Thesis

The rapid urbanization and population growth in cities have led to significant commuting challenges, including traffic congestion, pollution, and inefficient public transportation systems. Smart cities aim to leverage advanced technologies to create efficient, sustainable, and livable urban environments. This research explores the potential of Artificial Intelligence (AI), the Internet of Things (IoT), and Machine Learning (ML) algorithms in addressing commuting problems in smart cities. The study begins with a comprehensive review of existing literature on smart cities and their transportation challenges. It then delves into the technological frameworks that underpin smart city infrastructure, emphasizing the synergistic role of AI, IoT, and ML. AI and ML algorithms can analyze the data to predict traffic congestion, and enhance public transportation routes. The core of the research involves compare and test various AI and ML models to solve traffic congestion issues. This comparative study examines the per