a urea hydrolyzation using ureolytic bacteria allow an immediate carbonation of RMC. As a result, the strength of the bio-carbonated RMC treated materials gain strength rapidly due to a quick formation and crystallinity of hydrated magnesium carbonates. To study the mechanisms and effectiveness of this method, laboratory tests were carried out to evaluate the strength development with time in the RMC treated granular materials and the conditions controlling the amount of strength gain. The influence of biosolution mixing time on the strength gain and the microstructural development of the bio-carbonated RMC are also discussed. [ABSTRACT FROM AUTHOR]

The post-earthquake emergency period, which is a sensitive time segment just after an event, mainly focuses on saving life and restoring social order. To improve the seismic resilience of city road networks, a resilience evaluation method used in the post-earthquake emergency period is proposed. The road seismic damage index of a city road network can consider the influence of roads, bridges and buildings along the roads, etc. on road capacity after an earthquake. A function index for a city road network is developed, which reflects the connectivity, redundancy, traffic demand and traffic function of the network. An optimization model for improving the road repair order in the post-earthquake emergency period is also developed according to the resilience evaluation, to enable decision support for city emergency management and achieve the best seismic resilience of the city road network. The optimization model is applied to a city road network and the results illustrate the feasibility of the resilience evaluation and optimization method for a city road network in the post-earthquake emergency period. [ABSTRACT FROM AUTHOR]

Roads play a crucial role in urban transportation by facilitating the movement of materials within a city. The condition of road surfaces, such as damage and road facilities, directly affects traffic flow and influences decisions related to urban transportation maintenance and planning. To gather this information, we propose the Detecting and Clustering Framework for sensing road surface conditions based on crowd-sourced trajectories, utilizing various sensors (GPS, orientation sensors, and accelerometers) found in smartphones. Initially, smartphones are placed randomly during users' travels on the road to record the road surface conditions. Then, spatial transformations are applied to the accelerometer data based on attitude readings, and heading angles are computed to store movement information. Next, the feature encoding process operates on spatially adjusted accelerations using the wavelet scattering transformation. The resulting encoding results are then input into the designed LSTM neural network to extract bump features of the road surface (BFRSs). Finally, the BFRSs are represented and integrated using the proposed two-stage clustering method, considering distances and directions. Additionally, this procedure is also applied to crowd-sourced trajectories, and the road surface condition is computed and visualized on a map. Moreover, this method can provide valuable insights for urban road maintenance and planning, with significant practical applications. [ABSTRACT FROM AUTHOR]

Facing budgetary limitations and deteriorating infrastructure, government agencies and municipalities are contemplating a shift from reactive maintenance and repair (M&R) approaches to preventive M&R for road infrastructure. However, ambiguities concerning the long-term returns of preventive M&R for road systems deter their commitment to this transition. Although previous studies have demonstrated the advantages of preventive M&R, a comprehensive understanding of its value remains insufficient, particularly considering budget restrictions, varying levels of required serviceability, the types of preventive treatments, and the extent of preventive M&R. To bridge this knowledge gap, this study evaluated the economic impact of preventive treatments over the lifespan of urban highways, taking an urban highway in South Korea as a case study. The analysis results reveal a significant decrease in costs beginning after the first decade of the systematic applications of preventive M&R, with potential savings ranging between 20% and 30% over a half-century period. The findings will inform road M&R agencies or municipalities about cost profiles when employing preventive treatment methods (PTMs) for road M&R, thereby facilitating PTM implementation to reduce long-term road M&R budgets. Practical Applications: Government agencies and municipalities, grappling with limited budgets and aging road infrastructure, increasingly are considering preventive maintenance and repair (M&R) strategies over traditional reactive approaches. This shift, although promising, faces uncertainties regarding its long-term economic benefits. Prior research has underscored the advantages of preventive M&R, but a thorough understanding of its financial implications, particularly in the context of budget limitations, diverse serviceability needs, and various preventive measures, still is lacking. This study bridges this gap by evaluating the economic impact of preventive M&R on urban highways, using a South Korean urban highway as a case study. Our findings indicate a notable reduction in costs, especially after the first decade, with potential savings of 20%–30% over 50 years. These results offer crucial insights for road maintenance agencies and municipalities, presenting a clear cost–benefit analysis of implementing preventive M&R strategies. Such information is pivotal in aiding these entities to make informed decisions, potentially leading to significant reductions in long-term road maintenance budgets. [ABSTRACT FROM AUTHOR]

