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This study explores the application of machine learning (ML) models, specifically linear regression, random forest, and neural networks, to predict the pavement condition index (PCI) for the street network of Skellefteå municipality in Sweden over a four-year period. The random forest model demonstrated the highest accuracy with R2 values of 0.59 for residential streets and 0.79 for main, collector, and industrial streets, highlighting the importance of pavement age as a key variable. The findings suggest t

Author

saksham dixit

Language

EN