Road Asset Management Research Articles

Road Boundary Extraction Method from Mobile Laser Scanning Point Clouds

Abstract With the rapid development of mobile laser scanning (MLS) technology, high-precision three-dimensional point cloud data has shown great potential in different fields such as topographic mapping, road asset management and smart city construc-tion. Three-dimensional point cloud data contains not only position information, but also the shape and attributes of the target, which is very convenient for obtaining road information. Accurate extraction of the road boundary is a basic task for obtaining road infrastructural data, which can support the generation of high-precision maps, vehicle navigation and auton-omous driving. However, road boundary extraction in urban environments is easily occluded such as vehicles and pedestri-ans on the road, which leads to problems such as difficulty and incomplete extraction of MLS point cloud road boundaries. To address this problem, this study proposes a road boundary extraction method that integrates pavement edge information, accurately considers the position of the road boundary from two dimensions, eliminates false boundaries, and completes the missing boundary through the extracted boundary spatial relationship. First, grid elevation filtering is used to remove high-level non-ground points. Then the pavement edges and curb stone points are extracted from the preprocessed point cloud, and they are superimposed to remove false boundary points to obtain accurate road boundaries. Finally, based on the spatial relationship of road boundaries, missing parts are detected and repaired to obtain complete road boundaries. Experimental results show that the accuracy on real road scenes exceeds 98%, the completeness rate is above 91%, and the extraction quality is above 90%, which verifies the effectiveness and accuracy of this method.

Lifetime engineering principles in highway asset management

Introduction. An important tendency of our time is sustainability, good value for money and long-term planning. These are also the goals of the recently developed discipline, lifetime engineering. Although the principles of the science were originally developed for buildings and engineering structures, they can also be adapted to public roads. The article — through a case study in Hungary — presents the applicability of life engineering science to public road asset management. Besides, examples of pavement structure design, durability, maintenance-operation and environmental protection are also presented. Problem Statement. The typical lifetime engineering science principles used in the presentation were: increasing (pavement) life cycle; complex, multi-disciplinary design methodology; modular design; effective, quality insurance methods; high level satisfaction of the customers’ needs; minimisation of lifetime costs; sustainable, end-of-life strategies. It is emphasized that lifetime engineering is based on specific basic principles that effectively promote an up-to-date approach to any discipline related to engineering infrastructure. The paper shows that the efficacy of a road asset management. can be considerably increased if some elements of lifetime engineering are utilized.

Abnormal Pavement Condition Detection with Vehicle Posture Data Considering Speed Variations.

Pavement condition monitoring is an important task in road asset management and efficient abnormal pavement condition detection is critical for timely conservation management decisions. The present work introduces a mobile pavement condition monitoring approach utilizing low-cost sensor technology and machine-learning-based methodologies. Specifically, an on-board unit (OBU) embedded with an inertial measurement unit (IMU) and global positioning system (GPS) is applied to collect vehicle posture data in real time. Through a comprehensive analysis of both time domain and frequency domain data features for both normal and abnormal pavement conditions, feature engineering is conducted to identify how the most important features affect abnormal pavement condition recognition. Six machine learning models are then developed to identify different types of pavement conditions. The performance of different algorithms and the significance of different features are then analyzed. Moreover, the influence of vehicle speed on pavement condition assessment is further examined and classification models for different speed intervals are developed. The results indicate that the random forest (RF) model that considers vehicle speed achieves the best performance in pavement condition monitoring. The outcomes of the present work would contribute to cost-effective pavement condition monitoring and provide an important reference for pavement maintenance sectors.

