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Digital twin (DT) technology has garnered increasing attention across various sectors, particularly in the construction and road infrastructure domains. To fully realize its potential and systematically apply it in practice, adherence to a formalized approach is necessary. However, numerous DT-related standards and models currently exist, creating uncertainty in the selection of appropriate frameworks. Moreover, no widely accepted standard or reference model has yet been developed in the field of road infrastructure management. Therefore, this study examined the current standards and models employed in the adoption and implementation of DTs in road infrastructure management, focusing on their dimensions (layers) and functional components. A bottom-up approach was adopted by comprehensively reviewing the existing literature on road networks, bridges, tunnels, and other civil infrastructures and urban DTs. Ultimately, a DT framework was developed, comprising five core layers with their respective components and functionalities, to facilitate network-level integrated road infrastructure management. Moreover, the proposed framework’s implementation scenario enhances its applicability in the field. Overall, this study provides valuable insights for researchers and practitioners involved in DT implementation in infrastructure management and supports future standardization efforts in this domain.

The transportation sector has been fundamental in Colombia and faces serious consequences in terms of traffic accidents. This paper examines the relationship between accidentability and most outstanding road infrastructure factors in ten urban stretches of Neiva in the period 2017-2018, following the inclusion and exclusion criteria of the International Road Assessment Program (IRAP) for developing countries in order to characterize the state of road infrastructure of the selected points. The results showed that the central sections of the city tend to be related to the day, time, type of vehicle and victim, attribute and direction of the road. The motorcyclist was the road actor most vulnerable to injuries and fatalities, Saturday and 8:00 am was where more crashes occurred. The road infrastructure factors contributing to accidents highlight the relevance of the environment in the city.

Roads form part of the essential physical infrastructure but face numerous external threats throughout their lifespan ranging from physical, meteorological and even operational threats. With climate change, roads are becoming increasingly vulnerable to adverse events, with an urgent need for building resilience in this type of critical infrastructure. This study focused on the adaptation of the Critical Infrastructure Elements Resilience Assessment (CIERA) method for measuring the resilience of road infrastructure with respect to climate-related events. A qualitative approach was adopted to identify the parameters to be measured under the three main components of the CIERA framework, namely robustness, recoverability and adaptability. Semi-structured interviews were carried out with a purposive sample of 15 experts in the transportation field working in both the public and private sector. This yielded the various indicators of road infrastructure resilience for inclusion in the CIERA framework.ContributionThe study identified 32 indicators to be assessed for road infrastructure resilience. The most cited ones for the robustness component include the implementation of protective security measures, adoption of new design standards and availability of alternative routes, whereas for the recoverability component, fund allocation, pre-approved response plans and agreements with third parties for help during disasters have been most highlighted by interviewees. Lastly, appropriate risk management practices, investment in technological innovation and provision of training are considered important aspects for the adaptability component. The framework can be applied in the road transportation sector to assess the level of resilience and guide decisions at strategic levels for investment.

Accurate cost estimation during the conceptual and feasibility phase of highway projects is essential for informed decision making by public contracting authorities. Existing approaches often rely on pavement cross-section descriptors, general project classifications, or quantity estimates of major work categories that are not reliably available at the early planning stage, while focusing on one or more key asset categories such as roadworks, bridges or tunnels. This study makes a novel contribution to both scientific literature and practice by proposing the first early-stage highway construction cost estimation model that explicitly incorporates roadworks, interchanges, tunnels and bridges, using only readily available or easily derived geometric characteristics. A comprehensive and practical approach was adopted by developing and comparing models across multiple machine learning (ML) methods, including Multilayer Perceptron-Artificial Neural Network (MLP-ANN), Radial Basis Function-Artificial Neural Network (RBF-ANN), Multiple Linear Regression (MLR), Random Forests (RF), Support Vector Regression (SVR), XGBoost Technique, and K-Nearest Neighbors (KNN). Results demonstrate that the MLR model based on six independent variables—mainline length, service road length, number of interchanges, total area of structures, tunnel length, and number of culverts—consistently outperformed more complex alternatives. The full MLR model, including its coefficients and standardized parameters, is provided, enabling direct replication and immediate use by contracting authorities, hence supporting more informed decisions on project funding and procurement.

