Advancements in connected and automated vehicle (CAV) technologies present new challenges for Traffic Incident Management (TIM) and emergency responders (ERs), particularly due to limited information sharing among stakeholders. NCHRP Research Report 1104 offers essential resources and communication tools to enhance collaboration and understanding between CAV developers, TIM personnel, and ERs. The report equips responders and those in the TIM community with strategies and insights to effectively engage with the evolving CAV landscape.

NCHRP Research Report 1121 distills practical tools, methods, and guidelines that help transportation agencies modernize data integration, sharing, and management for planning and traffic operations. Grounded in real-world use cases—integrated corridor management, smart-city initiatives, incident response, performance management, work-zone oversight, and more—the report captures best practices and lessons learned while showcasing proof-of-concept products that tackle persistent obstacles such as incompatible data standards, data-quality gaps, and concerns over proprietary or sensitive information. Deployment assistance examples illustrate how well-designed data architectures foster collaboration among public agencies, private partners, travelers, and connected devices, leading to more informed, data-driven decisions. A companion website hosted by the National Operations Center of Excellence (https://data.transportationops.org) compiles the full set of best practices and supplemental resources.

Over the past two decades, machine learning—the leading branch of artificial intelligence—has risen rapidly on the strength of exploding data volumes, cheaper storage and computation, and continual algorithmic breakthroughs. Deep-learning techniques and generative AI systems such as ChatGPT are now transforming day-to-day business practices. Yet state and local transportation agencies are drowning in new data streams faster than they can translate them into actionable insight. TRB’s NCHRP Research Report 1122, *Implementing Machine Learning at State Departments of Transportation: A Guide*, equips DOTs with a primer on promising ML use cases, tools for weighing costs, benefits, risks, and constraints, and a roadmap for fostering the data-driven culture needed to scale these capabilities across transportation programs.