Fuzzy Rule Based System of Optimal Pavement Maintenance and Rehabilitation Strategies for Yangon-Mandalay Expressway

Purpose: Systematic pavement management plays a vital role in the better Road Asset Management system. The lifespan of pavement can be preserved and prolonged by adequate maintenance measures in proper time. By early detection and repair of defects at initial stages with predictive maintenance reduces the rapid deterioration of the pavement. Moreover, inaccuracy in pavement deterioration prediction leads to an improper maintenance with wastage of time, cost and labor strength. To overcome all of these obstacles, this paper attempts an approach on optimal pavement maintenance strategy by means of fuzzy rule-based system. Materials and Methods: In this research, four pavement indicators of PCI, IRI, PSR and PSI have been taken into account as antecedents and five different types of pavement maintenance as consequents. In this research, ASTM D-6433, Road Lab Pro software, AASHTO guidelines and Fuzzy Logic (Mamdani) model have been used to evaluate the current pavement condition and optimal pavement maintenance strategies. Findings: According to research area, 130 total pavement sample units were undertaken to study for the mean pavement condition of PCI value 54.93 (poor), IRI 7.01 (fair), PSR 3.33 (fair) and PSI 3.35 (fair). Due to 13 years-old life span of traffic volume, temperature and other external factors, this expressway is suffering from potholes, bleeding, longitudinal and transverse cracking, weathering, joint reflection cracking for Asphalt Concrete (AC) overlay and linear cracking, joint seal failure, scaling, faulting, depression, lane/shoulder drop off for existing concrete pavement. According to the fuzzy logic model results, 42 numbers of fuzzy If-Then maintenance rules were verified as the most relevant pavement maintenance and rehabilitation strategies for Yangon-Mandalay Expressway. Implications to Theory, Practice and Policy: This research provide implications to the study and contributions to theory, practice and policy. This research paper insights into effective infrastructure maintenance and investment, emphasizing the role of advanced computational techniques in transportation economics. The key advantage of this research paper is to be effective, supportive, easy and accessible decision-making tool for transportation and pavement engineers, expressway planners and highways department authorities especially from developing countries.

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.

Comparing Mobile and Aerial Laser Scanner point cloud data sets for automating the detection and delimitation procedure of safety-critical near-road slopes

An inappropriately maintained road cut-slope is likely to fail, resulting in landslides or falling rocks that compromise road safety. Thus, road managers need to know the location of dangerous slopes along the road in order to prevent these events from happening. In this article, we compare two different approaches for conducting the digitization of the road environment and the automatic detection and delimitation of road slopes: Mobile Laser Scanners (MLS) and Aerial Laser Scanners (ALS). The point clouds obtained using the first kind of devices are dense, rich in detail and generated from a ground perspective; the second type of scanners produce less dense clouds from a zenithal perspective. We explore what is the effect of the point cloud density and scanner point of view over the slope detection procedure. Two road segments from the Spanish A55 and A52 highways were used as study zones, and a total of 28.61 km were analyzed. Better detection and delimitation results were achieved when using the ALS data and its corresponding algorithm. It was observed that the higher point density and detail of the MLS clouds were not an advantage for the slope detection task, and that measuring the road from a terrestrial perspective affected in a negative way during the detection process: the crest of the slopes often remained unmeasured, hidden behind vegetation or man-made elements, thus resulting in the slopes not being complete in the MLS clouds. Meanwhile, the whole slope structure is scanned when the scene is measured from an aerial perspective, henceforth obtaining better detection rates despite the relatively low resolution. The findings of this study provide valuable information in the field of road asset management, and help road managers make decisions when choosing what technology to use for the data gathering process.

An ensemble learning with sequential model-based optimization approach for pavement roughness estimation using smartphone sensor data

