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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.

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.

This study aims to investigate ground-related design deficiencies as potential avenues of avoidable cost overruns, discernible from the geotechnical practices of highway agencies in the Niger Delta region of Nigeria.

The study deploys an interpretivist qualitative methodology to provide a detailed descriptive analysis of the design-related geotechnical practices of highway agencies during the pre-contract phase of highway projects. Semi-structured interviews were conducted with in-house professionals, consultants and contractors affiliated with the three highway agencies in the Niger Delta and thematically analysed to identify significant deviations from geotechnical best practices.

The study outcome shows that during the pre-contract phase, a chain of design-related geotechnical shortcomings has plagued highway projects executed in the Niger Delta. This view of practice uncovered in this study demonstrates a culture of significant deviation from best practice recommendations, which could plausibly contribute to the history of significant project cost overruns recorded in the region.

The study qualitatively spotlights gaps in the practice of highway agencies and reinforces the need for a re-orientation of the attitude to risk management, to give geotechnical concerns a priority in the financial management of highway projects executed in the Niger Delta region of Nigeria.

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.

This study aims to analyze the interaction and key factors within the network of factors influencing the success of green highway projects.

Through literature review and interviews with experienced project managers, this study identified 33 influencing factors from the perspectives of stakeholders and life cycle. The interaction between these influencing factors was determined by surveying different experts, most of whom have participated in green highway projects in China. Then, social network analysis (SNA) was used to explore the impact and control ability of different factors.

According to the prioritization of these factors, the results showed that some key factors were identified, especially incremental cost, lack of standards and regulations, development of innovative technologies and materials, public awareness and environmental protection behavior. Finally, some meaningful suggestions were put forward for different influencing factors of green highway projects.

While the key influencing factors of the green highway projects have been identified by considering the interrelationships between different factors, the specific influencing paths and levels of different factors are not analyzed, more studies and methods should be conducted on this area in the future.

This finding of factors influencing the success of green highway projects which is useful for managers to overcome various obstacles encountered in similar projects. Moreover, considering the interaction among the influencing factors enables managers to make systematic and efficient decisions.

Developing green highways have been perceived as a major innovation to help achieve the synergy of environmental protection, economic development and social responsibility. Studying the key factors influencing the success of green highway projects and putting forward targeted suggestions are crucial for promoting the environmental protection transformation of highway construction.

Compared with most studies on the identification of key influencing factors of construction projects, this research emphasized the interaction between different factors within the system in the analysis process. The findings could provide useful references to promote the successful implementation of green highway projects.

Unisys ‘data transaction highway’ process flow was first presented to the Council of Logistics Management at its 1988 conference. This article will identify some specific developments that have occurred over the past year.

This paper describes the quality management function as practiced in highway construction. This function is displayed as an interrelated system, which identifies the main quality activities. Documents and records used in these activities are also reviewed. A model for multilevel quality management involvement is defined, encompassing contractors, engineers, and managers. The model describes the quality management tasks and the roles assumed in a scheme relating construction quality control, quality assurance, and the interface between them. The scheme can be applied to any construction domain and quality management organizational structure. The study advances the understanding of how quality management is performed and engages participants at several management levels.

This paper aims to investigate the statistical validity of geotechnical risk factors in accounting for cost overruns in highway projects. The study hypothesises that “latent pathogens” because of mismanaged geotechnical risk, which lay dormant in organisational practices of highway agencies, trigger cost overruns.

To test this hypothesis, cost and geotechnical data gathered for 61 completed highway projects, executed in the Niger Delta, recording unusually high cost overruns, along with qualitative data from 16 interviews with the project commissioners, were comprehensively analysed via regression modelling, to statistically explain recorded cost variance.

The results provide empirical evidence supporting a cause–effect relationship between the extent of cost overrun and key geotechnical factors. It is suggested that positive changes made in the geotechnical practices of the highway agencies will produce an expected exponential decrease in the level of cost overruns recorded in highway projects.

The study is limited to explaining the propagation of unusually high cost overruns in the geologic setting of the Niger Delta region of Nigeria. As such there is a need to test the generalisability of the theory presented.

The emergent view of geotechnical practice calls for further research, necessary to align geotechnical best practice into highway project delivery in the Niger Delta region.

The study used a robust methodological approach to understanding the propagation of cost overruns in highway projects, based on a characterisation of geotechnical intricacies, which is unprecedented in cost overrun research.

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.