The rapid identification of structural damage in pavements remains a challenging issue. This paper proposes an automatic identification method that combines nondestructive testing, the ground penetrating radar (GPR), and object detection (OD) models for pavement structural assessment. Radar wave image data were collected from a 40 km single-lane track road and processed to create the training data set of 2,000 labeled images with two types of structural distress. Four OD models, including Faster R-CNN, YOLOv5, YOLOv8, and DETR, were adapted and trained on the data set to recognize structural damage in the images, and their prediction performances were compared. The results demonstrated that YOLOv8 was the state-of-the-art (SOTA) model, which achieved precise identification of the location and type of pavement structural damage with a mean average precision (mAP) of 98.5%. This study contributes to the improvement of latest OD models in the non-destructive inspection, facilitating the development of real-time automated pavement assessment and maintenance decision systems. Practical Applications: This research addresses a common problem in civil engineering: how to quickly detect all hidden damage in pavement structures over long stretches of highway projects. Although there has been previous research in this area, finding a more reliable solution has proven to be a challenging goal. This study presents a method that utilizes the geological radar, a nondestructive testing technology, meaning it will not harm the road, along with artificial intelligence (AI) computer models to identify damage under the pavement surface. Think of it as teaching a computer to recognize cracks or looseness in the radar images of the road. Four different computer models were trained using 2,000 road images with known damage to teach them how to identify different damage features. The results were impressive—the computer could accurately identify the location and type of damage with a high level of confidence. In fact, the best model achieved a 98.5% score measured by mean average precision, which indicates its effectiveness. This research has significant practical applications as it streamlines the process of inspecting roads for structural problems, making it faster and more efficient. It can help with real-time evaluations and decisions about road maintenance. [ABSTRACT FROM AUTHOR]

Road markings are essential features to convey important information to various roadway users such as pedestrians, bicyclists, and motorists. Although mobile laser scanning (MLS) technology provides dense and spatially accurate data, automated identification of road markings remains a challenge. Although approaches have been developed to detect road markings from point clouds, prior studies have not thoroughly investigated road discretization and road marking clustering, which are essential for effective instance-level identification and management of road markings. To improve road discretization, our approach uses line segmentation to divide the trajectory of MLS data and fit circles to individual segments. By calculating the intersection between the road points and the circle's center for each trajectory segment, this method ensures no overlap occurs when discretizing roads with sharp turns. To improve road marking clustering, the discretized points are rasterized onto a two-dimensional (2D) image and clustered using connected component labeling. Individual markings are skeletonized to detect junctions and corners, which enables the separation of underclustered road markings. Overclustered road markings are then merged using a rule-based approach optimized with a traffic line manual. The proposed approach was evaluated through extensive experiments using 28 MLS data sets containing 2,340 road marking clusters with complex geometry and wear. Out of these, only 64 instances (2.7%) were falsely clustered, achieving a precision rate of 97.5% and a recall rate of 99.7%. Practical Applications: Efficient road maintenance and improved road safety rely heavily on the clear visibility of road markings, which guide diverse users, such as pedestrians, cyclists, and drivers. Traditional methods for inspecting these markings are often time-consuming and prone to human error. In this study, we use MLS technology to collect comprehensive and precise data on roadways. We have developed an automated technique that significantly enhances the identification and management of road markings. This method accurately rasterizes and segments roadway lidar data—even in areas with sharp turns—to apply advanced image processing for the recognition of individual markings, including those that are substantially worn or have complex geometries. This simplifies the geometric data in a GIS database such that each line object is more representative of how someone would manually digitize the marking. Tested on numerous data sets, our approach achieved an accuracy rate of over 97% in clustering individual road markings. The developed approach not only streamlines roadway maintenance but also offers potential benefits for intelligent transportation systems. It contributes to safer autonomous vehicle navigation and supports informed decision-making regarding road infrastructure, ultimately improving all road users' experience. [ABSTRACT FROM AUTHOR]