Pavement roughness monitoring has always been a concern in the field of road asset management. However, the utilization of laser profilometers to measure pavement roughness is costly and inconvenient. To address these challenges, this study developed a low-cost, lightweight, and rapid approach for pavement roughness estimation using smartphone sensor data. Firstly, the feasibility of the chosen smartphone in vibration acceleration acquisition was examined through micro-scale shaking table tests. Then, the Categorical Boosting (CatBoost) model with sequential model-based optimization (SMBO) approach was developed for smartphone-based pavement roughness estimation. Finally, the feature importance and feature interaction effects in the pavement roughness estimation task were interpreted using Shapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP), respectively. The results show that the chosen smartphone has great potential in vibration data acquisition, and the recorded data are highly consistent with the results obtained by the professional accelerometer. The CatBoost model outperforms the other reference models in terms of pavement roughness estimation accuracy, with coefficient of determination (R2) of 0.807, root mean square error (RMSE) of 0.553, and mean absolute error (MAE) of 0.433. Furthermore, the feature interpretation results indicate that the smartphone-based pavement roughness estimation approach relies on the coupling effects of vibration and velocity features. This study provides a novel insight into the lightweight pavement roughness estimation approach, which has potential implications for assisting pavement maintenance decision-making in road asset management.

Road Asset Management System and Operation and Maintenance of World Bank-Funded Roads in Lira City, Uganda

Purpose: This study examined the contribution of road asset management to the operations and maintenance of World Bank funded roads in Lira City East division. Specifically, the study focussed on the effect of data collection, data management; and data analysis on operation and maintenance of World Bank funded roads in Lira City East division.
 Methodology: The study adopted a cross-sectional study design, which employed a mixed methods approach of both quantitative and qualitative methods. The study population consisted of 82 respondents, which involved political leaders, technical staff and city development forum (CDF) members of Lira City East division. Both the Simple random sampling technique and purposive sampling technique were used to select a sample of 71 participants. The researcher adopted Self-Administered Questionnaires to collect quantitative data and an interview guide to collect qualitative data. Both descriptive statistics, and inferential statistics were used to analyse numerical data. Content analysis was used to analyse qualitative data.
 Findings: The findings revealed a statistically significant positive relationship between road asset management and the operation and maintenance of World Bank funded roads. Further, it was revealed that data collection, data management and data analysis yield a significant effect on operation and maintenance of World Bank funded roads in Lira City East division.
 Unique contribution to theory, policy and practice: The study concluded that road asset management significantly affects operation and maintenance of World Bank funded roads in Lira City East division. It is encouraged that, for proper functionality and operations of world bank funded roads in Lira City division, the authorities of Lira City east division should do periodic inspection of all the roads, engage knowledgeable personnel in the collection of data on the conditions of all the road assets so that it can be used as a basis of decision making during the development of work plan and budgets for road maintenance. This study contributes an original and empirical-evidence of the contribution of road asset management on the operation and maintenance of roads in Lira City East division.

Road Asset Management and the Vehicles of the Future: An Overview, Opportunities, and Challenges

Connected and automated vehicles (CAVs) have the potential to significantly impact transportation systems in terms of mobility, the environment, safety, and the economy. These vehicles rely on a range of sensors and cameras to detect road signs and lane markings, as well as to scan their surroundings, and they are connected to other vehicles and infrastructures. Previous research has highlighted the need for transport asset management processes to manage the intrinsic aspects of CAVs more effectively, with a view to improving performance, resource utilization, and budget planning. However, little literature was found with a deep discussion of how CAVs will impact asset management. In this article we offer an initial discussion on the impacts of CAVs on road asset management. To do so, we first provide a short introduction to CAVs, followed by an overview of road asset management. We then comprehensively discuss many asset management aspects that are affected by CAVs. Finally, future research opportunities, challenges, and important subjects are outlined.

Risk Assessment on Toll Road Asset Management

Risk Assessment on Toll Road Asset Management

Assessment of Level of Service for Roads under Performance Based Road Maintenance in Kenya

