Managing Transportation Infrastructure with Markov and Semi-Markov Models

Effective management of transportation infrastructure requires accurate modeling of system dynamics and deterioration processes. This study explores the application of Markov and Semi-Markov models as decision-support tools for the maintenance and rehabilitation of transportation assets, such as roads, bridges, and transit systems. Markov models are employed to represent the probabilistic transitions of infrastructure condition states over discrete time intervals, enabling planners to estimate long-term performance and optimize maintenance policies. Semi-Markov models extend this framework by incorporating variable sojourn times, allowing for more realistic modeling of time-dependent deterioration and maintenance effects. By comparing the predictive capabilities and computational performance of both models, this research highlights their respective advantages and suitability for different infrastructure management scenarios. The findings support the integration of stochastic modeling approaches into infrastructure asset management systems, leading to improved decision-making, cost-efficiency, and service reliability. For many years, pavement and bridge management systems have included Markov models. Semi-Markov models have been used in Bridge Management Systems in more recent years. According to research, this stochastic technique can be used to predict future network level conditions and to develop preservation models for transportation infrastructure if there is sufficient data to create semi-Markov models for that infrastructure. These methods can be used in numerous contexts and are not just restricted to transportation infrastructure.