The development and integration of automated driving systems in vehicles hold substantial promise for fostering enhanced efficiency, environmental sustainability, and safety in transportation. Notably, at the lower levels of automation (LI, L2), the lane-keeping system emerges as a widely adopted automated driving feature, ensuring the vehicle's alignment within its designated lane. With the recent introduction of new European regulations mandating the inclusion of emergency lane-keeping systems in all new vehicles starting July 2022, a growing prevalence of such systems is anticipated in the forthcoming decades. The precision and reliability of these systems in accurately detecting road markings and their distinctive features are paramount for achieving safe and intelligent mobility solutions. To fully capitalize on the advantages these systems offer, they need to expand their operational design domain. This necessitates a comprehensive understanding of their performance across diverse road design and maintenance conditions, supporting road operators in updating standards and maintenance protocols. The primary objective of this study is to investigate how various road characteristics impact the performance of lane-keeping assistant systems. Within this framework, the paper presents an experimental evaluation of Lane-Keeping System (LSS) performance conducted on two-lane rural roads. Advanced technologies for road monitoring and LSS were employed under different road and driving conditions. Through rigorous data analysis and the application of statistical models, variables significant to the fault probability of LSS were identified, highlighting the role played by horizontal curvature and driving speed. Results underscore the relevance of horizontal curvature as a critical factor constraining the physical infrastructure, shaping the operational design domain of LSS. This research contributes valuable insights toward optimizing lane-keeping assistant systems, thereby advancing the development and deployment of safe and efficient automated driving systems in diverse road scenarios. [ABSTRACT FROM AUTHOR]

The road’s infrastructure is crucial for growth, development, and forming the backbone of any country's economy. The accurate detection and classification of road defects for optimal road maintenance is a challenging task due to the varied types of road defects of different severity. This paper presents a transfer learning model for the detection and classification of road defects based on types of defects (cracks and potholes) and severity. A new local dataset was introduced consisting of road surface images (defects and non-defects) of Kaduna metropolis, Nigeria. The types and severity of the defects were grouped as non-defect, low-pothole, low-crack, moderate-pothole, moderate-crack, highpothole, and high-crack. The model was developed by extracting the features using pretrained VGG19 and EfficientNetB3. The extracted features were concatenated and evaluated on the locally gathered datasets. The pretrained EfficientNetB3 achieved an accuracy 98.1% higher than the 97.9% accuracy of VGG19. The concatenated model (VGG19+EfficientNetB3) achieved 98.5% accuracy, which outperformed the two pretrained models, VGG19 and EfficientNetB3. This study demonstrated the benefit of combining the strengths of VGG19 and EfficientNetB3, for improved performance and efficiency in road defect detection and classification tasks. [ABSTRACT FROM AUTHOR]

Roadway pavements constitute a vital component of transportation infrastructure, and their effective maintenance and rehabilitation are paramount to ensure optimal functionality. Efficient road management systems save time and resources and rely on accurate pavement condition surveys. To ensure optimal functionality, it is crucial to establish a pavement management system (PMS) that involves assessing the current state of the road network and predicting future conditions. In this research, we assessed the effectiveness of asphalt pavements using two well-known approaches: The Present Serviceability Index and The Pavement Condition Index (PCI). The PCI, a numerical index, is a reliable indicator to assess the operational condition and structural reliability of pavements based on visual assessments of distress type, severity, and quantity. We aimed to classify the various types of distress observed on Road No.80 and estimate the pavement condition index. We identified nine out of nineteen distress problems in the pavement section under study through a comprehensive pavement distress condition rating approach. The most prevalent distress types included longitudinal and transverse cracking, rutting, potholes, edge cracking, alligator cracking,patching and weathering and raveling. [ABSTRACT FROM AUTHOR]

This document is a personal account by Danilo Haudenschild Fischer, a school teacher in Arroyo Ata, about his daily life and activities in Nordrand, Paraguay. He wakes up early to milk cows and divide the work with his siblings. He attends school with both Germans and Paraguayans, and there is no longer a stigma against mixing. Danilo prefers his school in Arroyo Ata over the one in Nueva Germania due to better performance and rumors of drug activity. In his free time, he plays volleyball and occasionally participates in local football competitions. Danilo believes that his life is good, but mentions the need for better road maintenance and English classes. [Extracted from the article]

This study delves into quantifying the adverse effects of road damage on users, particularly focusing on the increased travel time and consequent financial burdens stemming from delayed repairs. Utilizing a comparative method, the research underscores notable reductions in speed and prolonged travel times due to damaged roads, leading to substantial economic losses for road users. To streamline the estimation of road user costs (RUC), the study proposes a simulation model that incorporates varying traffic volumes and repair delays. This model demonstrates a high level of accuracy in estimating RUC, revealing heightened sensitivity to fluctuations in traffic volume and repair delays compared to agency costs. Consequently, the research underscores the imperative of implementing effective repair strategies to alleviate these impacts efficiently, thereby emphasizing the significance of timely infrastructure maintenance in mitigating financial burdens on road users. [ABSTRACT FROM AUTHOR]

