This study proposes a novel GPS-based methodology for Macroscopic Fundamental Diagram (MFD) estimation to overcome limitations of fixed detectors and inaccurate penetration rate assumptions. The approach dynamically identifies stop-line positions using spatiotemporal floating car data, calculates maximum queue lengths per signal cycle by combining floating car positions with estimated arriving vehicle lengths, and establishes a speed-based nonlinear model to determine queuing vehicle counts. A dynamic scaling coefficient derived from maximum queue lengths enables assumption-free estimation of total regional vehicles when applied to the floating car population. Validation using Chengdu data demonstrates significant improvements: unary cubic curves achieve optimal fitting for MFD relationships (R2 up to 0.9157); the HMM-CRF hybrid map-matching algorithm reduces average position error by 29% and intersection mismatch rate by approximately 40%; simulation results show queue length estimation accuracy of RMSE 22.8m and MAPE 18.5%, while MFD estimation error for maximum network flow drops from −17.5% to −3.5%, representing an 80% relative accuracy improvement. The proposed methodology provides robust technical support for urban road network assessment and management by enabling high-precision acquisition of MFDs from floating car data, effectively addressing critical challenges in macroscopic traffic modeling and monitoring. This advancement presents potential value for perimeter control applications and other MFD-based traffic management strategies.

Monitoring road pavement conditions is a fundamental activity in road network management. A well-structured pavement management system (PMS) is based on the continuous collection of data on pavement conditions throughout the road’s life cycle. In recent years, the integration of sensor technologies into road pavement for condition monitoring has attracted increasing attention. The collection of such data allows the construction of models that describe pavement deterioration as a function of traffic loads. This study presents an innovative solution (NDSPHM) for monitoring the structural condition of road pavements, which involves using acoustic sensors (microphones) to acquire the signature generated by passing vehicles, which propagates through the pavement structure. In more detail, this work focuses on the processing methodology applied to data collected on a highway under real traffic conditions.

The efficient and cost-effective management of road networks is reliant on accurate, reliable and up-to-date bridge asset information. The scale of road networks and the associated number of bridge assets make the collection, verification and updating of this information a significant burden on time and cost. This study explores how multiple existing public data sources can be used, at a network scale, to assess and enhance existing asset records using a novel fuzzy logic algorithm, allowing a single set of bridge records to be created with reliability measures. Existing bridge asset and road network datasets were combined with a processed aerial LiDAR dataset covering a 528 km 2 area of South-East Queensland, with sub-areas extracted to verify the methodology on a large scale area. Processing, aggregation, and verification of the three sources of bridge records was completed in approximately 64 s per bridge. Common bridge records between datasets were merged and reported high reliabilities (75%–98%), while unique records reported low reliabilities ( < 30%). A reliability informed data assessment method was then demonstrated, which allowed the merged dataset to be rapidly interrogated and enhanced. Three major data enhancement outcomes were achieved, including: incorporation of non-erroneous LiDAR data sources into existing bridge asset datasets, verification and augmentation of data within existing bridge asset datasets; and quantitative measures of aggregate data reliability for asset managers and owners. The proposed methodology is concluded to be scalable and effective for a range of bridge types, environments, and data end uses. It also forms an efficient and reliable method for developing large scale, labelled datasets for bridge assets. As such, research findings are also likely to support emerging classes of machine learning algorithms for bridge identification, measurement, and inference of semantic information. • Aggregation of geospatial data and traditional bridge asset records. • Fuzzy logic algorithm to incorporate uncertainty inherent within variable datasets. • Automated verification of aggregated records via record specific reliability score. • Small network-scale case study to verify scalability and effectiveness.

Urban areas worldwide are grappling with the pervasive challenge of traffic congestion, which impedes economic productivity, degrades environmental quality, and diminishes the quality of life for residents. The design and management of road networks are fundamental determinants of urban mobility. This systematic review presents a comprehensive analysis and proposes a new conceptual framework for optimizing road networks to enhance traffic flow. The critical interdependencies between network topology, smart traffic management systems, and multimodal integration are examined. Through a structured review of 82 selected studies, this paper synthesizes the evidence on various interventions, from hierarchical network forms to adaptive traffic control and Bus Rapid Transit (BRT). Case studies from Singapore, Curitiba, and Amsterdam illustrate that success is contingent not only on technical solutions but also on specific governance models. The review highlights the powerful synergy between physical infrastructure and intelligent control systems, but also identifies an emerging conflict between centralized planning and decentralized, user-led optimization through modern navigation applications. We conclude that moving beyond isolated fixes requires an integrated decision-making framework that acknowledges these complex interactions. This paper provides a comprehensive resource for researchers, transportation engineers, and urban planners seeking to develop more efficient, sustainable, and resilient urban transportation systems.

