Uncovering Socioeconomic Features in Pavement Conditions Through Data Mining: A Two-Step Clustering Model

Across the United States, individuals regardless of their socioeconomic backgrounds deserve equitable access to high-quality roads and highways. This research delves into the use of data mining methods to examine access quality, focusing on pavement condition through the International Roughness Index and socioeconomic factors, by exploring the Highway Performance Monitoring System (HPMS) dataset. Data mining serves as an exploratory process, unveiling and visualizing valuable yet not immediately evident insights within extensive datasets. Through data mining with two-step clusters, k-means, and hierarchical agglomerative clustering, we examined over 8 million records from HPMS and U.S. census data over four years. Our findings highlight the impact of socioeconomic elements-such as urbanization, income, and demographic composition-on pavement quality, beyond traffic, weather, and technical specifications. These insights emphasize the need for incorporating social equity into pavement maintenance and budgeting strategies, underscoring the significant role socioeconomic factors play in pavement performance.