Purpose: This paper seeks to assess Level of Service (LOS) of roads under performance-based road maintenance (PBRM) in Kenya. This was done through a comparison of actual ratings with performance targets defined by the agencies for each asset item within a sample unit. The comparison was used to determine if each asset item had been preserved at minimum acceptable Level of Service (LOS) with objective of establishing if there are any benefits accrued from adopting the PBRM method.
 Methodology: Seven roads were identified in the study area and were divided into 14 sample units, each sample unit containing different asset items to be assessed. Data from the field was collected and recorded using field measurement tools for linear measurements. Dynamic Response Intelligent equipment (DRIMS) was used for measurement of International Roughness Index (IRI). Raw data was converted into performance information using guidelines from Quality Assurance in Highway Maintenance Programs and measurement of conformance conducted using Schedule Performance Index.
 Findings: The performance obtained indicated that the goal objective for each road was not achieved with an overall performance deviating from the target performance by 25.05%. However, there was 10% compliance at the asset group level and 35% compliance at asset item level. Noncompliance at road section level, 10% compliance at asset group level and 35% compliance at asset item level signifies inadequacies in PBRM practice in Kenya. This informs need for improvement by road agencies and contractors for optimum benefits from PBRM. There is need for contractor capacity building and enhanced partnering environment between contractors and management engineering consultants to provide technical assistance for improved contractor compliance in PBRM practice.
 Unique Contribution to Theory, Practice and Policy: The Concept of Performance based road maintenance (PBRM) is meant to solve problems arising from traditional contracting methods. As demonstrated in this study, effective application of PBRM principles results in cost savings in managing and maintaining roads, expenditure certainty, reduced inhouse workforce, greater road user satisfaction and improved conditions of contracted road assets. The requirement for contractors to provide a product of predefined quality is a definite means of ensuring that the road asset is performing over extended time. It is recommended that PBRM is adopted widely in road asset management to achieve an effective and efficient road network.

The Impact of Road Development on Household Welfare in Rural Papua New Guinea

In this paper we evaluate the impact of road development on household welfare in rural Papua New Guinea (PNG) between 1996 and 2010, using two geocoded cross-sectional national household surveys and corresponding road maps. We make use of time-variation in road surface type and condition as recorded in PNG’s National Road Asset Management System, focusing on routes that connect rural households to urban areas. To tackle endogenous placement of road infrastructure programs, we employ a correlated random effects model that controls for the location-specific average road quality over the period of analysis. We also use a newly developed generalised quantile regression method to investigate whether road works favour the poor. Our estimates show that better roads to nearest towns lead to higher consumption levels and housing quality, and to less reliance on subsistence farming. The effects are stronger among poor and remote households.

Towards a digital twin-based intelligent decision support for road maintenance

The digitalisation, automation and robotisation of road inspection and maintenance technologies make it possible to collect bigger volumes of data and additional types of information about road infrastructure. Methodologies and tools to support road asset management decision-making are needed to exploit this new information, progressing towards predictive maintenance and improving different aspects of road asset management. This study presents a Digital Twin-based Decision Support Tool to assist road operators in road inspection, maintenance and upgrade. The goal of the paper is twofold. First, the architecture of the Digital Twin-based Decision Support Tool is presented, describing the main components and functionalities. The system is based on a Digital Twin (DT) that mirrors real road assets to integrate different sources of data and support the processing of low-level data into high-level information. The decision support tool (DST) is able to analyse the collected information and compute the road pavement condition to derive optimal intervention plans, addressing road section conditions, human and technical resources and other external constraints. Second, the application of the proposed architecture to road pavement maintenance is described, considering the Italian highway A24 and its connections with Rome´s ring road, managed by Strada dei Parchi SpA. Road pavement data, such as the International Roughness Index (IRI) and the Sideway Force Coefficient (SFC), are integrated into the DT to be analysed through Artificial Intelligence-clustering techniques to perform the sectioning and clustering of road sections according to their status and quality index. The paper shows the benefits derived from the integration of DT technologies with DSTs for improving processes of road maintenance.