Abstract Following several collapses, bridge management has become crucial in recent decades. In Italy, the publication of the Guidelines for Bridge Management has prompted administrations and researchers to implement new solutions: in fact, Bridge Management System (BMS) platforms, integrated with Geographic Information System (GIS) and Building Information Modeling (BIM), have been developed through commercial solutions or research projects. However, these platforms can present challenges for less experienced users: commercial platforms are often predefined and hard to customize, while research‐based solutions tend to be complex and difficult to implement in practice. Consequently, the authors stress the need for easy‐to‐use tools for beginners, providing essential starting tools. This article proposes a simple methodology for integrating the Guidelines within Bridge Information Modeling (BrIM), offering operational tools to understand and apply BIM processes in infrastructure management. It represents a first step in a larger project to develop an open platform for road and rail network management. A BrIM template with required components, digital Guidelines sheet, and IFC export settings is provided, tested through a case study to verify the procedure's correctness and visualization with IFC viewers.

This study presents an innovative approach for automated pavement degradation monitoring in Cameroon, specifically focusing on the city of Yaoundé. We integrate drone photogrammetry for high-resolution images with artificial intelligence, leveraging a YOLOv11 model for precise detection and segmentation of various road surface degradations. A comprehensive Python workflow was developed to automate the entire processing chain, from orthomosaic tiling and AI model inference to georeferencing predictions and generating GIS-ready layers. The methodology was applied to a 2.5 km road section (Elig-Effa–Melen– Carrefour EMIA) in Yaoundé. The trained model achieved a F1-score of 77% with a precision of 86% and recall of 70% for the detection and F1-score of 81% with a precision of 86% and recall of 78% for the segmentation. The automated workflow processed the road section in 5 minutes 47 seconds, detecting 438 instances of degradation with a total affected surface area of 662.96 m², demonstrating significant time savings compared to days or weeks required for manual inspection. The generated degradation maps and an interactive dashboard provide objective, actionable insights for road managers, enabling proactive maintenance planning and optimized resource allocation. While acknowledging limitations such as the need for further validation with field surveys and the current focus on 2D surface data, this research highlights the scalability and cost-effectiveness of the proposed solution. It offers a promising pathway toward intelligent and efficient road network management in developing countries, by providing timely and accurate data to support data-driven maintenance strategies. Keywords: Photogrammetry, Drone, Artificial Intelligence, Paved Roads, Degradations, YOLOv11. Received Date: August 21, 2025 Accepted Date: September 13, 2025 Published Date: October 01, 2025 Available Online at https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/458

The high bandwidth, low latency, and extensive coverage of Sixth Generation (6G) communication technology provide robust data support and communication guarantees for intelligent road network management. This study aims to explore the application of 6G communication technology and deep reinforcement learning (DRL) algorithms in smart road network management, with the goal of enhancing the intelligence of traffic management systems. DRL algorithms are capable of handling complex traffic environments and, through self-learning and optimization, can achieve intelligent decision-making and route planning. This study proposes a DRL-based traffic signal control method leveraging 6G communication technology. The core of this method lies in its capability to manage complex and dynamically changing traffic flows, adjusting traffic signal plans based on real-time data to adapt to various traffic conditions. By learning traffic distribution patterns, the model generates appropriate traffic signals for each intersection, thereby optimizing the traffic signal plans. Simulation experiments found that, compared to the Convolutional Neural Network (CNN) algorithm, DRL not only reduced the average travel time by 28.2% but also increased the average travel speed by 26.3%, and significantly reduced the average queue length by 42.9%. These results demonstrate that the proposed DRL algorithm shows significant advantages in alleviating traffic congestion and optimizing traffic signal control. This study offers a novel solution for intelligent road network management and validates the potential of 6G communication technology and DRL algorithms in this field.

