Transport Infrastructure Research Articles

Views of emerging sustainability leaders on the future of Transport: A Q study in a Taiwan tertiary education program

As we approach critical milestones such as the year 2050, by which time the global community aims to achieve zero carbon emissions, the decisions we make today concerning transportation infrastructure become increasingly influential. This research seeks to broaden the dialogue beyond the traditional domain of current policymakers and transport planners by incorporating the perspectives of the generation that will inherit and live with the long-term impacts of today’s decisions. This study investigates the perspectives of learners in a unique sustainability-focused undergraduate program in Taiwan regarding transportation’s future in a world impacted by anthropogenic climate change. Considering their potential as future sustainability professionals and leaders, understanding their views can offer insights for both educational and transportation policy. Using Q methodology, this research captures a range of viewpoints. The five distinct perspectives include the advocates of collective responsibility and tech-optimists, who hold positive views towards collective action and technological advancements respectively. The pragmatic convenience seekers and solitaries who prioritize personal comfort and express skepticism about environmental targets. The public transport advocates, meanwhile, favor shared forms of transport and see a crucial role for the government in carbon reduction efforts. The study’s significance lies in its emphasis on a previously underexplored demographic within the unique context of Taiwan, revealing their perspectives on a transportation future shaped by climate change by using Q methodology, and its implications for policy derived from the findings.

Spatial variation of bus stop crime response to changes in the surrounding environment and transit level of service

Increasing concerns over the significant number of crimes occurring at and around bus stops have motivated the analysis of intervening factors to propose solutions to this problem. However, models found so far overlook important features of crime data: spatial dependence and spatial heterogeneity. In addition, crime predictor data has shown to be multicollinear in previous studies. To tackle these issues, this paper analyzes mobile phone thefts and robberies at 19,329 bus stops in São Paulo (Brazil) based on Geographically Weighted Regression (GWR), using components retained from a Principal Component Analysis (PCA) as explanatory variables. A comparison is carried out between GWR and a non-spatial Transformed Linear Regression (TLR), and a Negative Binomial Regression (NBR) with uncorrelated predictors. Effects on crime from 9 PCs, representing central areas, bus transit level of service, transport infrastructure, land use and sociodemographic features, were proven to have high spatial variability. Changes in the surrounding environment can cause higher or lower increases in mobile phone thefts and robberies at stops according to their spatial location. Results showed that GWR performs better than NBR and TLR in predicting bus stop crime, thus compensating for a loss of information associated with PCA. In addition, GWR was able to completely incorporate the spatial dependence found in the non-spatial model, which covered the nearest 96 neighboring stops. The proposed method can effectively detect critical bus stops and regions, contributing to crime prevention. GWR coupled with PCA can also help identify the best locations to install new bus stops.

Pothole Detection Model for Road Safety using Computer Vision and Machine Learning

<div align="center"><table width="590" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>Potholes pose significant threats to vehicular movement, causing damage to vehicles and risking the safety of drivers and pedestrians. The escalating issue of potholes has led to substantial financial losses for vehicle owners and drivers. Traditional methods of pothole detection are impractical, necessitating an innovative approach. The study focuses on implementing a detection system capable of accurately identifying potholes, empowering vehicles to adapt their speed or halt to prevent damage. The transformative solution presented in this research leverages cutting-edge technologies, specifically computer vision and machine learning, aiming to enhance road safety and streamline maintenance efforts. By addressing the interdependence of modern civilization on road networks, the Pothole Detection Model promises improved road safety, efficient maintenance practices, and the emergence of an era in intelligent transportation systems. The integration of technology into transportation infrastructure highlights the proactive measures needed to combat road imperfections, ensuring a safer and more efficient road network for the benefit of society.</p></td></tr></tbody></table></div>

Engineering indigenous dispossession and plantation slavery in the Southeast Gulf coast

