Road Infrastructure Management Research Articles

Geographic information system for reporting damage and road grouping based on website using waterfall method and k-means method

Roads are a vital land transportation infrastructure and transportation route. One of the functions of roads is as a driving factor in the development process and equitable distribution of development in a region. Road damage that occurs disrupts the activities and routines of residents. Lhokseumawe City as the center of economic and social development in its region requires an efficient system for the management and maintenance of road infrastructure. The reason is that many road damages occur in Lhokseumawe City that have not been repaired due to limited damage information so reports are needed from residents to find out the location of the road damage so that it is easier for the Lhokseumawe City government to follow up on the report. With these problems, a geographic information system for reporting road damage is created. It is hoped that the government can follow up on the report and prioritize damage with the highest group, which groups road damage using the clustering method. This web-based information system is designed using the UML (Unified Modeling Language) model with the PHP programming language and uses MySQL as its database. With this system, the government can receive reports of existing damage and group the damage into three levels to determine the priority of road repairs to be carried out.

Use of a multi-criteria approach to analyze the degradation of reinforced concrete beam bridges. Three case studies in the north west of Algeria

The main motivation for this research is to develop assessment and estimation tools for the degradation state of existing civil engineering structures. The longevity of a structure is influenced by a complex set of interconnected phenomena and parameters. Therefore, we adopt a multicriteria approach aimed at better identifying these elements, particularly the most influential ones. This study focuses on assessing the degradation of reinforced concrete beam bridges by developing a digital tool based on the Analytic Hierarchy Process (AHP). We have identified 30 factors impacting degradation, categorized as internal factors, such as the condition of structural elements, and external factors, such as age and environment. From these factors, we calculate a Global Bridge Degradation Index (GBDI) that reflects the overall condition of the bridge. This index allows us to establish a classification of the bridges, ranging from no danger to a critical state requiring immediate intervention. The innovation of our model lies in its ability to provide a quantified estimation of the degradation state while incorporating an unlimited number of factors and criteria. After applying this model to three bridges in Algeria, we observed satisfactory results. The ultimate goal is to provide managers with tools for reflection and comparison to assess the condition of bridges and consider the necessary actions for their maintenance, repair, or replacement, thereby contributing to the improvement of the management and safety of road infrastructures.

The Risk Management And Usage Of The Net Present Value-At-Risk To Evaluate Indonesian Toll Road

This study evaluates the financial feasibility and risk management of toll road infrastructure so as characteristics of toll road projects prone to risks and uncertainties. The study was conducted using case study and qualitative methods. The parameters used to determine the fair value of the project through parameters such as NPV, IRR, Payback Period, NPV at risk, and Risk Adjusted Return on Capital (RAROC) for financial and risk-based feasibility evaluation. This study also conducted a risk assessment in risk management analysis. The study provides various illustrations of possible outputs (NPV) and risk variability so that it can provide a more communicative and varied offer and recommendation, especially for investors as a consideration in making investment decisions. The results of the risk assesment process showed a low to the very high-risk level that requires different treatment. This study contributes understanding the investment in Indonesia toll road project

Network-level crash risk analysis using large-scale geometry features

Road traffic crashes are common occurrences that create substantial losses and hazards to society. A complex interaction of components, including drivers, vehicles, roads, and the environment, can impact the causes of these crashes. Due to its complexity, crash identification, and prediction research over large-scale areas faces several obstacles, including high costs and challenging data collecting. This study offers a method for large-scale road network crash risk identification based on open-source data, given that roadways’ horizontal and vertical geometric alignment is crucial in highway traffic crashes. This methodology includes a comprehensive technique for feature extraction from horizontal curves (H-curves) and vertical curves (V-curves) and a novel way of combining the XGBoost model’s attributes with the Harris Hawks Optimization (HHO) algorithm—referred to as the HHO-XGBoost model. Using this model on the road geometry-crash risk dataset developed specifically for this study, the HHO approach adaptively identifies the optimal set of XGBoost hyperparameters and yields favorable outcomes. This study creates a three-dimensional road geometry database that may be utilized for various road infrastructure management, operation, and safety in addition to completing a tiered risk analysis of “region-road-segment” for large-scale road networks. It also offers direction on using swarm intelligence algorithms in integrated learning models.