The occurrence of a large number of road fatalities necessitates making improvements in road safety conditions. Governments and experts of European countries have been involved in this activity by investing efforts to increase security as well as achieve the goals of the European Commission (EC) to reduce fatalities by 50% by 2030.They aim to achieve road traffic without any fatalities occurring by 2050. Including a series of innovations for safety in the auto industry, deploying ITS technology, enforcing stringent legal regulations, emphasising on higher education of all traffic participants and undertaking other such important actions help achieve the set goals. Furthermore, adequate road maintenance with the use of modern models will definitely contribute to improving road safety. Moreover, it is necessary to regularly monitor road safety indicators and react accordingly on time. This paper presents a current state safety analysis from the perspective of road fatalities and road maintenance investments (RMIs) in the European Union (EU) and in European Free Trade Association (EFTA) countries. The main objectives of this paper are to analyse road safety aspects and emphasise on the relation existing between road fatalities and RMI. Concurrently, the objective of this paper is to verify the possible influence of the performance-based maintenance (PBM) model on improving road safety in the European Union. As part of the research, 27 EU member states and 3 EFTA members were analysed in the period 2010–2021. The results indicate a connection between road fatalities and RMI. Thus, this particular one is almost linear at the average EU level and that PBM models can directly contribute to improving traffic safety and indirectly by savings in maintenance costs.

The quality of road infrastructure significantly influences road safety, vehicle performance, and the overall driving experience. Traditional methods of assessing road quality, such as manual inspections, often lack the efficiency and accuracy needed to address modern transportation challenges. To overcome these limitations, this project focuses on developing an innovative model to assess road roughness using sensor data. The model leverages Android sensor technologies, primarily utilizing two types of sensors: accelerometers (inertial sensors) and GNSS (Global Navigation Satellite System) sensors. Given resource constraints, data was collected using an Android phone mounted on bicycles, which provided valuable insights despite some challenges and errors encountered during data collection. At the core of our model is the analysis of the International Roughness Index (IRI), which has been widely recognized as a reliable indicator for assessing road roughness on a quantitative scale. By deriving the parameters associated with IRI and applying the proposed formulae, we were able to recognize and categorize road surface irregularities such as potholes and humps. Our approach was further validated through the application of statistical methods, including the Kolmogorov-Smirnov (KS) test and Q-Q (Quantile-Quantile) plots. These methods demonstrated that the IRI is indeed a robust metric for indicating road roughness and low-cost sensors can be used for estimating road roughness. The metrics established in this study can serve as the foundation for developing more sophisticated algorithms that assess road roughness based on accelerometer data, ultimately contributing to enhanced transportation efficiency and road safety.

An intelligent transportation system is one of the fundamental goals of the smart city concept. The Internet of Things (IoT) concept is a basic instrument to digitalize and automatize the process in the intelligent transportation system. Digitalization via the IoT concept enables the automatic collection of data usable for management in the transportation system. The IoT concept includes a system of sensors, actuators, control units and computational distribution among the edge, fog and cloud layers. The study proposes a taxonomy of sensors used for monitoring tasks based on motion detection and object tracking in intelligent transportation system tasks. The sensor’s taxonomy helps to categorize the sensors based on working principles, installation or maintenance methods and other categories. The sensor’s categorization enables us to compare the effectiveness of each sensor’s system. Monitoring tasks are analyzed, categorized, and solved in intelligent transportation systems based on a literature review and focusing on motion detection and object tracking methods. A literature survey of sensor systems used for monitoring tasks in the intelligent transportation system was performed according to sensor and monitoring task categorization. In this review, we analyzed the achieved results to measure, sense, or classify events in intelligent transportation system monitoring tasks. The review conclusions were used to propose an architecture of the universal sensor system for common monitoring tasks based on motion detection and object tracking methods in intelligent transportation tasks. The proposed architecture was built and tested for the first experimental results in the case study scenario. Finally, we propose methods that could significantly improve the results in the following research.