In this paper we examine the activities of US Army topographers and engineers in the Apalachicola-Chattahoochee-Flint (ACF) watershed during the violent transformation of the region from the heartlands of the Creek confederacy to US territorial control. A vital waterway for the Creek in the late eighteenth century, the rivers would become an important transportation network in the US plantation economy by the early nineteenth century. We emphasize that the army made its initial infrastructural improvements in the region to provide security for the plantation system. Army engineering in the ACF watershed began in the struggle for the Gulf borderlands, as white American settlers, British forces, Indigenous peoples, and Black maroons fought for control over the contested terrain. US engineers and topographers produced territorial knowledge and physical infrastructure to facilitate the occupation of Indigenous territory and elimination of potential spaces of Black freedom. Topographic knowledge would later serve as the foundation for restructuring the land as property, naturalizing its possession by white plantation owners. Similarly, roads and waterway improvements, created to facilitate troop movements, would later serve as vital transportation infrastructure for settlement and expanding plantation slavery. This paper demonstrates how military engineering techniques designed to secure the Nation in the context of race war subsequently provided the coordinates for reorganizing the land within an emergent plantation economy inside its territorial borders.

A Data-Driven Seismic Fragility Model for Post-Earthquake Repairable Performance of Highway Curved Bridge Portfolios

The rapid assessment of seismic fragility for highway curved bridges is crucial for enhancing the seismic resilience of transportation infrastructure. However, commonly used performance limit states fail to fully capture post-earthquake rehabilitation requirements. To address this, this paper proposes a data-driven, system-level seismic fragility assessment method for post-earthquake repairable performance of highway curved bridge portfolios. A sample database of curved bridge portfolios is generated using uniform design (UD) and a parametric finite element program, which then drives machine learning (ML) algorithms to develop a seismic fragility prediction model for post-earthquake repairable performance. The model is used to analyze the influence of various structural attributes on the median values of fragility curves (mR) for curved bridges. Additionally, a simplified fragility estimation method is developed based on the analysis results, and its reliability is validated through typical case studies. The results show that the central discrepancy of the UD sampling method is 0.0649, providing reliable and comprehensive data support for the ML prediction model. The fragility prediction model based on an artificial neural network (ANN) exhibits favorable fidelity and robustness. Curved bridges with central angles (α) in the range of 00.5rad show significant variations in mR, with a coupling effect observed between α and column height (H). The median absolute percentage error (MAPE) of the simplified fragility parameter calculation formula is below 11%, offering a preliminary reference for assessing the post-earthquake repairable performance of curved bridges.

Unveiling the spatially varied nonlinear effects of urban built environment on housing prices using an interpretable ensemble learning model

The relationship between urban built environment (UBE) and housing prices manifests as complex, exhibiting significant nonlinearities and spatial heterogeneity that remain inadequately understood. Taking Shanghai as a testbed, this study employs a novel ensemble learning approach, augmented by Bayesian optimization and Monte Carlo simulation, to decipher the intricate and nonlinear impacts of UBE factors on housing markets across diverse urban geographies. Our analysis unveils substantial spatial variations in how transit accessibility, amenities, residential density, and green/blue spaces influence real estate values. Proximity to metro stations and bike-sharing facilities exerts a more pronounced positive effect than bus stops. Moreover, residents in central areas demonstrate a higher willingness-to-pay for public service amenities, while those in outer suburbs prioritize access to public transportation infrastructure. Intriguingly, it invokes an optimal threshold range of urban density for properties in new cities, thereby increasing the vitality and dense socio-economic networks. Furthermore, the sprawling suburbs have identified an adverse economic impact of large conservation green/blue spaces. These insights can guide policymakers in crafting spatially-tailored strategies that harness localized built environment drivers to catalyse equitable and prosperous urban development. Tailored policies informed by this spatially explicit understanding of nonlinear built environment-housing interactions can foster more sustainable, liveable, and inclusive cities.

Addressing Infrastructure Design Challenges In Constructing At-Grade Rail Crossings Near Road Intersections

At-grade rail crossings, where railway tracks intersect roadways at the same level rather than being separated by bridges or tunnels, are crucial elements of transportation infrastructure. However, these crossings create significant challenges for rail operations, particularly regarding safety and efficiency. Locating an at-grade rail crossing near a road intersection is generally discouraged due to the increased risk of train delays, reduced visibility for train operators, and the potential for train-road conflicts. While these issues are undesirable, they are sometimes unavoidable when rail infrastructure must be near or across road intersections. This study examines the design challenges of constructing at-grade rail crossings near road intersections. It provides a comprehensive review of the safety and operational concerns, including delays, conflicts, and the potential for accidents. The study also discusses existing guidelines and design practices to mitigate these challenges, offering insights into improving the efficiency and safety of at-grade rail crossings near road intersections.

