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 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 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