Advancing road safety strategy development: A data-driven multi-objective optimisation integrated approach

Road traffic accidents pose a significant global health concern, with an alarming 1.19 million fatalities reported in 2021. Traditionally, strategies to address this challenge have relied on expert input and subjective evaluations. This study introduces an adaptive and novel two-stage model that minimizes expert interference by integrating multi-objective optimisation with association rule mining. This innovative approach provides a systematic framework to enhance the efficiency of decision-making and optimise resource allocation outcomes in road infrastructure management, facilitating adaptability to diverse objectives. A case study in Utrecht from iRAP validates the efficiency of the approach, demonstrating significant improvements across various objectives by using the non-dominant sorting genetic algorithm and enabling road local authorities to tailor investment plans to their specific road network characteristics by crash database mining. However, the methodology requires refinement, particularly in identifying risk levels considering the interactive effects of multiple road attributes. In conclusion, while representing a substantial advancement, further refinement is necessary to fully realize its potential in enhancing road safety.

Assessing Multifaceted Effects of Speed Humps and Bumps: Travel Time, Safety, and Environmental Considerations

This study focuses on investigating the significant impacts of speed breakers on various parameters, including travel time delays, vehicle speeds, fuel consumption, pavement maintenance costs, and vehicular exhaust emissions. Field data was collected and analyzed to assess the effects of different types of traffic calming measures on these parameters. The findings provide valuable insights into the implications of speed breakers on road safety, environmental pollution, and overall road infrastructure management. The results reveal that the implementation of speed humps, speed bumps, and triple bumps effectively slows down vehicles, as evidenced by considerable reductions in the 85th percentile speeds. The reduction percentages were 41.65% for speed humps, 73.52% for speed bumps, and 86.27% for triple bumps. This indicates the effectiveness of these traffic calming measures in improving road safety by reducing vehicle speeds. However, the presence of speed breakers also leads to increased travel time delays. On average, traversing stretches with speed humps, speed bumps, and triple bumps resulted in delays of 9.31, 16.42, and 29.51 seconds, respectively. While the individual delay times may appear relatively short, the cumulative effect of multiple speed obstacles along a road needs to be considered. Another significant impact observed is the increased fuel consumption associated with speed breakers. The study found that for every 100 km of travel, motorcycles and passenger cars consumed approximately 12.07 km and 27.37 km of additional fuel, respectively, when the density of speed breakers was 1.33/km. This translates to a fuel consumption increase of 13.73% for motorcycles and 37.74% for passenger cars. Furthermore, the presence of speed humps was found to contribute to pavement deterioration, as indicated by decreased Pavement Condition Index (PCI) values. The study also revealed that sections with speed humps incurred significantly higher maintenance costs compared to sections without speed humps. The increase in maintenance cost ranged from 100 to 264% across different road sections, with higher traffic volumes leading to greater cost escalation. Additionally, the study confirms that lower vehicle speeds, particularly between 0-15 km/hr, are associated with higher emissions of pollutants, including carbon monoxide (CO) and other pollutants. This highlights the environmental implications of speed breakers and their contribution to urban air pollution. Doi: 10.28991/CEJ-2024-010-07-07 Full Text: PDF

A Conception of Collaborative Decision Support Systems: Approach and Platform Architecture

The paper describes a general conception of collaborative decision support systems, in which teams providing decision support a) are formed flexibly in accordance with the problem and b) consist of both human experts and intelligent agents implementing AI methods and techniques. An analysis of the key problems of creating collaborative decision support systems based on the collaboration of humans and AI is carried out, the following problems are highlighted: ensuring interoperability (mutual understanding) between heterogeneous team members, reconciling differing positions of participants, ensuring trust between participants, ensuring the effectiveness of joint actions planning and maintaining a balance between predefined workflows and self-organization. Principles for constructing such systems have been formed, offering solutions to the identified problems. In particular, it is proposed to employ an ontology-oriented representation of information about the problem (in the form of multi-aspect ontology), a set of methods for monitoring team activities, reputation scheme, elements of explainable AI, as well as mechanisms of limited self-organization. The proposed concept forms the basis of a software platform for the development of collaborative decision support systems, the main architectural provisions of which are also presented in the paper. The use of the platform is illustrated by an example from the field of rational management of road infrastructure and the creation of a collaborative DSS for the development of measures to reduce road accidents.

