Artificial Intelligence in Highway Engineering: Optimizing Infrastructure and Mobility
Apr 2027 · Elsevier
Ebook
400
Pages
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infoThis book will become available on April 5, 2027. You will not be charged until it is released.
About this ebook
As transportation networks evolve in scale, complexity, and interdependence, artificial intelligence (AI) has emerged as a core enabler of engineering innovation. Artificial Intelligence in Highway Engineering: Optimizing Infrastructure and Mobility responds to this shift with a focused and technically rigorous investigation of AI-driven methods that are fundamentally redefining the design, operation, and strategic management of highway systems.The volume embraces a truly integrative perspective at the nexus of computational modeling, infrastructure analytics, and transportation engineering to tackle multifaceted, domain-specific challenges. Moving beyond theoretical discourse, it delivers a rich analysis grounded in modern-day practice of how algorithmic models interface with physical assets, the dynamic behaviors of urban environments, and real-world system-level constraints. These insights reveal AI's capacity to inform long-term infrastructure planning, enable adaptive functionalities, and guide high-stakes decisions in unpredictable operational contexts.Emphasizing practical implementation and scalability, this valuable reference resource equips academic and industry readers alike with actionable knowledge on seamlessly embedding contemporary AI architectures to boost transportation networks' performance and strengthen their reliability as well as advance smart mobility solutions. - Captures both the physical (infrastructure) and dynamic (mobility) elements of modern highway engineering to enhance environmental sustainability, resilience, and operational efficiency - Explores foundational and next-generation AI models—including Kolmogorov–Arnold Networks (KAN), Mamba, deep learning, and graph neural networks—for predictive analytics and intelligent mobility solutions - Demonstrates applied uses of AI in traffic management, crash risk assessment, connected and automated vehicle technologies, and infrastructure lifecycle optimization through practical, hands-on case studies - Introduces cutting-edge approaches such as AI-enabled digital twins, explainable AI for transparent decision-making, and agent-based simulations for long-term transportation planning - Provides fully open-source datasets and code via a GitHub repository, supporting replicability, clarity, and real-world implementation
As transportation networks evolve in scale, complexity, and interdependence, artificial intelligence (AI) has emerged as a core enabler of engineering innovation. Artificial Intelligence in Highway Engineering: Optimizing Infrastructure and Mobility responds to this shift with a focused and techn...
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About the author
Dr. Subasish Das is Assistant Professor of Civil Engineering at the Ingram School of Engineering at Texas State University. Previously, he worked as a full-time Associate Research Scientist at the Texas A&M Transportation Institute (2015-2022). He has more than 17 years of experience related to AI, roadway safety, traffic operation, and connected and automated vehicle (CAV) technologies. His major areas of expertise include database management, statistical analysis and machine learning with emphasis in safety and transportation operations, spatial analysis with modern web GIS tools, interactive data visualization, and deep learning tools for CAV technologies. Dr. Das has published more than 220 technical reports and journal articles, as well as the book Artificial Intelligence in Highway Safety, published by CRC Press in 2022. He has taught a PhD-level course on AI in Civil Engineering since 2024. He is also an active member of ITE, ASCE and three Transportation Research Board Committees.
Dr. Subasish Das is Assistant Professor of Civil Engineering at the Ingram School of Engineering at Texas State University. Previously, he worked as a full-time Associate Research Scientist at the Texas A&M Transportation Institute (2015-2022). He has more than 17 years of experience related to A...
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