Snowfall and road surface freezing cause traffic disruptions and skidding accidents. When widespread extreme cold events or sudden heavy snowfalls occur, the continuous monitoring and management of extensive road networks until the restoration of traffic operations is constrained by the limited personnel and resources available to road authorities. Consequently, road surface condition prediction models have become increasingly necessary to enable timely and sustainable decision-making. This study proposes a road surface condition prediction model based on CCTV images collected from roadside cameras. Three databases were constructed based on different definitions of moisture-related surface classes, and models with the same architecture were trained and evaluated. The results showed that the best performance was achieved when ice and snow were combined into a single class rather than treated separately. The proposed model was designed with a simplified structure to ensure applicability in practical operations requiring computational efficiency. Compared with transfer learning using deeper and more complex pre-trained models, the proposed model achieved comparable prediction accuracy while requiring less training time and computational resources. These findings demonstrate the reliability and practical utility of the developed model, indicating that its application can support sustainable snow removal decision-making across extensive road networks.

Road networks are essential for access, resource distribution, and population evacuation during natural events. These challenges are pronounced in rural areas, where network redundancy is limited and communities may have social disparities. While traditional risk management systems often focus on the physical consequences of hazard events alone, specialized literature increasingly suggests the development of a more comprehensive approach for risk assessment, where not only physical aspects associated with infrastructure, such as damage level or disruptions, but also the social and economic attributes of the affected population are considered. Consequently, this paper proposes a Vulnerability Access Index (VAI) to support road network decision-making that integrates the social vulnerability of rural communities exposed to natural events, their accessibility to nearby critical infrastructure, and physical risk. The research methodology considers (i) the Social Vulnerability Index (SVI) calculation based on socioeconomic variables, (ii) Importance Index estimation (Iimp) to evaluate access to critical infrastructure, (iii) VAI calculation combining SVI and Iimp, and (iv) application to a case study in the influence area of the Villarrica volcano in southern Chile. The results show that when incorporating social variables and accessibility, infrastructure criticality varies significantly compared to the infrastructure criticality assessment based solely on physical risk, modifying the decision-making regarding road infrastructure robustness and resilience improvements.

Road maintenance, as an effort to maintain its performance, plays a crucial role in a road network management system. However, insufficient resources and differences in how stakholders perceive this issue pose challenges in the process of programming road maintenance. This study aims to develop a road maintenance prioritization model based on the Analytical Hierarchy Process (AHP) for the road network under the authority of the Jambi City Government. The AHP-based weighting is conducted based on a hierarchical decision-making model for road maintenance, which consists of three main criteria: technical road conditions, socio-economic factors, and connectivity, along with their respective sub-criteria. The results of the study show that in the main criterion, road technical conditions weighted the highest of all with a value of 0,65. Followed by connectivity at 0,19 and socio-economic at 0,16. Then regarding the sub-criterion, the highest global weight is road damage level, with a value of 0,28. The model developed in this study is expected to be used as a decision-making support tools to program road maintenance. The multi-criteria assesment which was conducted is expected to evaluate road priority level holistically and provide a new perspective in decision-making.

ABSTRACT Accurate and reliable bridge asset information is crucial for the efficient management of road networks, however the scale of road networks makes collection and maintenance of this data a significant burden. While current studies have demonstrated the feasibility of the detection of bridge assets from aerial LiDAR, this research addresses the added complexities of network-scale application of such techniques, considering variability in structure types, environments, and data quality. A two-pronged LiDAR processing method was developed, employing geometric segmentation and point LAS classification. The methods were tested on public LiDAR datasets covering 6 km2 and showed average identification accuracies of 64% and 93%, with detection efficacy strongly influenced by on and under bridge use combinations. This paper also presents the first benchmarking of bridge measurement accuracy from aerial LiDAR, with median accuracies for bridge length and width of 90% and 66% as compared to ground truth data.

Origin-destination (OD) demand matrix plays an essential role in performance assessment and traffic management of road networks. While existing real-time models for time-varying OD estimation offer promising solutions, their applicability could be constrained by prior OD database development, insufficient system observability modelling, or suboptimal solving procedures. This study presents a systematic modelling framework to tackle these key challenges. Under this framework, a robust structural decomposition scheme is proposed, building upon which the modelling of dynamic traffic assignment and dynamic OD estimation is investigated. To ensure good convergence of the state space models, the system observability problem within the dynamic OD estimation context is properly addressed. Finally, two kinds of state space models with a solving procedure enriched by explicit error statistical analysis and adaptive error correction are developed. A real-world urban network in the downtown area of Kunshan, China was utilised to demonstrate the potential of the proposed framework.