Accounting for traffic disturbance in road infrastructure management: Optimal maintenance and rehabilitation planning for the society

Maintaining a high level of network performance at minimal cost is a central objective for road infrastructure management. Going beyond agency costs, decisions must consider the economic and environmental impacts of maintenance and repairs strategies due to traffic delays on commuters and providers of goods and services. A dynamic programming model is presented to determine optimal management policies for current and proposed paradigms by selecting the best maintenance and rehabilitation activities at each time step over the time horizon given the concomitant and predicted states of the bridges within the network as well as predicted future origin-destination flows. The empirical analysis is demonstrated for a small network of three viaducts in an urban environment in Montréal, Canada. The incremental user costs due to interventions on the structures of the network are obtained through traffic simulations based on trip-survey data to evaluate travel delays for all possible combinations of concomitant partial or complete road closures. The results show that optimal infrastructure management strategies in space and time differ substantially depending on whether only agency costs are used, or if policies also reflect socio-economic goals (including sustainability). The main findings indicate that by including user costs in decision processes, significant reductions in user costs are achieved with minimal impact on operator costs, thus easily satisfying a cost/benefit criterion. The findings provide new evidence for policymakers that the optimal sequencing of interventions in time and space on a network can significantly reduce societal costs, sustainability objectives and environmental impacts.

Automated Pavement Condition Index Assessment with Deep Learning and Image Analysis: An End-to-End Approach.

The degradation of road pavements due to environmental factors is a pressing issue in infrastructure maintenance, necessitating precise identification of pavement distresses. The pavement condition index (PCI) serves as a critical metric for evaluating pavement conditions, essential for effective budget allocation and performance tracking. Traditional manual PCI assessment methods are limited by labor intensity, subjectivity, and susceptibility to human error. Addressing these challenges, this paper presents a novel, end-to-end automated method for PCI calculation, integrating deep learning and image processing technologies. The first stage employs a deep learning algorithm for accurate detection of pavement cracks, followed by the application of a segmentation-based skeleton algorithm in image processing to estimate crack width precisely. This integrated approach enhances the assessment process, providing a more comprehensive evaluation of pavement integrity. The validation results demonstrate a 95% accuracy in crack detection and 90% accuracy in crack width estimation. Leveraging these results, the automated PCI rating is achieved, aligned with standards, showcasing significant improvements in the efficiency and reliability of PCI evaluations. This method offers advancements in pavement maintenance strategies and potential applications in broader road infrastructure management.

CONCEPTUAL ASPECTS OF SUSTAINABLE ROAD INFRASTRUCTURE MANAGEMENT

The road infrastructure is closely correlated with the economic development of a country. Critical factors such as transportation facilitation, cost reduction, regional development stimulation, investment attraction, tourism promotion, increased economic productivity through reduced travel time, and improved logistics depend on the quality of road infrastructure management. In this study, the author theoretically grounded the concept of road infrastructure management from the perspective of sustainability and outlined a vision of road infrastructure management as an integrated system. The methods employed included monographic analysis, synthesis, deduction, comparative analysis, and other qualitative methods. The relevance of this study lies in the synthesis of the theoretical conceptual model of sustainable road infrastructure management. The author reviewed the main characteristics of sustainable road infrastructure management, highlighted the key criteria evaluating the quality of sustainable infrastructure, and provided a comprehensive overview of the subject.

Methods for Addressing Pavement Defects Based on Digital Twin Technology--A Case Study of Snow and Water Accumulation on Road Surface

This paper explores the utilization of digital twin technology for the management of urban road infrastructure challenges, specifically addressing issues related to snow and water accumulation. The study emphasizes the constraints associated with traditional road maintenance methods and proposes the implementation of a digital twin city framework to augment real-time monitoring and enhance emergency response efficiency. Through the integration of real-time data from sensors, traffic, and weather sources, the digital twin model replicates road conditions and forecasts potential issues, thereby enabling early warning systems. Furthermore, the research delves into the application of conductive asphalt concrete and superhydrophobic materials to mitigate road-related challenges. The results indicate that digital twin technology significantly enhances economic and operational efficiency in urban road infrastructure management when compared to conventional approaches.

On the role of digitalization in road asset management

A significant part of the country's wealth is made up of road assets - highways, bridges, overpasses, tunnels, bus stations, parking lots, drainage systems and other road infrastructure. The well-being of citizens, the profitability of organizations, and the economic power of the state depend on the effectiveness of management decisions when working with road assets. The article presents new approaches to road infrastructure management, in the context of which a conclusion is drawn about the role of digital technologies.

