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With the majority of highway projects in China having entered their operational phases, the maintenance and repair of the pavement is receiving increasing attention. One problem that needs to be addressed urgently is that of how to raise the proper funds for highway maintenance to ensure the sustainable operation of the project. To this end, the aim of this study is to investigate the capital demand for operation and maintenance of a project by means of a refinancing scheme, in order to reduce the possibility of project bankruptcy and to enhance the economic value of the project.

Based on an analysis of the dynamic complexity of the highway pavement maintenance system, a Markov model is used to predict pavement performance, and an optimal capital structure decision model is proposed for highway public–private partnership (PPP) project refinancing, using the method of system dynamics (SD). The proposed model is then applied to a real case study.

Results show that the proposed model can be used to predict accurately the dynamic changes in the demand for road maintenance funds and refinancing during the period of operation, before making the optimal decision for the refinancing capital structure.

Although many scholars have studied the optimal refinancing capital structure of PPP projects, the dynamic changes inherent in the demand for maintenance funds for highway PPP projects are seldom considered. Therefore, in the approach used here the influence of the dynamic change of road maintenance capital demand on refinancing is investigated, and SD is used for the optimal capital structure decision-making model of highway PPP project refinancing, to make the decision-making process more reasonable and scientific.

GASB Statement No. 34 required state and local governments to report information regarding general infrastructure in financial statements, to improve understanding of the organization's investments in capital assets. Some proponents suggested that this information would affect management practices and potentially resource allocation decisions, but initial survey data found limited evidence of effects. We use dynamic panel analysis covering 47 states from 1995 to 2009 to explore whether implementation of GASB 34 affected state highway capital and maintenance spending. We find evidence of increased capital spending, but no statistically significant change in maintenance expenditures. The choice of reporting method was not found to affect spending outcomes.

An important feature in managing road infrastructures is the growing use of performance-based contracts (PBCs) in the delivery of maintenance. The expectations are high. But there are also risks connected to PBC. The main question for road agencies is: how to achieve as much as possible of the expected advantages while limiting the possible disadvantages? The purpose of this paper is to answer that question and explore how PBC of maintenance can be improved.

Based on theoretical constructs this paper investigates the strategies of the English Highways Agency and the Dutch Rijkswaterstaat, when outsourcing the maintenance of their existing road infrastructures and the effects of their strategies.

The paper finds that road agencies should focus on the process of interaction of the main actors involved, rather than the performance measurement systems (PMS) itself. The agencies should adjust their governance to the degree of uncertainty. PBC requires an informed and knowledgeable principal.

The in-depth study is limited to two road agencies. More systematic research is needed in linking theoretical constructs with empirical evidence from more road agencies. The method applied in this paper can be used for further research.

The lessons drawn from the case studies offer potential benefits to other road authorities that use or consider PBC as their method of delivery of maintenance.

Where only few empirical studies have investigated in detail the actual achievements of PBC in road maintenance, this empirical research aims to fill that gap.

Given the significant amount of public infrastructure spending and the widely expressed concern about the declining quality of the American public infrastructure system, research about the effectiveness of public infrastructure investment is especially timely and crucial. The study extends public service production theory and public choice theory to the public infrastructure field, and develops a realistic and full theoretical model of public highway production by taking state highway efficiency differences into account. The panel fixed-effects method is used to examine the effects of state highway finance on state highway infrastructure quality. This study finds that state highway maintenance spending plays a crucial role in improving state road and bridge quality. Moreover, highway efficiency elements matter for state highway infrastructure quality.

The purpose of this paper is to present a study of reliability-centred maintenance (RCM), which is conducted on the key sub-assets of a newly constructed road junction infrastructure in Nigeria.

The classical RCM methodology, a type of RCM, which has a top down, zero-based approach for maintenance analysis, is implemented in this study.

The implementation of the classical RCM is successful in its application of various PM policies assigned to the assets and it shows that its application in the highway industry could reduce excessive maintenance backlog and frequent reactive maintenance by effective optimisation of its preventive maintenance (PM) intervals.

Road junctions are originators of more than 70 per cent of road traffic congestion and account for high accident rate. The traditional methods of reliability assurance used in the highway industry such as reactive maintenance and routine maintenance are often inadequate to meet the round the clock usage demands of these assets, thus the consideration for the application of a systematic RCM process for maintaining the system function by selecting and applying effective PM tasks.

It uses an approach that critically develops and analyses thoroughly preventive and continuous maintenance strategy in a new circumstance with environment of uncertainty and limited operating data. The case-based reasoning cycle has been applied in the RCM approach with real-time data obtained from a UK-based network maintenance management system for highway infrastructures.

Effective management of highways requires management of diverse data sets including traffic volume data, roadway, and road edge and road-side data. Like all major infrastructure clients, highways administration authorities are under pressure to use such platforms for better management of data that, in addition to creating other opportunities, allows improved life cycle management of asset data and predictive analytics. This paper aims to review such opportunities and the value that can be generated through integrated life cycle data management by leveraging Big Data and building information modelling (BIM).