Road transport is one of the main contributors to increasing greenhouse gas (GHG) emissions, consequently aggravating global warming, but it is also one of the sectors that most suffer from climate change, which causes extreme weather events. Thus, strategies, also called adaptation measures, have been discussed to minimize the impacts of climate change on transport systems and their infrastructure; however, a knowledge gap is evident in the literature. Therefore, this article develops a systematic review with a bibliometric approach, still scarce in the literature, in renowned databases, focusing on studies developed on adaptation measures for road infrastructure. The results show that, since the development of the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), an increasing amount of studies on the theme have been published in recognized journals such as Science of the Total Environment, Energy and Buildings and Urban Climate, analyzing climate threats such as intense precipitations and high temperatures that have led to biophysical impacts such as flooding and urban heat island. In addition, for each type of adverse weather condition, many impacts on road infrastructure can be listed, as well as ways to detect these impacts, and adaptation measures that can be used to minimize these problems.

This research introduces a cutting-edge approach to traffic infrastructure digitization, integrating UAV oblique photography with LiDAR point clouds for high-precision, lightweight 3D road modeling. The proposed method addresses the challenge of accurately capturing the current state of infrastructure while minimizing redundancy and optimizing computational efficiency. A key innovation is the development of the TJYRoad-Net model, which achieves over 85% mIoU segmentation accuracy by including a traffic feature computing (TFC) module composed of three critical components: the Regional Coordinate Encoder (RCE), the Context-Aware Aggregation Unit (CAU), and the Hierarchical Expansion Block. Comparative analysis segments the point clouds into road and non-road categories, achieving centimeter-level registration accuracy with RANSAC and ICP. Two lightweight surface reconstruction techniques are implemented: (1) algorithmic reconstruction, which delivers a 6.3 mm elevation error at 95% confidence in complex intersections, and (2) template matching, which replaces road markings, poles, and vegetation using bounding boxes. These methods ensure accurate results with minimal memory overhead. The optimized 3D models have been successfully applied in driving simulation and traffic flow analysis, providing a practical and scalable solution for real-world infrastructure modeling and analysis. These applications demonstrate the versatility and efficiency of the proposed methods in modern traffic system simulations.

To standardize definitions and guide the design, regulation, and policy related to automated transportation, the Society of Automotive Engineers (SAE) has established a taxonomy consisting of six levels of vehicle automation. The SAE taxonomy defines each level based on the capabilities of the automated system. It does not fully consider the infrastructure support required for each level. This can be considered a critical gap in the practice because the existing taxonomy does not account for the fact that the operational design domain (ODD) of any system must describe the specific conditions, including infrastructure, under which the system can function. In this paper, we argue that the ambient road infrastructure plays a critical role in characterizing the capabilities of autonomous vehicles (AVs) including mapping, perception, and motion planning, and therefore, the current taxonomy needs enhancement. To throw more light and stimulate discussion on this issue, this paper reviews, analyzes, and proposes a supplement to the existing SAE levels of automation from a road infrastructure perspective, considering the infrastructure support required for automated driving at each level of automation. Specifically, we focus on Level 4 because it is expected to be the most likely level of automation that will be deployed soon. Through an analysis of driving scenarios and state-of-the-art infrastructure technologies, we propose five sub-levels for Level 4 automated driving systems: Level 4-A (Dedicated Guideway Level), Level 4-B (Expressway Level), Level 4-C (Well-Structured Road Level), Level 4-D (Limited-Structured road Level), and Level 4-E (Disorganized Area Level). These sublevels reflect a progression from highly structured environments with robust infrastructure support to less structured environments with limited or no infrastructure support. The proposed supplement to the SAE taxonomy is expected to benefit both potential AV consumers and manufacturers through defining clear expectations of AV performance in different environments and infrastructure settings. In addition, transportation agencies may gain insights from this research towards their planning regarding future infrastructure improvements needed to support the emerging era of driving automation.