Study on Relationship of skid resistance and texture depth for Indian traffic condition

Abstract

Abstract This study aims to develop prediction models for skid resistance (SR), texture depth (TD), and the relationship between SR and TD for urban roads. These models are intended to help highway engineers by providing prior information about the maintenance and development of urban roads. The study collected data from about 250 road stretches of metropolitan roadways in Chennai, Tamil Nadu. This data includes information on skid resistance, texture depth, commercial vehicle per day (CVPD), Abrasion value (AV) and other relevant factors. This model was developed by using multiple linear regression (MLR) and Artificial Neural Network (ANN) techniques. This generated three prediction models: Model 1: Predicting skid resistance (SR). Model 2: Predicting texture depth (TD), and Model 3: Establishing a relationship between SR and TD for urban roads. The study divided the data into a "Training" set (70% of the data) and a "Validation" set (30% of the data) for model development and testing. The models created in this study have practical applications for highway engineers. They can use these models to assess SR and TD and make prior decisions for budget allocation, scheduling of repair and maintenance of roads scientifically.