  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.
