Improving customers' service of a transit bus system with the help of machine learning and artificial intelligence

In this research, the aim is to analyse the data related to a bus transit system and to extract patterns from these data, as well as anomalies. These patterns and anomalies will then be notified to the affected customers who would benefit from these notifications. The project will be conducted using CRISP-DM (cross-industry standard process for data mining) methodology, which is, developing a business understanding as the first step and then understanding the available data along with their limitations and usefulness. These data will be then prepared for modelling and testing, with all necessary transformations. Several different models will be developed and tested using a variety of validation techniques and the most appropriate model will be selected for deployment. The outcome of this project will be a prototype dashboard application which will act as a proof-of-concept. This prototype will ultimately lead to a commercial production-grade web application for customers.