Pendekatan Machine Learning Berbasis Fitur Geospasial Imputasi Nilai AADT yang Hilang: Studi Kasus Texas - Afridayani; Afrisawati; Rizky Maulidya Afifa; Cut Try Utari (JURIKOM (Jurnal Riset Komputer), 2026)
Annual Average Daily Traffic (AADT) is an important indicator in transportation planning and road network performance evaluation. However, missing values ​​due to sensor interference or unrecorded data can reduce the quality of the analysis. This study aims to estimate missing AADT values ​​using a geospatial feature-based machine learning approach with a case study in Texas, United States. Automatic Traffic Recorder (ATR) data is integrated with road network attributes from OpenStreetMap (OSM) through a spatial join process to produce features such as road classification, number of lanes, and speed limits. A Random Forest model is used to build an estimation model based on valid data (AADT > 0). The evaluation results show a coefficient of determination (R²) of 0.548, indicating that geospatial features can significantly explain variations in AADT. The imputation process successfully produced a dataset with a 100% convenience level and a spatial distribution pattern consistent with the road network hierarchy and metropolitan area. This approach demonstrates that the integration of spatial data and machine learning is effective in improving the integrity of traffic data to support data-driven decision making.