A literature review is initially performed to systematically gather information to identify and understand BIM as a collaborative platform. Data management applications in other industries are also reviewed. Interviews were conducted and two industry workshops were organised to understand BIM implementation challenges within highways development projects and the role BIM can play in bridging inefficiencies resulting from loss of information at handover phases. The overall understanding lead to drawing up user needs, gathering system requirements and eventually a system architecture design to promote efficient information management throughout the asset lifecycle.

It is observed that data from the design and construction phases of projects can be used to inform asset registers from an earlier stage. This information can be used to plan maintenance schedules. Moreover, it can also be integrated with data generated from numerous other sensors to develop a better picture of network operations and support key decision-making. Effective road network management involves collection and analysis of huge data from a variety of sources including sensors, mobiles, assets and Open Data. Recent growth in Big Data analytics and data integration technologies provides new opportunities to optimise operations of highways infrastructure.

The system architecture designed for this research is translated into a prototype system as a proof of concept. However, it needs to be tested and validated by end users to be transformed into a useful solution for the industry.

This paper provides an enhanced understanding of new opportunities created to optimise operations of highways infrastructure using the recent growth in Big Data analytics and data integration technologies.

It is well known that public infrastructure, particularly the transportation infrastructure sector, is notorious for low efficiency, spending waste and corruption. Achieving the efficiency of public infrastructure investment is a crucial element to improve the current deteriorating condition of American transportation infrastructure system. The paper aims to discuss these issues.

This research utilizes an input-oriented, variable return to scale non-parametric data envelopment analysis to estimate the relative cost efficiency of highway infrastructure investment among the 48 American continental states from 1995 to 2009.

The empirical results reveal that there is a large efficiency variation among state highway infrastructure systems.

In addition, state governments on average reach 95.8 percent of the efficiency of their best practice peers in terms of providing quality highway infrastructure outcomes.

Automated condition surveys have been recently introduced for condition assessment of highway infrastructures worldwide. Accurate predictions of the current state, median life (ML) and future state of highway infrastructures are crucial for developing appropriate inspection and maintenance strategies for newly created as well as existing aging highway infrastructures. The paper aims to discuss these issues.

This paper proposes Markov Chain based deterioration modelling using a linear transition probability (LTP) matrix method and a median life expectancy (MLE) algorithm. The proposed method is applied and evaluated using condition improvement between the two successive inspections from the Surface Condition Assessment of National Network of Roads survey of the UK Pavement Management System.

The proposed LTP matrix model utilises better insight than the generic or decoupling linear approach used in estimating transition probabilities formulated in the past. The simulated LTP predicted conditions are portrayed in a deterioration profile and a pairwise correlation. The MLs are computed statistically with a cumulative distribution function plot.

The paper concludes that MLE is ideal for projecting half asset life, and the LTP matrix approach presents a feasible approach for new maintenance regime when more certain deterioration data become available.

The research presented in this paper aims to investigate highway construction/maintenance professionals' perceptions of the effects of night‐time construction conditions on worker visibility and of issues associated with safety vests in night‐time activities.

The research was conducted by administering a questionnaire survey to Illinois Department of Transportation operations personnel, resident engineers, contractors, and construction/maintenance professionals involved in night‐time construction in the Departments of Transportation of states other than Illinois.

It was found that most accidents in night‐time construction work areas are caused by the condition of the vehicle operator, that accidents are caused by through‐traffic and construction equipment operating inside the work area, and that the poor visibility of the workers plays an important role in accidents.

A national survey (rather than mostly Illinois personnel) would certainly increase the sample size and therefore allow researchers to validate the findings of this study and to conduct extensive statistical analyses.

Night‐time construction/maintenance operations on highways may be hazardous for both drivers and construction personnel because of poor visibility at night. It is recommended that the design of safety vests adhere to existing standards issued by the American National Standards Institute and the International Safety Equipment Association. It is particularly important to ensure adequate performance in wet weather conditions.

If safety vests are perceived by workers to be effective, the frequency and severity of night‐time accidents can be reduced and labor productivity can be enhanced.

The purpose of this paper is to present a methodology for estimating the “price,” or the not-to-loss value, of individual highway assets, which reflects not only the assets’ capital value but also economic productivity, by adopting a productivity-based asset valuation framework. The price tags can be used in prioritizing highway assets in support of transportation asset management processes.

The methodology adopts the utility theory to consider multiple performance measures reflecting the economic productivity generated by the assets, as well as their capital value. Key performance measures are first selected, and their values are retrieved from highway asset management databases. Next, the utility functions representing decision makers’ preferences convert the performance measures into utility values, which adjust the replacement cost (RC) of each highway asset to estimate price tags. To demonstrate its applicability, case studies were conducted for the highway networks of Texas and Washington State in the USA.

The methodology yielded price tags that better reflect the importance of highways’ roles in the economy in comparison to methods where only RCs are used. Furthermore, it was proven to be flexible enough to accommodate local conditions such as varying data availability.

The research provides a practical and reasonable way to prioritize critical highway assets in purport of maintenance and rehabilitation resource allocations, based on their economic productivity as well as physical condition and historical cost information, enhancing the overall efficiency and effectiveness of highway asset management.