Autonomous Driving and Mixed Traffic Dynamics: Modeling, Simulation, and Control provides an introduction to autonomous vehicles (AV) in road transport systems, along with discussions on the critical similarities and differences with human drivers. Focusing on key concepts in traffic dynamics and AI-based modeling, the book also offers a comprehensive discussion of the unique dynamics introduced by AVs and their impacts on congestion, safety, energy, and their role in sustainable future intelligent transportation systems. Sections delve into traditional driving behaviors, examining the basics of car-following, driver characteristics, lateral movement, and how well AI models generalize these behaviors.Further sections covers shifts to autonomous driving systems, analyzing their operational principles and providing comparative evidence with human drivers. Assessments on the performance of traditional car-following models against artificial intelligence developments that highlight strengths and weaknesses for each approach are also included. Final sections integrate human drivers and AVs into broader traffic flow theories, presenting findings on how autonomous driving impacts traffic patterns, look at the role of AI and modeling, explore the pros and cons of various methods and data sources, and discuss real-world traffic management applications, combining AI and traditional models for traffic estimation, control, and ensuring fair, disruption-resilient outcomes. - Authored by a team with years of expertise and cross-disciplinary interaction in Computer Science, Mechanical Engineering, and Traffic Engineering - Utilizes a step-by-step approach to exploring the implications of Autonomous Vehicles, beginning with foundational concepts and progressively extending to their impact on segment-level traffic dynamics, operations, and broader network level - Provides definitions of key terms, methods, applications, case studies, reviews, the latest research, and future implications

Dr. Michail A. Makridis is the Deputy Director of the Traffic Engineering and Control group at ETH Zürich, Switzerland. Previously, he led the Transport and Traffic Engineering group at the Zurich University of Applied Sciences and was the scientific lead for the Traffic Modeling Group at the Joint Research Centre (JRC) of the European Commission (EC). He holds a Ph.D. in Computer Vision from Democritus University of Thrace, Greece. His research focuses on traffic flow, management, and control for Intelligent Transportation Systems involving Connected and Automated Vehicles. His work emphasizes data-driven, physics-informed modeling and AI to enhance sustainability, traffic efficiency, antifragile operations, and equitable transport networks. In 2022, he received the JRC Annual Award for Excellence in Research from the EC. He is an Associate Editor for the IEEE Open Journal of Intelligent Transportation Systems and serves on various scientific committees.Dr. Yifan Zhang is an Assistant Professor with the Department of Computer Science at the City University of Hong Kong (Dongguan), China. She completed a PhD in Computer Science at City University of Hong Kong in Summer 2022. Her major research interests lie in autonomous driving, microscopic traffic modeling, and intelligent transportation systems. Her previous work mainly covers human driving behavior modeling, motion planning of autonomous vehicles, trajectory prediction, and traffic prediction through leveraging multiple AI models, such as conditional variational autoencoder, convolutional neural networks, transformers, neural processes, and so on. These works are published in top AI conferences and transportation journals. She is also a reviewer of several A-tier transportation journals and computer science conferences.