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Artificial Intelligence Deployment Center of Excellence (AI COE)

rosaP Repository & Open Science Access Portal

The AI COE Collection is a central resource for transportation stakeholders that may aid in deployment of safe and responsible AI in surface transportation. The collection will feature current and emerging resources, innovations, and technologies. For more information on the AI COE, go to https://highways.dot.gov/ai-coe.

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Showing 41 - 60 of 613 results
  • CAV Pilot Development and Deployment in Midwest Winter
    CAV Pilot Development and Deployment in Midwest Winter
    Zhou, Yupeng ;
    Cui, Can
    2026-01-01 | PDF
    This project addresses the critical challenges of deploying Connected and Automated Vehicles (CAVs) in adverse Midwest winter conditions by developing a novel hierarchical framework that integrates Large Vision-Language Models for robust, context-aware decision-making. Real-world pilot experiments demonstrate that this architecture significantly en ...
  • Learning Transportation Insecurity from Location Intelligence Data
    Learning Transportation Insecurity from Location Intelligence Data
    Nie, Yu (Marco) ;
    Chen, Ying
    2026-01-01 | PDF
    Transportation insecurity (TI)- a condition in which one is unable to regularly move from place to place in a safe or timely manner because one lacks the material, economic, or social resources necessary for transportation-remains a persistent barrier to economic opportunity and quality of life in the United States. While the Transportation Securit ...
  • Creation of an Allweather Road Impact Prediction System ARIPS Summary
    Creation of an All-weather Road Impact Prediction System (ARIPS) [Summary]
    Reeves, Heather D. ;
    Rosenow, Andrew A.
    2026-01-01 | PDF
    This project is designed to examine the utility of using the database of flood-induced road closures collected by MoDOT to develop an Artificial Intelligence/Machine Learning (AI/ML) model. This was accomplished by assembling a database of predictors. These predictors include quantitative precipitation estimates, hydrologic model output, and a seri ...
  • Creation of an Allweather Road Impact Prediction System ARIPS
    Creation of an All-weather Road Impact Prediction System (ARIPS)
    Reeves, Heather D. ;
    Rosenow, Andrew A.
    2026-01-01 | PDF
    This project is designed to examine the utility of using the database of flood-induced road closures collected by MoDOT to develop an Artificial Intelligence/Machine Learning (AI/ML) model. This was accomplished by assembling a database of predictors. These predictors include quantitative precipitation estimates, hydrologic model output, and a seri ...
  • Developing a Pedestrian Safety Performance Function for Oregon
    Developing a Pedestrian Safety Performance Function for Oregon
    Roll, Josh ;
    Anderson, Jason
    2026-01-01 | PDF
    This report details the development of a pedestrian safety performance function (SPF) for intersections in urban areas in the state of Oregon. The report documents the process to develop SPFs for pedestrian crashes including the process to develop the data for analysis. A key innovation of this work is the use of pedestrian traffic volume estimates ...
  • Learning From RealWorld Trajectories A Hybrid Genetic Algorithm and Reinforcement Learning Approach for Vulnerable Road User Model Calibration brochure
    Learning From Real-World Trajectories: A Hybrid Genetic Algorithm and Reinforcement Learning Approach for Vulnerable Road User Model Calibration [brochure]
    United States. Department of Transportation. Federal Highway Administration
    2026-01-01 | PDF
    Microscopic simulation models require accurate calibration to reflect real-world dynamics. Vulnerable road user (VRU) model calibration is challenging due to: Complex interactions and behaviors, Mode-specific interactions, Diversity in infrastructure and the existence of shared spaces. Existing calibration methods, relying solely on numeric loss fu ...
  • Further Refinement and Integrated Platform for INDOT Traffic Management and Safety Toolset
    Further Refinement and Integrated Platform for INDOT Traffic Management and Safety Toolset
    Chien, Stanley ;
    Hu, Shu
    2025-12-05 | PDF
    The project aims to further refine the previously developed Anomaly Detection and Weaving Analysis programs and create a unified, Web-based user interface for users to access TASI-developed software tools conveniently. 1. The INDOT Application Suite has been successfully developed for centralizing the digital platform developed by Purdue University ...
  • MultiSource Comprehensive Program to Assess Monitor and Report Retroreflectivity of Pavement Markings
    Multi-Source Comprehensive Program to Assess, Monitor, and Report Retroreflectivity of Pavement Markings
    Mohammadi, Abbas ;
    Medina, Juan C.
    2025-12-01 | PDF
    This research investigates the feasibility of estimating retroreflectivity of durable longitudinal pavement markings using multi-source data to reduce UDOT's reliance on costly specialized field inspections. The study uses a combination of existing datasets, mobile LiDAR point clouds, street-level roadway imagery, and historical retroreflectivity m ...
  • Connected Vehicle Winter Safety Improvement With Infrared Thermography Technology
    Connected Vehicle Winter Safety Improvement With Infrared Thermography Technology
    Zhu, Xuan ;
    Yang, Xianfeng (Terry)
    2025-12-01 | PDF
    The primary objective of this research is to utilize dual-spectrum cameras, optical, and infrared thermography (IRT) cameras, to detect slippery road spots and high-crash-risk locations in winter seasons, using field evaluations and machine learning to improve detection algorithms. This capability can be used to detect road surface snow/ice pattern ...
  • Hybrid ClassicalQuantum AI Approach for Detecting Cyberattacks in Vehicles
    Hybrid Classical-Quantum AI Approach for Detecting Cyberattacks in Vehicles
    Li, Shaozhi ;
    Tewari, Sumanta
    2025-12-01 | PDF
