Analyzing Public Transit Schedule Deviations: A Case Study on Montreal Using Real-Time Data

Metropolitan cities heavily rely on Intelligent Transportation Systems (ITS) to enhance the overall well-being of their citizens. Despite the implementation of various policies and strategies aimed at improving the reliability and quality of public transportation services, transit authorities consistently face criticism from commuters. The main cause of dissatisfaction arises from deviations in scheduled bus arrival times, leading to either early or late arrivals that disrupt the schedules of commuters. These deviations can result in missed appointments, prolonged wait times at bus stops, and instances of being late for work. This paper provides a preliminary analysis of the public transit system in Montreal City, focusing on delays and deviations. It utilizes planned and real-time transit data to quantify, locate, and classify deviations as systematic (i.e., deviations that are accommodated in the schedules by the transit authority) or stochastic (unforeseen deviations, e.g., due to sudden road accidents). The paper also explores using machine learning models to predict stochastic delays.