Urban traffic congestion remains a critical challenge for transportation infrastructure, with significant impacts on economic productivity, environmental sustainability, and commuter well-being. This meta-analysis investigates the role of Artificial Intelligence (AI)-enabled Adaptive Traffic Control Systems (ATCS) in mitigating urban congestion and enhancing mobility performance, integrating findings from 68 empirical studies and government performance datasets spanning 2010–2024. The analysis draws heavily on annual congestion statistics reported by the Federal Highway Administration (FHWA), particularly from 2022 and 2023. Empirical data reveal persistent trends in urban congestion. In 2022, U.S. urban areas experienced an average of 2 hours and 55 minutes of daily congestion, improving by 10 minutes from 2021. The Travel Time Index (TTI) rose from 1.19 to 1.22, while the Planning Time Index (PTI)—indicating travel reliability—jumped from 1.72 to 1.80. In 2023, although congested hours further decreased to 2 hours and 45 minutes, average congestion (TTI) worsened to 1.24, and PTI increased again to 1.88, reflecting growing travel time unpredictability. AI-enabled ATCS implementations, particularly those using Reinforcement Learning (RL), demonstrated measurable reductions in congestion across pilot deployments. Synthesized results show that AI-driven systems reduce average vehicle delay by 24% to 36%, intersection queuing by 28%, and overall travel time by up to 19% compared to pre-implementation baselines. Multi-agent Deep RL strategies exhibited superior scalability and adaptation under dynamic flow conditions, while hybrid models (e.g., fuzzy logic + neural nets) enhanced performance during atypical events such as construction detours and emergency reroutes. Importantly, this meta-analysis identifies that regions with AI-supported traffic signal optimization—especially those leveraging real-time data from the NPMRDS (National Performance Management Research Data Set)—achieved notably higher improvements in throughput and lower TTI variability. Case studies, such as Tennessee DOT’s use of crowdsourced and sensor data during the I-40 bridge closure, demonstrate the operational value of intelligent systems in supporting incident management and routing optimization. These findings underscore the strategic importance of deploying AI-based adaptive systems within the broader framework of Intelligent Transportation Systems (ITS) and Smart City planning. The paper concludes with implementation recommendations focused on infrastructure readiness, data integration standards, and policy harmonization for sustainable urban mobility.

Purpose: Our article addresses road‑cost management at the regional level—an area less studied than local roads or highways. The study aims to identify critical, long‑term urban factors that lead to higher regional road‑management costs and to propose a financially sustainable strategy for road‑network reconstruction using various reclaimed asphalt pavement (RAP) materials.Methodology: Using stepwise and enter regression analyses with data on road quality and maintenance costs in Czechia, the study considers factors such as elevation, slope, and changes in population and population density.Findings: The results highlight that slope and road class—both of which are linked to the disconnectedness of the road network—increase road‑maintenance costs. Thus, network renewals implemented in compact sets of roads can significantly reduce costs. By contrast, population and population density have only a minimal impact on long‑term costs.Practical Implications: We define scenarios to reduce costs through RAP materials and determine potential savings, using regional roads in Czechia as an example. The scenarios indicate potential savings of nearly €27 million per region when RAP is employed. In practice, using RAP materials can enable infrastructure managers to renew more than one‑third of roads each year compared with conventional mixes, or to increase the frequency of restoring lower‑quality road sections from every three years to every 2.25 years.Value: The article offers new insights into the factors that determine regional road‑level costs. It demonstrates that using RAP materials in regional road management can positively affect the frequency of road revitalisation.

