Data-Driven Maintenance of Heterogeneous Road Networks: Part 1— Integration of Cluster Analysis and Markov Chains for Pavement Deterioration Modeling

Road pavements deteriorate over time due to material aging and external factors, making timely maintenance essential. However, managing large road networks is complex due to their heterogeneity, with road sections affected by various deterioration factors. This research presents an analytical framework for road network management that uses cluster analysis to construct distinct road-section typologies and then develops stochastic pavement deterioration models for each typology. Utilizing data on pavement condition index and road network characteristics, this research proposes a framework by applying multiple correspondence analyses and hierarchical clustering on principal components to categorize road sections into seven typologies. Markov chains are then used to model pavement deterioration for each typology. The results identify key factors influencing pavement deterioration, including traffic loading, terrain, and the presence or absence of side shoulders. It is also observed that road sections in hilly areas deteriorate faster than those in plain areas within the provincial road network of Pakistan. This approach supports data-driven decision-making, enabling effective maintenance planning by identifying influential factors in large, heterogeneous road networks.