Roadway and highway agencies across the globe spend a sizable fraction of their annual budget for the upkeep and maintenance of roadways. Different road segments deteriorate at different rates owing to variable traffic flow along the segments. In previous works, various forms of mathematical formulations were provided for roadway maintenance and pavement deterioration modeling. Numerical solutions algorithms using linear programming, dynamic programming, and genetic algorithms were proposed. The solution algorithms, however, did not benefit from the prescriptive and predictive capabilities of machine learning (ML) algorithms (e.g., random forest classifier, support vector machine, and artificial neural networks). Furthermore, previous methods treated transition probabilities of condition states of a pavement in future years to be static. In this paper, a variable transition probability is introduced based on the deterioration rate of a pavement over time. A modified capacitated arc routing formulation is developed for a highway infrastructure management information system. Prescriptive and predictive analytics are performed using ML to analyze the road network in simulation studies and from Montgomery County, Maryland, USA. The pavement condition index (PCI) for the road network is predicted using ML algorithms. The results show a good promise for PCI prediction based on variable deterioration rate and for obtaining condition states in future years subject to varying transition probabilities. Received: 2 November 2023 | Revised: 12 December 2023 | Accepted: 25 December 2023 Conflicts of Interest Manoj K. Jha is an Associate Editor for Journal of Computational and Cognitive Engineering and was not involved in the editorial review or the decision to publish this article. The author declares that he has no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Manoj K. Jha: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization.

Determining the optimal location of a fleet of vehicles is necessary in a number of potential applications, such as special repair vehicles for buses on a large public transportation network. The Athens Urban Transport Authority operates a large bus fleet over an extensive network for 19 h a day and serves a population of approximately 4 million people, all in a heavily congested road network. During the 2004 Summer Olympic Games, held in Athens, most spectators, employees, and volunteers were transported to and from Olympic Games venues by public transportation. Dedicated Olympic Games bus lines operated under a tight around-the-clock schedule. During normal operations and particularly during events such as the Olympic Games, incidents such as vehicle breakdowns and minor accidents can have a severe effect on the operation of the public transport network and can cause a significant decrease in the level of service. To help the authority locate bus repair vehicles over the entire network, a decision support system was developed on the basis of an embedded genetic algorithm used for obtaining optimal location solutions. The system's design and performance make it easy to operate under real-time conditions, which is useful for planning and for fast vehicle redeployment.

Highway maintainability is a significant issue, which should be considered in the planning stages of a highway development process. A number of infrastructure models such as highway maintenance management systems, bridge management systems, and pavement management systems have been developed to predict future maintenance expenditures. However, these models only predict maintenance costs after a highway is built, and do not provide the flexibility of exploring various alternatives in the planning stages. In this work formulations for initial and maintenance costs have been developed based on the design variables which can be chosen to minimize these costs, focusing on the sideslopes in cut and fill sections. Maintenance cost has been represented as a function of sideslope width of highway cross section and annual average daily traffic. Sideslope is represented as a function of soil type. The proposed formulations are used in a highway design optimization model to optimize highway alignments. An example is presented to investigate the sensitivities of maintenance cost and soil characteristics in selecting alignments, using a real geographic database from Maryland. The results indicate that maintenance cost and soil characteristics are important considerations in alignment optimization.