Icy and snowy road conditions cause problems in many countries where temperature often drops below 0 °C. Preventive actions are necessary to keep roads ice-free and to optimize maintenance operations. Accurate road surface temperature and road condition forecasts are essential help for road maintenance crews to plan their actions. The Finnish Meteorological Institute (FMI) has produced road weather forecasts for many years with the in-house road weather model RoadSurf to aid local road authorities. Recently, FMI published an open-source road weather model library that consists of RoadSurf functions. The open publication provides an opportunity for many institutes and companies to use the library in road weather forecasting. The evaluation of the library shows that it is well suited for forecasting road surface temperature. [ABSTRACT FROM AUTHOR]

Monitoring pavement conditions is critical to pavement management and maintenance. Traditionally, pavement distress is mainly identified via accelerometers, videos, and laser scanning. However, the geographical coverage and temporal frequency are constrained by the limited amount of equipment and labor, which sometimes may delay road maintenance. By contrast, crowdsourced data, in a manner of crowdsensing, can provide real-time and valuable roadway information for extensive coverage. This study exploited crowdsourced Waze pothole and weather reports for pavement condition evaluation. Two surrogate measures are proposed, namely, the Pothole Report Density (PRD) and the Weather Report Density (WRD). They are compared with the Pavement Quality Index (PQI), which is calculated using laser truck data from the Tennessee Department of Transportation (TDOT). A geographically weighted random forest (GWRF) model was developed to capture the complicated relationships between the proposed measures and PQI. The results show that the PRD is highly correlated with the PQI, and the correlation also varies across the routes. It is also found to be the second most important factor (i.e., followed by pavement age) affecting the PQI values. Although Waze weather reports contribute to PQI values, their impact is significantly smaller compared to that of pothole reports. This paper demonstrates that surrogate pavement condition measures aggregated by crowdsourced data could be integrated into the state decision-making process by establishing nuanced relationships between the surrogated performance measures and the state pavement condition indices. The endeavor of this study also has the potential to enhance the granularity of pavement condition evaluation. [ABSTRACT FROM AUTHOR]

In maintaining roads and ensuring safety, promptly detecting and repairing pavement defects is crucial. However, conventional detection methods demand substantial manpower, incur high costs, and suffer from low efficiency. To enhance road maintenance efficiency and reduce costs, we propose an improved algorithm based on YOLOv8. Our method incorporates several key enhancements. First, we replace conventional convolutions with a module composed of spatial-to-depth layers and nonstrided convolution layers (SPD-Conv) in the network backbone, enhancing the capability of recognizing small-sized defects. Second, we replace the neck of YOLOv8 with the neck of the ASF-YOLO network to fully integrate spatial and scale features, improving multiscale feature extraction capability. Additionally, we introduce the FasterNet block from the FasterNet network into C2f to minimize redundant computations. Furthermore, we utilize Wise-IoU (WIoU) to optimize the model's loss function, which accounts for the quality factors of objects more effectively, enabling adaptive learning adjustments based on samples of varying qualities. Our model was evaluated on the RDD2022 road damage dataset, demonstrating significant improvements over the baseline model. Specifically, with a 2.8% improvement in m A P and a detection speed reaching 43 FPS, our method proves to be highly effective in real-time road damage detection tasks. [ABSTRACT FROM AUTHOR]

To ensure the safety of vehicle travel, the maintenance of road infrastructure has become increasingly critical, with efficient and accurate detection techniques for road cracks emerging as a key research focus in the industry. The development of deep learning technologies has shown tremendous potential in improving the efficiency of road crack detection. While convolutional neural networks have proven effective in most semantic segmentation tasks, overcoming their limitations in road crack segmentation remains a challenge. To address this, this paper proposes a novel road crack segmentation network that leverages the powerful spatial feature modeling capabilities of Swin Transformer and the Encoder–Decoder architecture of DeepLabv3+. Additionally, the incorporation of a multi-scale coding module and attention mechanism enhances the network's ability to densely fuse multi-scale features and expand the receptive field, thereby improving the integration of information from feature maps. Performance comparisons with current mainstream semantic segmentation models on crack datasets demonstrate that the proposed model achieves the best results, with an MIoU of 81.06%, Precision of 79.95%, and F1-score of 77.56%. The experimental results further highlight the model's superior ability in identifying complex and irregular cracks and extracting contours, providing guidance for future applications in this field. [ABSTRACT FROM AUTHOR]

