Advantages of Machine Learning in Bus Transport Analysis

Abstract

Supervised Machine Learning is an innovative method that aims to mimic human learning by using past experiences. In this study, we utilize supervised machine learning algorithms to analyze the factors that contribute to the punctuality of Tehran BRT bus system. We gather publicly available datasets of 2020 to 2022 from Municipality of Tehran to train and test our models. By employing various algorithms and leveraging Python's Sci Kit Learn and Stats Models libraries, we construct accurate models capable of predicting whether a bus route will meet the prescribed standards for on-time performance on any given day. Furthermore, we delve deeper into the decision-making process of each algorithm to determine the most influential factor it considers. This investigation allows us to uncover the key feature that significantly impacts the effectiveness of bus routes, providing valuable insights for improving their performance.