The chapter explores how artificial intelligence (AI) is transforming traffic
management and urban mobility in response to rising vehicle volumes, congestion, and
environmental concerns. It highlights key AI technologies such as machine learning,
deep learning, computer vision, and reinforcement learning, which enable adaptive
traffic systems capable of responding to real-time conditions. Advanced sensor
technologies, including IoT devices, cameras, radar, and LiDAR, are discussed as
essential tools for collecting the data that fuels AI-driven decision-making. The
integration of AI with intelligent transportation systems (ITS) and connected vehicle
technologies, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I)
communication, is presented as a game-changer for improving traffic flow and safety.
The role of big data analytics in analyzing large datasets to optimize traffic patterns is
also explored, along with AI-based traffic simulation models that predict behavior and
test scenarios before implementation. The chapter emphasizes sustainability,
showcasing how AI can streamline traffic routes, reduce fuel consumption, and lower
emissions. It also addresses challenges such as data privacy concerns, cybersecurity
risks, and the need for substantial infrastructure investment. Case studies from cities
successfully implementing AI-driven traffic solutions are included, highlighting
improvements in congestion, safety, and environmental impact. By combining insights
into technology, applications, and real-world examples, the chapter offers a
comprehensive perspective on AI’s potential to revolutionize urban transportation
systems.
Keywords: Edge computing, Machine learning, Computer vision, Sensor fusion, Vehicle-to-infrastructure (V2I) communication.
