Cost and Time Efficiency Analysis of Road Condition Surveys: Manual vs Semi-Automated Methods

Roads are a fundamental component of transportation that plays a critical role in national economic growth. Maintaining road conditions is essential to ensure optimal traffic serviceability. However, in developing countries like Indonesia, these surveys are predominantly conducted manually. This conventional approach is time-consuming, costly, and requires a substantial amount of human resources. The swift progression of machine learning (ML) within Artificial Intelligence (AI) presents an opportunity to be utilized as a data processing tool for road condition surveys, leading to greater time and cost efficiency. This study analyzes the cost and time required for two survey methods: manual and semi-automated, employing machine learning. Based on the analysis conducted on 164 km of urban roads in Bandung City, the semi-automated ML method achieved a cost efficiency of 72.23%, with its total cost being only 27.77% of the manual method. Furthermore, the time efficiency reached 96.34%, meaning the survey was completed in just 3.66% of the time required by the manual approach. These results indicate that the application of machine learning for semi-automated road condition surveys is substantially more efficient in terms of both time and cost compared to traditional manual surveys.