Geographical health inequalities in India: the impact of the COVID-19 pandemic on healthcare access and healthcare inequality.

With an emphasis on spatial health disparities, this study examines how COVID-19 has affected healthcare access and inequality in India. The study developed the Healthcare Access Index (HAI) and Healthcare Inequality Index (HII) to assess the pandemic's effects on healthcare. The study addresses spatial health disparities in healthcare access and inequality, filling gaps in the literature. The final aim of the study is to offer policy suggestions to lessen healthcare inequities in India, particularly in the context of COVID-19. The study incorporates secondary data from publicly accessible databases such as the National Family Health Survey, Niti-Ayog and Indian Census databases and employs a quantitative research design. The impact of the COVID-19 pandemic on healthcare access and healthcare inequality in India is examined using the HAI and the HII. The five dimensions of healthcare access - availability, accessibility, accommodation, cost and acceptability - were used in developing the HAI. The study uses a panel data analysis methodology to examine the HAI and HII scores for 19 states over the pre-COVID-19 (2015) and post-COVID-19 (2020) periods. In order to investigate the connection between healthcare access, healthcare inequality and the COVID-19 pandemic, the analysis employs statistical tests such as descriptive statistics, correlation analysis, factor analysis and visualization analysis. According to the study, COVID-19 impacted healthcare access and inequality in India, with notable regional inequalities between states. The pandemic has increased healthcare disparities by widening the gap between states with high and low HII ratings. Healthcare access is closely tied to healthcare inequality, with lower levels of access being associated with more significant levels of inequality. The report advises governmental initiatives to lessen healthcare disparities in India, such as raising healthcare spending, strengthening healthcare services in underperforming states and enhancing healthcare infrastructure. For Indian healthcare authorities and practitioners, the study has significant ramifications. In light of the COVID-19 pandemic, there has been a main focus on addressing geographic gaps in healthcare access and inequality. The report suggests upgrading transportation infrastructure, lowering out-of-pocket costs, increasing health insurance coverage and enhancing healthcare infrastructure and services in underperforming states. The HAI and the HII are tools that policymakers can use to identify states needing immediate attention and appropriately spend resources. These doable recommendations provide a framework for lowering healthcare disparities in India and enhancing healthcare outcomes for all communities. The study's originality resides in establishing the HAI and HII indices, using panel data analysis and assessing healthcare inequality regarding geographic disparities. Policy choices targeted at lowering healthcare disparities and enhancing healthcare outcomes for all people in India can be informed by the study's practical consequences.

Do transportation infrastructure contracts enhance spending efficiency? Examining state highway construction programs

This study examines the impact of state highway construction contracts on state spending efficiency controlling for production structure, service demands, and situational factors. The theoretical argument is that because highway construction projects are relatively large in scale, complex, and can be monitored through objective performance measurement, state highway construction programs may save government production costs through contracts. Contracting helps highway producers achieve efficiency by optimizing production size based on workload and task complexity. The unit of analysis is 48 state governments’ highway construction contracts from 1998 to 2008. Through a two-stage analysis method including a Total Function Productivity (TFP) index and system dynamic panel data analysis, the results suggest that highway construction contracts enhance state highway spending efficiency, especially for large-scale construction projects.

