The development of information technology is transforming the automotive industry, leading to the emergence of connected vehicles that integrate advanced sensing, communication, and control systems to enhance connectivity between vehicles, people, and infrastructure. Connected vehicles enable real-time data exchange, thus improving safety, efficiency, and urban traffic management. They collect and analyze traffic data to optimize routes, reduce congestion, and support smart city planning through interactions with infrastructure like intelligent signals. Traffic flow prediction, a critical area in connected vehicle research, combines vehicle networks, big data, and AI to address spatial-temporal variability and external factors such as location, weather, and events, which impact traffic patterns. This book presents five chapters, starting with foundational knowledge on AI and graph neural networks in connected vehicles, followed by specific traffic prediction methods, and concluding with a discussion on future challenges.