Performance Analysis of Bandung City Traffic Flow Classification with Machine Learning and Kriging Interpolation

Abstract

This research focuses on making classification maps using the Classification And Regression Trees (CART), Random Forest and Ordinary Kriging methods. The dataset used is data from the Area Traffic Control System (ATCS) of the Bandung City Transportation Agency and the Google Maps application in April 2022. After the dataset is obtained, then the data pre-processing process will be carried out then the CART and Random Forest classification learning models will be made, after the CART and Random Forest classification learning is complete. From the CART and Random Forest classification models, traffic congestion classification map will then be made using the ArcMap application with the Ordinary Kriging interpolation method. The results of the comparison of classification maps made with Ordinary Kriging interpolation with the Gaussian Model semivariogram in both methods, namely CART and Random Forest. With the CART method has an accuracy of up to 88% while the classification map made with the Random Forest method has an accuracy of up to 90%. This proves that in this study the Random Forest method is far superior in building classification maps compared to the CART method