Assessment of Tourism Development in Belarus: A Comprehensive Analysis of Infrastructure, Cultural Heritage and Natural Resources

Purpose: This study examines the development of tourism in Belarus, evaluating its infrastructure, cultural heritage potential, and natural resources. The research aims to identify key factors influencing tourism development and assess their relative impact on the sector's growth. Design/methodology/approach: The study employs a mixed-methods approach, primarily utilizing a quantitative survey (n=512) of local stakeholders across six regions of Belarus, supplemented by comprehensive statistical analysis. The research instrument included 17 structured questions measuring perceptions of tourism development across multiple dimensions. Data analysis was conducted using chi-square tests and Cramér's V coefficient to determine statistical significance and relationship strength. Findings: The results indicate moderate tourism development in Belarus (39% of respondents rated it as average, 42% as high). Infrastructure quality shows significant regional variation (χ2(4)=31.50, p<0.001, V=0.56). Cultural heritage emerged as a primary attraction (72% agreement), while natural resources received varied evaluations (57% highlighting landscape value). The study identified key development barriers: visa restrictions (74% of respondents), insufficient marketing (62%), and underdeveloped transport infrastructure (51%). Research limitations/implications: The study's cross-sectional nature and focus on domestic stakeholders may limit its generalizability. Future longitudinal studies incorporating international perspectives could provide additional insights. Conclusion: The study reveals that Belarus's tourism development exhibits a complex pattern of strengths and challenges. While the country possesses significant potential through its cultural heritage (72% positive evaluation) and natural resources (57% positive assessment), its tourism development is constrained by structural barriers. The research demonstrates that future tourism growth requires a three-pronged approach: (1) systematic infrastructure modernization, particularly in regional areas; (2) reform of visa policies to enhance accessibility; and (3) development of comprehensive marketing strategies. The statistical significance of these findings (p<0.001) supports the need for evidence-based policy interventions. This research contributes to both theoretical understanding of tourism development in transition economies and practical policy formulation, while establishing a methodological framework for future studies in similar contexts.

Enhancing the energy and environmental efficiency of the city's infrastructure based on optimizing its fuel and energy balance

The article considers the direction of increasing the energy and environmental efficiency of the city's energy infrastructure through the modernization of existing thermal power plants (TPP) and combined heat and power (CHP) plants using combined-cycle gas turbine (CCGT) plants. Fuel and energy balances of electric power plants have been constructed, and the energy and environmental effects of the use of CCGT have been assessed. The modeling of energy and environmental indicators for the thermal circuits of the CCGT was conducted using the Thermoflex, GT PRO software (Thermoflow) and the ISC Manager program developed at the National Research University "Moscow Power Engineering Institute (MPEI)". The analysis of CCGT efficiency in urban energy applications is based on modeling and optimizing the city’s fuel and energy balance (FEB). For this purpose, the OptiTEB program developed at the Department of PTS at the National Research University "MРEI" is used. Constructing fuel and energy systems in cities and regions is a pressing task that enables the planning of a strategy for the development of the urban fuel and energy complex (FEC), with an assessment of the magnitude of harmful emissions, including greenhouse gases. The article presents the results of work on establishing scientific foundations for modern heat supply systems, exemplified by a mathematical model of the Moscow thermal power plant and its optimization, taking into account the projected development of electric transport infrastructure over the coming decades, improving the thermal protection characteristics of newly constructed buildings, and the potential increase in the use of renewable energy sources (RES). The growth in the share of electric transport should be linked to an increase in the share of renewable energy sources (RES). Simply increasing the number of electric vehicles without a significant rise in renewable energy usage in the city's energy balance will not result in a reduction of carbon dioxide and harmful substance emissions. Expanding electricity generation through CCGT, alongside the growth of renewable energy use in the city, can lead to a significant decrease in carbon dioxide emissions.

THE MEDIATION EFFECTS OF FDI ON TRANSPORTATION INFRASTRUCTURE AND ECONOMIC GROWTH IN LAO PEOPLE’S DEMOCRATIC REPUBLIC

This paper aims to study the mediation effects of Foreign direct investment (FDI) on the relationship between transportation infrastructure and economic growth in Lao people’s Democratic Republic (Lao PDR) by using Sobel test. This study is quantitative research using panel data from 2013 to 2022. The regression results conclude that there is a positive relationship between transportation infrastructure and FDI by p-value=0.01. In addition, there is a positive relationship between FDI and Economic growth by significance at 0.05. Result demonstrates that there is direct effect and indirect effect between transportation infrastructure and Economic growth and Sobel test found that FDI was a partial mediator of the relationship between transportation infrastructure and Economic growth as well. In summary, transportation infrastructure enhances the attractiveness and efficiency of a region, drawing in FDI. The resultant FDI brings in capital, technology, and expertise, stimulating economic activities and contributing to overall economic growth. This mediating role of FDI underscores the interconnectedness of transportation infrastructure development, investment attraction, and economic growth.