This research note examines the early development of the statute labor system in Quebec. The focus is on the Eastern Townships region where—for cultural as well as geographic reasons—the road building and maintenance system was more community-based than in the rest of the province. That region welcomed the introduction of municipal governance and the property assessment system in the 1840s, but rural ratepayers remained determined to pay their road taxes with their own labor. As a result, despite the inefficiencies of the labor tax and the criticisms of road improvers, provincial legislation recognized it as a permissible option, thereby allowing it to survive until after the turn of the 20th century. [ABSTRACT FROM AUTHOR]

This paper examines the road corvée, a practice of using unpaid labour for road maintenance, in the nineteenth and twentieth century. I focus on the case of Estonia, where the road corvée, originating in the feudal economic system, persisted surprisingly long, being abolished only in 1959. Earlier studies on the road corvée have focused mainly on road construction and have therefore failed to recognise the use of the practice beyond absolutist Europe and colonial Africa. Focusing on maintenance reveals that the corvée was also widespread in twentieth-century Europe. I examine how the road corvée was organised and debated to reveal what inhibited and what accelerated its abolition. The study shows how maintenance practices can be deeply embedded in social and economic structures – like the agricultural system in this case – and ultimately be highly inert and resistant to change. [ABSTRACT FROM AUTHOR]

A nation's development depends on its transport networks, particularly the road network, which plays a crucial role in the country's economic and social advancement and well-being. However, roads are subject to deterioration due to weather conditions, traffic loading, and construction quality. If they are not maintained properly, they will quickly worsen over time, resulting in reduced mobility and accessibility. To develop and maintain a good road network, careful planning is needed, which covers all aspects of road maintenance, funding, construction, quality, and other criteria. However, due to limited budgets, not all roads can be maintained and rehabilitated at the same time. Road maintenance priority and optimal use of insufficient funding are the most challenging problems the authorities face. The development of a systematic approach is essential to formulate appropriate maintenance policies. This is why the concept of road maintenance prioritization is essential. Additionally, industry experts have also identified a lack of a Multi-Criteria Decision Making (MCDM) technique that can incorporate the views of all Decision Makers (DMs) in the road maintenance prioritization process. This study aims to propose a framework for prioritizing road maintenance using MCDM techniques in a fuzzy environment. A case study that considers 20 criteria was conducted. The study integrated two MCDM techniques, namely the Fuzzy Best-Worst Method (BWM) and VIKOR, to help DMs evaluate and rank the alternatives, on the basis of the selected maintenance criteria. The aim of this framework is to enhance the decision-making process with impartiality and reliability and to assist in reaching an optimal decision. By comparing the Q values for each alternative, A5 was revealed to have higher priority over the other roads in terms of maintenance and rehabilitation activities. [ABSTRACT FROM AUTHOR]

The long segment scheme allowing contractors to oversee road maintenance still has drawbacks. For example, non-compliance, such as delaying preservation for road issues, persists due to small penalties. This leads service providers to neglect road performance without objection to fines. This research aims to provide sufficient incentives for contractors to comply with the implementation of the late delivery penalty rate of road service levels. This research used an experimental method to test two formulas for the late delivery penalty rate of road service levels on two road sections in Central Java and Special Region of Yogyakarta, Indonesia. During the experiment, the time taken by service providers to fulfill the road service level on the two different road sections was measured and recorded. In the first road section, the previously used formula resulted in a penalty of only $7.39, while the developed formula yielded $122.17. Furthermore, in the second road section, the government formula led to a penalty of $375.89, whereas the developed formula resulted in a fine amount of $1,468.99. The results showed that the penalty value given to contractors for two road segment trials using the research formula was 16 and 4 times higher than the formula used by the Directorate General of Highways. In addition, the developed formula takes into account other road performance indicators such as potholes with a diameter < 10 cm and depth < 4 cm, ponding, and uneven patching. [ABSTRACT FROM AUTHOR]

