Autonomous Driving and Traffic Dynamics in Road Transportation
Modeling, Simulation, and Control
- 1st Edition - April 1, 2026
- Latest edition
- Authors: Michail Makridis, Yifan Zhang
- Language: English
Autonomous Driving and Traffic Dynamics in Road Transportation: Modeling, Simulation, and Control explores the transformative effects of autonomous vehicles (AVs) on road transp… Read more
Autonomous Driving and Traffic Dynamics in Road Transportation: Modeling, Simulation, and Control explores the transformative effects of autonomous vehicles (AVs) on road transportation, emphasizing that AVs introduce unique challenges and opportunities distinct from conventional vehicles. The book highlights the risks of oversimplification in modeling AVs and underscores the necessity for nuanced, AI-driven approaches to understanding traffic dynamics. Through an in-depth discussion, it examines how AVs affect congestion, safety, energy use, and their pivotal role in shaping intelligent, sustainable transportation systems for the future. In addition to foundational concepts, the book compares traditional and AI-based car-following models, assessing their strengths and weaknesses.
The book integrates human and autonomous driving behaviors into traffic flow theories and evaluates their collective impact on traffic patterns, efficiency, and safety. The text also delves into real-world applications of AI and hybrid modeling techniques for traffic management, offering actionable insights for researchers, engineers, and policymakers seeking to advance the state of traffic estimation and control in an era of increasing automation.
The book integrates human and autonomous driving behaviors into traffic flow theories and evaluates their collective impact on traffic patterns, efficiency, and safety. The text also delves into real-world applications of AI and hybrid modeling techniques for traffic management, offering actionable insights for researchers, engineers, and policymakers seeking to advance the state of traffic estimation and control in an era of increasing automation.
- 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
University students and Researchers at MSc, Ph.D. and post-doc levels; experts who work on sustainable transport and emerging vehicle technologies; practitioners who run simulations, consultants who generate reports on autonomous vehicles
1. Human drivers and traffic dynamics in road transport
2. Autonomous driving systems and how they operate
3. Humans, autonomous vehicles and traffic flow
4. Data observations and the role of AI in traffic flow modeling
5. Traffic management and traffic control with AD
2. Autonomous driving systems and how they operate
3. Humans, autonomous vehicles and traffic flow
4. Data observations and the role of AI in traffic flow modeling
5. Traffic management and traffic control with AD
- Edition: 1
- Latest edition
- Published: April 1, 2026
- Language: English
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Michail Makridis
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.
Affiliations and expertise
Deputy Director, Traffic Engineering and Control group, ETH Zürich, SwitzerlandYZ
Yifan Zhang
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.
Affiliations and expertise
Assistant Professor, Department of Computer Science, City University of Hong Kong (Dongguan), China