Spatial Distribution of Public Lower Secondary School and its Impact on Students’ Enrolment in Ogun Central Senatorial District Ogun State, Nigeria

Given the quest for universal basic education, the large enrolments in the Nigerian secondary educational institutions has resulted in the problem of over-crowded classes, shortage of teachers and shortages of classroom, which portend enormous challenges to the development of the educational sector in Ogun state. Using Ogun Central as a case study, the study identified the spatial pattern of the schools; analyzing students’ enrolment trends in the area, and identifying the factors associated with the observed pattern. Spatial analysis tool in ArcGIS was used to analyze both the spatial accessibility index and the students’ annual enrolment trend of the lower secondary school in the area. The result of the nearest neighbor analysis for the spatial pattern of lower secondary schools in the study revealed a random pattern. This pattern implies that the lower secondary schools are located far from one another, which indicates, that for students to attend the schools, most of them need to cover long distances. The spatial distribution of junior secondary school is influenced by socioeconomic status, environmental factors, demographic characteristics, transportation infrastructure, and geographic accessibility, which are key determinants of educational equity. Linear regression analysis was employed to identify trends and predict future enrollments of students. Further, the results of the trend analysis of enrolments of students in the zone from 2020 – 2023 reveal a negative trend. This implies a decrease in students’ enrolment as the year increases. The four years (2020 – 2023) annual trend of enrolments in the zone is represented by the negative trend y = -3180.3x + 99400. This result suggests that the students’ enrolments in the zone would gradually keep decreasing as time goes on if quick remedial actions are not taken. However, factors such as shortage of teachers, shortage of classrooms, COVID-19, insufficient infrastructural facilities and fuel subsidy removal could be responsible for the negative enrolment trend

Toward Climate Resilient Asphalt Binder Selection in Canada

Climate change and extreme weather events present a fundamental challenge to pavement engineering and planning practice, given that transportation infrastructure has traditionally been planned and designed using historical climate data under the implicit assumption that climate is stationary and future conditions will resemble past ones. Transportation professionals should consider future risks scenarios in road design, material selection and system operations. This will be challenging given the inherent uncertainties in any climate projections. However, changes might be needed since transportation professionals are expected to deliver cost-effective and climate-resilient transportation infrastructure. The material selection plays an important role in the durability of flexible roads; therefore, it is important to consider the whole road design life cycle in terms of climate change and extreme weather events impact on asphalt selection as well as to consider different traffic speeds. This led to the development of the NRC Climate Adaptation and Asphalt Selection Tool (CAAST).

Effects of regional network economies on industrial productivity in Japan: dynamic total factor productivity function approach

AbstractImproving productivity performance is essential for achieving regional economic sustainability. With the advent of the network society, regional economic agents have benefited not only from local within-region interactions but also from external out-of-region economies. However, it remains unclear whether localized external economies or spillovers of externalities through inter-regional networks are more significant in improving productivity performance. Therefore, this study examined the relationships between productivity performance and geographical externalities from an industry perspective using regional economic data for Japan and employed a dynamic total factor productivity function approach. It also assessed the impact of high-quality transportation infrastructure improvements on productivity catch-up. The results revealed the long-run effects of inter-regional networks on the productivity performance of industries. Furthermore, it revealed that, as a geographical externality, the “borrowed size” effect contributed significantly to productivity improvements in both manufacturing and non-manufacturing industries. These findings suggest that enhancing both high-quality transportation infrastructure and inter-regional transportation networks is likely to improve industry productivity performance.