Comparing Different Equipment and Applications in Pavement Data Collection as Part of Road Management System

Abstract National roads are the main arteries in road transport infrastructure. Therefore, all agencies or authorities responsible of road infrastructure, pay attention to road management systems. Albania is experiencing an increase in road infrastructure investments and maintenance of this road network. There have been some attempts to establish national and secondary road management systems. These systems attempt to achieve different objectives, such as the provision of an adequate level of service, the preservation of the road infrastructure, etc. A good Road Asset Management System (RAMS), helps to carry out all the actions of inventory, storage and maintenance of road assets as well as, supports the decision-making process. At present, there are several data collection devices and applications that carry out the job efficiently. The purpose of this paper is to present the analysis of the use and comparison of some equipment and Cell Phone Based Systems (MiniROMDAS, PaveProf-V2 and RoadLab_Pro) used for the road pavement data collection, necessary in the calculation of the International Roughness Index (IRI), along the national road network in Albania. The comparison is made, by analyzing the data and results obtained along a 20 km long road segment in Albania, using the various above-mentioned technologies. Also, an overview of the currently available technologies providing information that could assist managers in establishing an appropriate data collection program is given.

A Study of Point Laser for Automatic Local Road Condition Assessment: Feasibility, Challenges, and Limitations

Road condition assessment plays an important role in maintaining the integrity of mature and fast-expanding road networks. Such assessments provide invaluable information for local authorities that enable them to deliver timely preventative road network maintenance. To enable the best use of scarce maintenance resources, it is necessary to assess the condition of the roads periodically. In this article, we present the Point Laser (PL) system as an alternative approach for automatic road assessment. Compared to current systems, ground penetrating radar and the mobile laser scanning system, the PL system has the advantages of low hardware cost, low computational requirements, and fewer environmental restrictions in relation to its use. The article first outlines the principle of the PL system including a low-complexity signal processing approach. Afterward, the feasibility, challenges, and limitations of the PL system are discussed based on its use in a real environment. A visual-based road survey is used as a ground truth to determine the PL’s performance in relation to the requirements of road condition assessment at various levels of road management. The results of the comparison demonstrate that the PL system is an ideal candidate for the automatic assessment of the condition of local road networks, especially those that require frequent assessment for road asset management.

Assessment of vehicular fuel consumption and interaction with pavement characteristics using HDM-4 on Indian urban road network: A case of Pune city

Rapid urbanization and exponential increase in the number of vehicles on city roads have triggered issues like air pollution and accidents, endangering the health of millions of people in Indian cities. Vehicular emission is a major contributor to air pollution in Indian cities. This study primarily analyzes the relative effects of riding quality and pavement surface type on vehicular fuel consumption (FC) in an urban environment. This study utilizes the Road Asset Management System (RAMS) applications to predict FC in different scenarios of pavement maintenance. Highway Development and Management (HDM-4) software was integrated into RAMS for network & economic analysis and FC prediction of the road network. The study was conducted on the Pune city road network in India, and the results revealed a 2.32 % reduction in fuel use if timely maintenance is provided. Over 0.128 million liters of fuel per 1000 vehicle-km can be saved over the study network of only 36.5 km over the next 15 years, equivalent to US$ 155,206.18 in 2021. The analysis of covariance test results showed the significant effect of pavement surface type on fuel consumption, with 4.86 % less fuel consumed on cement concrete surfaces than bituminous surfaces. About 99.431 liters per 1000 vehicle-km of fuel were utilized by the vehicle fleet on concrete pavements, while 104.513 liters per 1000 vehicle-km of fuel were consumed on bituminous pavements after adjusting for roughness characteristics of both the surface types.

Construction of a simulation model for substantiating the parameters of long-term road maintenance contracts