Semi-automatic crack width measurement using an OrthoBoundary algorithm

Evaluation of pavements' crack severity levels currently relies heavily on width measurement, which necessitates the development of a rapid, and high-accurate, automatic measurement approach for complex pavement cracks. This paper presents an OrthoBoundary algorithm that leverages the crack boundary and skeleton directions to determine crack propagation. Comparative analysis has been conducted between OrthoBoundary and Area-Length, Skeleton Shortest Distance (SSD), Edge Shortest Distance (ESD), and Orthogonal Projection (OP) methods. Results indicate that the OrthoBoundary algorithm achieves an average accuracy of 90.10%, outperforming the Area-Length (86.60%), SSD (76.01%), ESD (87.24%), and OP (88.07%) methods. Notably, the OrthoBoundary algorithm also exhibits processing speeds approximately 120 times faster than other considered methods while demonstrating improved robustness and user-friendliness. It has significant potential to quantify and assess the severity of pavement cracks, as well as to facilitate maintenance decision-making processes in road infrastructure management systems.

Identifying and Assessing the Functionality of IT Systems for the Maintenance and Development of the Road Network

The article presents the results of research aimed at identifying and assessing the functionality of IT systems in activities related to the maintenance and development of the road network managed by local government units. Starting the research, the authors were operating under the assumption that, nowadays, IT systems should play an important role in solving problems related to the management of road infrastructure. The applied research method took a form of a survey, and the research sample consisted of 100 local government units acting as administrators of the road network, i.e., administrators of municipal roads (AMR), county roads (ACR) and operating within cities with independent county rights (ACCR). The results of the research indicate that few administrators utilise integrated information systems in practice. Their users turned out to be mainly representatives of the ACCR group, who use several different support systems on a daily basis, with different functionality and usability. The operation of IT systems is mainly used at the level of operational work related to the implementation of ongoing maintenance activities, excluding the possibility of using them in strategic management.

Assessing road roughness using UAV-derived dense point clouds

The quality and safety of road networks are of paramount importance in modern transportation infrastructure. Road surface conditions, particularly road roughness, significantly impact vehicular travel safety, user comfort, vehicle operating costs, and overall road infrastructure maintenance. Traditional methods for road roughness analysis, such as manual inspections or image annotation, often present limitations in terms of data completeness, efficiency, and cost-effectiveness, especially for extensive road networks. This study investigates the potential of Unmanned Aerial Vehicles (UAVs) equipped with Structure-from-Motion (SfM) derived point clouds to transform road roughness assessment. By leveraging the capabilities of UAVs, including rapid data acquisition and high-resolution imagery, and employing SfM to generate detailed point clouds, this research aims to provide a comprehensive analysis of road surface conditions. The study, conducted on a road segment within the Harran University Osmanbey campus, systematically examines road roughness at different kernel sizes: 30 cm (smaller), 50 cm (moderate), and 75 cm (larger). Through this investigation, insights are gained into how different scales of analysis influence roughness measurements. The findings highlight the potential of UAV-derived point clouds as a promising avenue for road roughness analysis, offering transportation authorities and road administrators an efficient and cost-effective means of maintaining and enhancing road networks. The integration of this technology could lead to the development of safer, more efficient, and economically sustainable road transportation systems, benefiting both road users and infrastructure managers. As research and technological advancements in UAV-based road roughness assessment continue to progress, the potential for revolutionizing road management practices becomes increasingly apparent, ultimately leading to improved road quality and enhanced travel experiences for road users.

An Analytical Study Predicting Future Conditions and Application Strategies of Concrete Bridge Pavement Based on Pavement Management System Database

South Korea is implementing various policies to address the aging of infrastructures and improve road infrastructure management. Moreover, numerous research projects aiming at the development of necessary technologies for the proper implementation of these policies are underway. This study specifically aims to overcome existing problems in bridge pavement maintenance, such as the inaccuracy of future condition predictions and the selection of incorrect evaluation indicators. Our goal is to provide a new approach for the improved management of the bridge pavement management system (BPMS). To address the issues of accuracy in future condition prediction and evaluation indicator selection within the existing maintenance system, we utilized particle filtering, a Kalman filter method among machine learning techniques. This method allows for the prediction of future conditions, based on the nonlinearly collected bridge pavement conditions within BPMS. Furthermore, we proposed a systematic bridge pavement management strategy. This strategy utilizes traffic volume (ESALs; equivalent single axle loadings), a factor that can influence the future condition of bridge pavement, in correlation with the future condition predicted through particle filtering within BPMS.