Achieving a framework of the circular economy in urban transport infrastructure projects: a meso-scale perspective

Urban infrastructure development is one principal way people are transforming the natural world and their living conditions. It is important for humanity, but it can also cause major impacts to the environment, such as huge amounts of solid waste and CO2 emissions. Considering this, the circular economy (CE) is a promising alternative to the traditional “make, use, and dispose” linear economy model. However, as a strategy for sustainable development (SD), the CE is still in its infancy in the urban transport infrastructure sector. Therefore, this article aims to guide the implementation of CE during transport infrastructure projects. To achieve this goal, a literature review and case study were adopted as the research methods. After reviewing existing well-established CE frameworks, the iReSOLVE (implement, Regenerate, Share, Optimize, Loop, Virtualize, Exchange) framework is recognized as the most comprehensive one. Upon it, an analytical framework containing specific-related aspects of CE in urban transport infrastructure projects (which belongs to meso-scale) is proposed (coined as the 4Wh-iReSOLVE framework). The 4Wh means Who, When, Where, and What. The proposed framework offers insight into potential CE activities for transport infrastructure projects and assists in assessing the performance and impacts of CE of these projects to cover the gap of the neglected meso-scale. Ten circular viaduct project initiatives in the Netherlands are used as case analyses with the 4Wh-iReSOLVE framework. The results present the highlights of the circular viaduct initiatives in the Netherlands, with CE activities categorized into five groups (design-related strategies, general CE strategies, implementation, management, and related digital technologies and materials, as well as environmental sustainability). As verified by several experts of the projects studied, it can be concluded that the 4Wh-iReSOLVE framework is suitable for transport infrastructure project CE analyses and implementations. It can potentially be a suggested guideline in future policy documents.

A Comprehensive Framework for Transportation Infrastructure Digitalization: TJYRoad-Net for Enhanced Point Cloud Segmentation

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.

THE ROLE OF INFORMATION FLOWS IN THE OPERATIONAL MANAGEMENT OF TRANSPORT

The management of transport flows is based on transmitting, receiving and processing the information related to them by applying the principles of the system approach. In a theoretical-applied aspect, it manifests itself as an economic activity for the development and implementation of methods for collecting, storing, processing and distributing information for specific functions and operations in transport infrastructure systems.

Real-Time Tracking System for City Bus Location And Passenger Count

This survey paper reviews the advancements in real-time tracking and passenger counting systems for city buses, emphasizing the growing need for efficient, data-driven public transit solutions. As urban populations rise, city transport systems face challenges in managing demand, reducing congestion, and improving service reliability. Real-time tracking systems using GPS, GIS, and IoT-enabled sensors enable passengers and transit authorities to monitor bus locations and optimize travel times. Additionally, passenger counting systems—utilizing infrared, AI-based vision models, and pressure sensors—provide data on bus occupancy levels, allowing commuters to make informed travel decisions and helping transit agencies manage resources efficiently. This paper examines existing technologies, compares various tracking and counting methods based on accuracy, cost, and scalability, and explores recent trends in artificial intelligence, big data analytics, and smart city integration. Furthermore, it identifies critical challenges, including data privacy, accuracy in urban environments, and energy consumption, proposing future research directions for enhancing the reliability, sustainability, and user-friendliness of these systems. Our findings underscore the transformative potential of real-time bus tracking and passenger counting in advancing public transportation infrastructure and enhancing urban mobility

An Autotuning Hybrid Method with Bayesian Optimization for Road Edge Extraction in Highway Systems from Point Clouds

In transportation infrastructure systems, feature images and spatial characteristics are generally utilized as complementary elements derived from point clouds for road edge extraction, but the involvement of one or more hyperparameters in each makes the extraction complicated. This study proposes an autotuning hybrid method with Bayesian optimization for road edge extraction in highway systems. The hybrid method combines the strengths of 2D feature images and 3D spatial characteristics while also automatically tuning the hyperparameter combination using Bayesian optimization. The hyperparameters encompass high and low pixel gradient thresholds, neighborhood radius, and normal vector threshold. Later, the point cloud dataset of national highways in Henan Province, China, is taken as the case study to evaluate the performance of the proposed method against three benchmark methods in two typical road scenarios: straight and curved edges. Experimental results show that the proposed method outperforms the benchmarks in detection quality and accuracy. It can serve as a decision-making tool to complement traditional manual road surveying, enabling efficient and automated road edge extraction in highway systems.