This paper reports a study into the main parameters of long-term contracts for the maintenance of roads. Weaknesses in existing methods and models of substantiation of the initial characteristics of contracts have been identified. It is established that the main reason for the transition to long-term contacts in the road sector is the need to improve the efficiency and effectiveness of road asset management. This determines the main goal to maintain the operational condition of all components of roads at a level that ensures the satisfaction of user requirements and contributes to the preservation of assets. A simulation model for substantiating the parameters of long-term contracts for road maintenance has been built, which makes it possible to simulate forecast assessments of the characteristics of contracts. Underlying this study is the Monte Carlo method, as well as the triangular law of distribution, models of deterioration and restoration of the condition of road elements. Taking into consideration these models, it was established that the error in justifying the parameters of long-term contracts according to the devised method is up to 10 %. The devised model was tested by applying the developed original LTCsimula program using an example of the section of a motorway with a length of 87.3 km with an average level of requirements. According to the results of the test, the assessment of the laws of distribution of value, as well as the amount of deductions and profits of a long-term contract, was carried out. The calculation results demonstrated the model’s capability to determine the strategies for the maintenance of roads, taking into consideration the risk of implementing a contract with an error of up to 3.9 %. The practical use of the devised simulation model makes it possible to improve the efficiency of operational maintenance of roads, as well as to save from 10 % to 40 % of the cost of road maintenance

Road Asset Value Calculation Based on Asset Performance, Community Benefits and Technical Condition

The article presents a comprehensive asset management method. Here presented method aims to bridge the economic approach to asset management with the technical approach to road infrastructure life cycle, namely its resilience and performance. The presented asset value calculation methods are based both on socio-economic viewpoints on community benefits of an asset, as well as the technical aspect of the technical condition and residual life calculations of a road infrastructure. In contrast to common road asset management methods, asset value is not arbitrarily annually depreciated, instead, it is exactly calculated based on pavement performance models, pavement construction fatigue and paving material properties. Road asset value calculation is based on the asset performance and the technical condition of a pavement structure and other objects. Road asset performance is defined in terms of society and road user demands put on road category and its qualitative standard. Road asset technical condition is evaluated by the procurement cost calculation and condition deterioration. Value of condition deterioration is defined by residual life expectancy based on fatigue and construction reliability of the road infrastructure. The cross-asset allocation method is used for the creation of programs for claim and allocation of funding. The aim was to increase the credibility of the road administrators with the public as they present their decisions based on road asset management, and to increase the level of acceptance for practitioners.

A Probabilistic Approach to the Evaluation of Seismic Resilience in Road Asset Management

Road networks are classified as critical infrastructure systems. Their loss of functionality not only hinders residential and commercial activities, but also compromises evacuation and rescue after disasters. Dealing with risks to key strategic objectives is not new to asset management, and risk management is considered one of the core elements of asset management. Risk analysis has recently focused on understanding and designing strategies for resilience, especially in the case of seismic events that present a significant hazard to highway transportation networks. Following a review of risk and resilience concepts and metrics, an innovative methodology to stochastically assess the economic resources needed to restore damaged infrastructures, one that is a relevant and complementary element within a wider resilience-based framework, is proposed. The original methodology is based on collecting and analyzing ex post reconstruction and hazard data and was calibrated on data measured during the earthquake that struck central Italy in 2016 and collected in the following recovery phase. Although further improvements are needed, the proposed approach can be used effectively by road managers to provide useful information in developing seismic retrofitting plans.

Improvement of the Algorithm for Computing Adjusted Structure Number for Determination of Backlog Maintenance

The pavement strength is very important for the evaluation of backlog maintenance. The current trend in many developing countries used pavement conditions index-PCI in estimating maintenance costs. The PCI can only justify periodic and routine recurrent maintenance. The condition strength is rarely determined in a flexible pavement creating an opportunity for back long maintenance. This paper reports the study conducted to develop and improve the algorithm for estimating the adjusted structure number to estimate the remaining thickness of the flexible pavement. The analysis of eight ways of computing structure numbers from FWD data ware analyzed and found that the improvement of the HDM 3 - 4 models can influence the usefulness of data collected from road asset management in Tanzania. The algorithm for estimating structural numbers from CBR was improved to compute adjusted structural numbers finally used to estimate the remaining life of the flexible pavement. The analysis of the network of about 6900 km including ST and AM was found that 64.72% were very good, 12% were Good, 10% were fair and 7.84% were poor and 5.4% were very poor.