Citizens from various cities around the world utilize different types of public transport to commute from one place to another. Additionally, information and communication technology (ICT) has been evolving over the last few decades, and governments are using it to improve the quality of the services provided to their citizens, such as public transport, together with the analysis of the available data. Thus, big data analytics is one of the technologies that are emerging as solutions to help improve efficiency in this specific segment. This thesis presents findings from a variety of articles by conducting a literature review about public transport, big data analytics, and the city of Natal, Rio Grande do Norte (RN), Brazil – the target city of this research. Specifically, the study sought to understand how big data analytics could improve the efficiency of public transport in Natal. Therefore, driven to answer the research question, issues were identified which had been caused by existing public transport in the city, which affected other sectors such as climate change, causes of environmental damage, vehicle engineering design, logistics, overpopulation, pollution, and traffic congestion. By implementing big data analytics solutions to each of these findings, promising outcomes were uncovered that may improve the public transport efficiency of this target city.