Application of artificial intelligence in road network inventory and network-wide road safety assessment

This research paper examines the application of artificial intelligence (AI) in road network inventory and network-wide road safety assessment. An overview of current research related to this field is presented and various aspects of the use of AI in road infrastructure and safety assessment are analysed. In today’s world, where the road network continues to develop and grow, the use of AI can offer significant advantages in the inventory and assessment of road infrastructure and safety. AI can be used to process large volumes of data collected from various sources such as geographic data, photos, documentation and more. One of the main aspects that the article looks at is the use of AI to automatically process data and create a digital inventory of the road network. AI systems can analyse geographic data and photos to identify and classify roads, road signs, markings, and other elements of road infrastructure. This can facilitate engineers and competent authorities in the process of road inventory and maintenance. Another important aspect discussed in the paper is the use of AI for the general assessment of road safety. AI models can analyse road infrastructure inventory data, as well as traffic data, crashes, and other factors, to provide a comprehensive assessment of road network safety. This allows the competent authorities to identify vulnerable places, carry out preventive measures and plan appropriate interventions aimed at improving road safety. In conclusion, the paper highlights the possibilities and advantages of using artificial intelligence in road network inventory and network-wide road safety assessment. Such an approach can contribute to more efficient management of road infrastructure and improvement of road safety.

Machine Learning for Prediction of the International Roughness Index on Flexible Pavements: A Review, Challenges, and Future Directions

Timely maintenance of road pavements is crucial to ensure optimal performance. The accurate prediction of trends in pavement defects enables more efficient allocation of funds, leading to a safer, higher-quality road network. This article systematically reviews machine learning (ML) models for predicting the international roughness index (IRI), specifically focusing on flexible pavements, offering a comprehensive synthesis of the state-of-the-art. The study’s objective was to assess the effectiveness of various ML techniques in predicting IRI for flexible pavements. Among the evaluated ML models, tree ensembles and boosted trees are identified as the most effective, particularly in managing data related to traffic, pavement structure, and climatic conditions, which are essential for training these models. Our analysis reveals that traffic data are present in 89% of the studies, while pavement structure and climatic factors are featured in 78%. However, maintenance and rehabilitation history appears less frequently, included in 33% of the studies. This research underscores the need for high-quality, standardized datasets, and highlights the importance of model interpretability and computational efficiency. Addressing data consistency, model interpretability, and replicability across studies are crucial for leveraging ML’s full potential in fine-tuning IRI predictions. Future research directions include developing more interpretable, computationally efficient, and less complex models to maximize the impact of this research field in road infrastructure management.

Application of Management Information Systems in Road Infrastructure Management

In the field of spatial management, especially from the point of view of spatial economics, many decisions have long-term effects on the environment. Therefore, it is essential that the boundary conditions of the decision-making process are not only transparent, but also aligned with the sustainable development goals. At the same time, road infrastructure represents one of the most complex linear structures in the area, and its comprehensive management should respond to various types of problems, from structured to unstructured, which occur at all levels of decision-making. To solve such problems, management information systems (UIS) can help decision-makers to apply appropriate models and methods during the decision-making process. This paper provides an overview of the different types of UIS that can be used in road infrastructure management. The method of selecting an appropriate UIS and the method of interaction between data and models in such a management system are also discussed.

Productivity Assessment of the Yolo V5 Model in Detecting Road Surface Damages

Artificial intelligence models are currently being proposed for application in improving performance in addressing contemporary management and production issues. With the goal of automating the detection of road surface defects in transportation infrastructure management to make it more convenient, this research harnesses the advancements of the latest artificial intelligence models. Notably, new technology is used in this study to develop software that can automatically detect road surface damage, which shall lead to better results compared to previous models. This study evaluates and compares machine learning models using the same dataset for model training and performance assessment consisting of 9053 images from previous research. Furthermore, to demonstrate practicality and superior performance over previous image recognition models, mAP (mean average precision) and processing speed, which are recognized as a measure of effectiveness, are employed to assess the performance of the machine learning object recognition software models. The results of this research reveal the potential of the new technology, YOLO V5 (2023), as a high-performance model for object detection in technical transportation infrastructure images. Another significant outcome of the research is the development of an improved software named RTI-IMS, which can apply automation features and accurately detect road surface damages, thereby aiding more effective management and monitoring of sustainable road infrastructure.