Document Library

NCHRP Web-Only Document 413: Applying the Safe System Approach to Transportation Planning, Design, and Operations is supplemental to NCHRP Report 1135: A Guide to Applying the Safe System Approach to Transportation Planning, Design, and Operations.

NCHRP Synthesis 652: Traffic Capacity Level of Service: Adaptations and Usage provides resources for state DOTs to evaluate and improve the LOS practices and policies

NCHRP Research Report 1135: A Guide to Applying the Safe System Approach to Transportation Planning, Design, and Operations, provides information for state departments of transportation and other transportation agencies to apply the holistic Safe System approach.

NCHRP Web-Only Document 431: Snapshots of Planning Practices, provides a series of illustrated "snapshots" as as a resource for transportation-planning practice development. This supplemental web-only document provides information for realistic practice implementation and program advancement and was published accompanying NCHRP Research Report 1158: Developing Snapshots for Transportation Planning.

NCHRP Research Report 1158: Developing Snapshots for Transportation Planning, chronicles the process of developing four Snapshots of Planning Practices. Snapshots are concise and visually appealing documents that report on current planning practices in use by transportation agencies across four practice areas: Complete Streets, Data Sharing for Performance Management, Collaboration on Local Freight Delivery, and Economic Analysis to Support Decision-Making.

This report explores the use of AI and computer vision to automate condition assessment of roadside safety assets for UDOT. Leveraging street-level imagery, the project tested object detection models (YOLO11n) and vision-language models (Gemma 3, Llama 3.2) to classify defects, rate conditions, and evaluate design compliance for primary and secondary assets. Findings highlight the challenges of defect detection, the promise of advanced models, and opportunities to improve maintenance prioritization and roadway safety.

Developed as a modular, open-source Python package with accompanying Jupyter notebook examples and hosted on GitHub, this tool provides a scalable approach to pavement condition monitoring. The research shows that temperature data collected from embedded thermocouples, when analyzed using advanced computational techniques, can serve as reliable indicators of pavement degradation, supporting more informed, data-driven infrastructure management decisions

This calendar includes meetings hosted by the AASHTO Committee on Performance Based Management and its subcommittees, as well as programs and events supported by the Transportation Performance Management Technical Service Program, including peer exchanges and monthly webinars. This calendar spans June 2025 - December 2025.

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.

This short document provides a vision/roadmap for public agencies to adopt and leverage AI-Ready federal statistical data.

This guide from TRB’s National Cooperative Highway Research Program highlights how AI, machine learning, and emerging data sources—such as connected vehicles, LiDAR, video analytics, and edge devices—can improve traditional road safety evaluations. Case studies from Oregon DOT and the City of Bellevue, WA, illustrate practical applications, including using ML to inventory streetlights for crash risk analysis and analyzing turning behavior at intersections to enhance safety.