The increasing impact of the greenhouse effect on ecosystems is prompting transportation agencies to seek methods for reducing CO2 emissions during pavement construction and maintenance. Additionally, the laboratory mix design process, which involves selecting aggregate gradation and binder content, is time-consuming and labor-intensive. To accelerate the traditional mix design procedure, this study presented a mix design procedure that can automatically determine gradation and binder content based on machine learning (ML) and a meta-heuristic algorithm. Specifically, ML approaches were employed to model the relationship between volumetric properties (mixture bulk specific gravity (Gmb) and air void (VV)) and both mixture component properties and mixture proportion, based on a dataset collected from literature with 660 mixture designs. Integrated with the prediction of ML models and the modified multi-objective grey wolf optimization (MOGWO) algorithm, an automatic asphalt mix design was proposed to pursue three goals, including VV, cost, and CO2 emission. The results indicated that least squares support vector regression (LSSVR) and eXtreme gradient boosting (XGBoost) achieved the highest prediction accuracies (correlation coefficient: 0.92 for VV and 0.96 for Gmb). The MOGWO algorithm successfully found the 26 optimal mix designs for the case of VV vs. cost vs. CO2 emission. Compared to the traditional laboratory design, the optimal mixture with VV of 4% achieves a cost saving of 2.46% and a reduction of 4.03% in carbon emission. The volumetric properties of the mixtures output by the approach also align closely with values measured in a laboratory [ABSTRACT FROM AUTHOR]

Research on applying artificial intelligence to autonomously detect road surface damages contributes positively to road traffic maintenance and management. This study introduces the Road Traffic Infrastructure Intelligent Management System (RTI IMS) software, utilizing the Yolo V5 programming platform. This software is designed to automatically identify road surface damages during operation. Based on machine learning image recognition technology, the model is trained with a diverse database of road surface damage images from Japan, India, and Vietnam. Additionally, to improve the inference efficiency of the RTI IMS software, the study uses the Mosaic method, tripling the variation to diversify input data during training, thereby minimizing prediction errors. The research results in the successful development of the RTI IMS software, capable of autonomously detecting road surface damages and providing corresponding images to traffic management authorities. This study significantly contributes to the modernization of current road traffic management practices. [ABSTRACT FROM AUTHOR]

The major highway in Sri Lanka that links the capital, Colombo, with the second capital, Kandy, passes through Kadugannawa, characterized by steep hills. The geological and geomorphological setting of the terrain often leads to slope failures. The objective of this study is to interpret the key factors influencing the slope failures that occurred in close proximity at two separate locations with two different slope conditions. Typical local and regional brittle and ductile structures include fault scarps, deep-seated detachments, and variable folding. According to our results, one of the studied locations experienced translational landslides because of weakened basement rock surfaces, hydrophilic clay minerals, and anthropogenic influences, whereas the other location experienced multiple stages of mass movement influenced by inhomogeneous colluvial soil and regional, geological, and hydrogeological conditions. Based on the present study, it can be concluded that geological studies must be carried out within the local area rather than at the regional scale. Otherwise, the constructions for the prevention of landslides in complicated geological settings will fail or may not be used for a long period. Moreover, consideration of future climate change is essential when undertaking construction in challenging terrains. [ABSTRACT FROM AUTHOR]

Roadway pavements constitute a vital component of transportation infrastructure, and their effective maintenance and rehabilitation are paramount to ensure optimal functionality. Efficient road management systems save time and resources and rely on accurate pavement condition surveys. To ensure optimal functionality, it is crucial to establish a pavement management system (PMS) that involves assessing the current state of the road network and predicting future conditions. In this research, we assessed the effectiveness of asphalt pavements using two well-known approaches: The Present Serviceability Index and The Pavement Condition Index (PCI). The PCI, a numerical index, is a reliable indicator to assess the operational condition and structural reliability of pavements based on visual assessments of distress type, severity, and quantity. We aimed to classify the various types of distress observed on Road No.80 and estimate the pavement condition index. We identified nine out of nineteen distress problems in the pavement section under study through a comprehensive pavement distress condition rating approach. The most prevalent distress types included longitudinal and transverse cracking, rutting, potholes, edge cracking, alligator cracking ,patching and weathering and raveling.

The growth of road maintenance has led to a shift in priorities, with greater focus on sustainability. The field of roads seeks to provide multiple alternatives to achieve the best road performance. By emphasizing the financial , environmental and socially benefits of achieving sustainability, which is one of the most important things baffling the world now, the study developed a framework for collecting, investigating, distinguishing and integrating information about value engineering and sustainability and integrating them economically, environmentally and socially to achieve sustainability through value engineering. The questionnaire was submitted digitally and on paper and distributed to among those with experience. Information was collected by asking respondents to fill out a questionnaire in either Arabic or English. Twenty-seven questions were asked in four parts of the survey. Everyone who participated was Egyptian. It was sent to 132 professionals in the field. There were only 100 people who filled out the survey. The first part of the questionnaire deals with details of the respondents' background, such as their occupations and length of service in the bandits. Next, you'll find details about people's shared understanding of violent extremism and sustainability, as well as some suggested links based on this shared understanding.