Autonomous Driving and Traffic Dynamics in Road Transportation

Autonomous Driving and Traffic Dynamics in Road Transportation: Modeling, Simulation, and Control discusses the introduction of autonomous vehicles (AV) on road transport systems and the similarities and differences with human drivers, focusing on key concepts in traffic dynamics and AI-based modeling. Simply treating AVs as conventional vehicles with slightly altered characteristics can lead to inaccurate conclusions, posing risks for researchers, engineers, and policymakers alike. This book addresses these challenges by offering a comprehensive discussion of the unique dynamics introduced by AVs and their impact on congestion, safety, energy, and their role in sustainable future intelligent transportation systems. Part I delves into traditional driving behaviors, examining the basics of car-following, driver characteristics, lateral movement, and how well AI models generalize these behaviors. Part II shifts to autonomous driving systems, analyzing their operational principles and providing comparative evidence with human drivers. Additionally, it assesses the performance of traditional car-following models against artificial intelligence developments highlighting strengths and weaknesses for each approach. Part III integrates human drivers and AVs into broader traffic flow theories, presenting findings on how autonomous driving impacts traffic patterns. It studies the impact from the perspective of traffic dynamics, energy efficiency and safety. Part IV looks at the role of AI and modeling, exploring the pros and cons of various methods and data sources. It emphasizes the need for physics-informed models to improve policy decisions and technical performance. Part V discusses real-world traffic management applications, combining AI and traditional models for traffic estimation, control, and ensuring fair, disruption-resilient outcomes. The book serves as a detailed guide for researchers, engineers, and policymakers who need to understand why there is a need to pay attention on the driving style of autonomous vehicles, where we should use analytical models and where data-driven approaches (or physics-informed ones), see the big picture, and learn about the state of the art in traffic state estimation and traffic management domains with the presence of autonomous vehicles.

• 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
• Each chapter provides definitions of key terms, methods, applications and case studies, reviews and latest research, and future implications