AASHTO Enterprise
Risk Management Portal
Browse the latest documents, videos, tools, trainings, and events for the ERM community.
Check out the Resilience Improvement Plan (RIP)Resource Collection
&
Stop by the CPBM Subcommittee on Risk Management community page
Download slides and video from the 2020 TAM MegaMeeting
We would love to hear your suggestions and feedback, so please Provide Site Feedback for anything you think can be enhanced or improved!
Connect
Connect
Sign up for monthly updates
About the site
The AASHTO ERM Portal connects you to a searchable database of enterprise risk management resources: documents, presentations, events, tools, and more. The portal is designed to help enterprise risk management practitioners search and access relevant information from multiple sources.
NCHRP Reports
NCHRP Report 1121 — Data Integration, Sharing, and Management for Transportation Planning and Traffic Operations | Research Report
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.
NCHRP Report 1122: Implementing Machine Learning at State Departments of Transportation | Research Report
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.
NCHRP Web-Only Document 404 — Implementing and Leveraging Machine Learning at State Departments of Transportation (2024) | Research Report
This is a companion document to NCHRP Report 1122: Implementing Machine Learning at State Departments of Transportation.
Tennessee DOT's Connected and Autonomous Vehicles Readiness Action Plan
Tennessee DOT’s five-year Connected and Automated Vehicle Readiness Action Plan charts how CAV technology will beco…
Colorado DOT's Resilience Improvement Plan (2024)
The Colorado Department of Transportation’s Resilience Improvement Plan (RIP) outlines the agency’s efforts to pr…
NCHRP Web-Only Document 404 — Implementing and Leveraging Machine Learning at State Departments of Transportation (2024)
This is a companion document to NCHRP Report 1122: Implementing Machine Learning at State Departments of Transportati…
Video
Tim Henkel – 2022 AASHTO Thomas H. MacDonald Memorial Award

Asset Valuation Guide
The Asset Valuation Guide helps transportation agencies compute and leverage system level valuations of their transportation assets.
See the Tool HereSearch across Documents, Tools, and Videos in the Resource Library
Collections
Browse the latest collections