Developing effective road network management is crucial for the socioeconomic development of developing countries, particularly the Lao People’s Democratic Republic (Lao PDR or Laos). The current road maintenance system in Lao PDR uses a traditional reactive maintenance approach, addressing road deterioration only after the condition reaches a critical state. This study proposes a stochastic Markov Decision Process (MDP) framework to enhance traditional road management practices. The proposed MDP framework shifts from a conventional reactive to a proactive strategy by considering probabilistic pavement performance and optimally allocating funding to rapidly deteriorating sections. This study enables decision-makers to determine the optimal intervention strategies based on different scenarios. The comparison of the ASIAN Road network, high technical design and construction, and the National Road network, standard technical design and construction, in different scenarios provides a workable framework for maintaining Laos, and other developing countries, road condition despite limited resources and sustainable development concerns. This comprehensive framework includes estimating deterioration rates, defining policies, conducting life-cycle cost (LCC) analysis, and determining optimal strategies that minimize LCC subject to financial and performance constraints. This study highlights significant improvements in decision-making, particularly in resource allocation, by creating innovative and preventive approaches that enhance the efficiency of road management systems and ensure sustainable maintenance practices in Lao PDR. Doi: 10.28991/CEJ-2025-011-05-023 Full Text: PDF

The most important intent of this research will be conducting a life-cycle cost analysis of different maintenance activities over DBST road segments undertaken by Ethiopian Roads Authority (ERA) Sodo road network and safety management branch directorate, considering the Maintenance District as a case study. In order to meet the objectives of the study, a case study has considered both quantitative and qualitative data. Road condition survey data was collected by conducting road condition survey with the help of Sodo RNSMBD staffs. After collecting the necessary data, all possible input data was arranged in order to feed HDM-4. There was analysis by using Highway Development and Management Model (HDM-4) tool and the life-cycle cost analysis (LCCA) were carried out in order to determine the economic viability of different road maintenance intervention alternatives. The analysis was carried out by considering a do nothing, do routine works and do periodic works scenarios. The economic indicator used for this study was Net present value (NPV). Generally, the results of this study indicate that most of the road condition of the selected DBST road segments falls under poor condition. The economic analysis results also depict that implementing preventive maintenance strategy on DBST road segments can significantly decrease the life cycle cost in terms of costs incurred by both the road agency and user under Sodo RNSMBD. Therefore, road agencies should embrace the practices of applying more preventive activities at early signs of pavement deterioration in order to preserve of road assets. Keywords: DBST, HDM-4, Life Cycle Cost Analysis, Road maintenance, ERA

Roads are a form of critical infrastructure, influencing economic growth, mobility, and public safety. However, the management, monitoring, and maintenance of road networks remain a challenge, particularly given limited budgets and the complexity of assessing widespread infrastructure. This Special Issue on “Road Detection, Monitoring, and Maintenance Using Remotely Sensed Data” presents innovative strategies leveraging remote sensing technologies, artificial intelligence (AI), and non-destructive testing (NDT) to optimize road infrastructure assessment. The ten papers published in this issue explore diverse methodologies, including novel deep learning algorithms for road inventory, novel methods for pavement crack detection, AI-enhanced ground-penetrating radar (GPR) imaging for subsurface assessment, high-resolution optical satellite imagery for unpaved road assessment, and aerial orthophotography for road mapping. Collectively, these studies demonstrate the transformative potential of remotely sensed data for improving the efficiency, accuracy, and scalability of road monitoring and maintenance processes. The findings highlight the importance of integrating multi-source remote sensing data with advanced AI-based techniques to develop cost-effective, automated, and scalable solutions for road authorities. As the first edition of this Special Issue, these contributions lay the groundwork for future advancements in remote sensing applications for road network management.

Road network management is an aspect of infrastructure heritage preservation that requires special attention from the departments concerned, in order to accurately predict maintenance, reinforcement and planning interventions, which requires effective methods for visualizing and tracking the network. This research aims to integrate techniques of the Geographic Information System (GIS) for modeling, classifying and analyzing the evolution of two parameters: Traffic and pavement deterioration. In this context, a case study examining GIS spatial representation options visualized these two parameters on national, regional and provincial roads in the Berkane province in Morocco based on the results of traffic investigations and visual surveys of pavement deterioration between 2016 and 2022. This study will provide road network managers and decision-makers with the information they need to make the right decisions regarding future maintenance programs.

Significance The motorisation of African countries has overtaken the development and sustainability of infrastructure, regulation and enforcement needed to support road transportation. In West Africa, governments are taking steps to reduce road accidents by implementing solutions from across a range of national, regional and international frameworks. Impacts Regional governments may increase the number of road safety officials deployed to major roads and highways. The attention paid to road network management may lead to increased efforts at infrastructure development. Government agencies will likely under-report road traffic-related deaths due to bureaucratic information siloing and a lack of resources.

Should I stay or move? Quantifying landscape of fear to enhance environmental management of road networks in a highly transformed landscape