    Machine learning has become central to self-driving vehicles, enabling tasks such as object detection and anomaly recognition. Yet classical methods often face challenges of high complexity, large parameter counts, and limited training efficiency. To address these, we propose quantum and quantum-inspired machine learning approaches that enhance bot ...
  • SafeMTS Report Applying Large Language Models to Maritime NearMiss Safety Data Analysis
    SafeMTS Report: Applying Large Language Models to Maritime Near-Miss Safety Data Analysis
    United States. Department of Transportation. Bureau of Transportation Statistics
    2025-12-01 | PDF
    This report describes the methods, models and results of the application of large language models to maritime near-miss safety data to increase analytical efficiency and accuracy among diverse industry data within the SafeMTS (Safe Maritime Transportation System) program.
  • Optimizing the Planning of Precast Concrete Bridge Construction Methods to Maximize Durability Safety and Sustainability
    Optimizing the Planning of Precast Concrete Bridge Construction Methods to Maximize Durability, Safety, and Sustainability
    El-Rayes, Khaled ;
    Ignacio, Ernest-John
    2025-12-01 | PDF
    The main goal of this project is to provide DOT planners with much-needed support that enables them to analyze and compare the performance of alternative bridge construction methods during the early design phase and optimize their construction decisions during the preconstruction phase. To accomplish this goal, the main tasks of this project focuse ...
  • Develop a Methodology for Pavement Drainage System Rating
    Develop a Methodology for Pavement Drainage System Rating
    Chen, Qiming ;
    Liu, Jun
    2025-12-01 | PDF
    Effective drainage is critical for preserving pavement integrity and extending service life, yet network-level methods for evaluating pavement drainage conditions remain limited. This study presents a practical methodology for assessing pavement drainage conditions using data from the Louisiana Department of Transportation and Development's (DOTD) ...
  • PrivacyPreserving Machine Learning Models for Traffic Forecasting
    Privacy-Preserving Machine Learning Models for Traffic Forecasting
    Mahmoud, Mahmoud N.
    ORCID ICON IMG
    2025-11-17 | PDF
    This report presents a comprehensive investigation into privacy-preserving traffic management, explainable artificial intelligence for autonomous systems, and cybersecurity in AV control. The research addresses critical challenges facing Intelligent Transportation Systems (ITS) and autonomous vehicles through four interconnected contributions. We i ...
  • Assessing Risks and Impacts of AI ARIA ARIA 01 Pilot Evaluation Report
    Assessing Risks and Impacts of AI (ARIA): ARIA 0.1: Pilot Evaluation Report
    Amironesei, Razvan ;
    Godil, Afzal
    2025-11-13 | PDF
    This document describes the procedure used for a pilot of NIST's Assessing Risks and Impacts of AI (ARIA) evaluation: ARIA 0.1. Five organizations participated, submitting a total of 7 AI applications to be evaluated. In this document, we first describe the design of the three evaluation scenarios (TV Spoilers, Meal Planner, Pathfinder) and the thr ...
  • Annotated Bridge Inspection Imagery and Videos From AI-Powered Defect Detection in Kansas Bridges [Dataset]
    Cui, Qingbin
    2025-11-03 | JSON
    This dataset contains annotated images and videos generated from drone-collected bridge inspection imagery used in AI-based defect detection research across four Kansas counties. The files include visual outputs from YOLOv11 instance segmentation models trained to identify and outline eight types of concrete surface defects: Crack, ACrack, Efflores ...
    Dataset Supporting Files
  • Investigating the Aging Behavior of Asphalt Binders at Different Production Stages and During the Service Life of the Pavement
    Investigating the Aging Behavior of Asphalt Binders at Different Production Stages and During the Service Life of the Pavement
    Elkashef, Mohamed ;
    Mishra, Debakanta (Deb)
    2025-11-01 | PDF
    This study examines how asphalt mixtures change over time and how their performance is influenced by material selection and environmental factors. Thirty-six asphalt mixes produced in Oklahoma were collected and tested to understand their behavior in terms of cracking, rutting, moisture damage, and compaction. The work focuses on how aging affects ...
  • Modeling Travel Time Reliability for NonInterstate National Highway System Routes
    Modeling Travel Time Reliability for Non-Interstate National Highway System Routes
    Zhao, Mo ;
    Lan, Chien-Lun
    2025-11-01 | PDF
    Under the Moving Ahead for Progress in the 21st Century Act, state departments of transportation are required to report travel time reliability and set performance targets for interstate and non-interstate National Highway System facilities. Although several Virginia Transportation Research Council studies have analyzed and predicted travel time re ...
  • Artificial Intelligence and Mobile PhoneBased Pavement Marking Condition Assessment and Litter Identification
    Artificial Intelligence and Mobile Phone-Based Pavement Marking Condition Assessment and Litter Identification
    Kuang, Biao ;
    Chen, Jianli
    2025-11-01 | PDF
    Regular inspection of transportation assets is essential to ensure pavement markings and pavements remain in good, clean, and safe condition. Our previous MPC-funded project (Report No. MPC-668) demonstrated the strong potential of using artificial intelligence (AI) and mobile phone imagery to identify various transportation assets. However, that i ...
  • Artificial Intelligence and Mobile PhoneBased Pavement Marking Condition Assessment and Litter Identification Research Brief
    Artificial Intelligence and Mobile Phone-Based Pavement Marking Condition Assessment and Litter Identification [Research Brief]
    Chen, Jianli ;
    Kuang, Biao
    2025-11-01 | PDF
    This research developed advanced AI-based methods for automated inspection of transportation assets, focusing on pavement markings and roadside litter. The methodology involved expanding and annotating two large-scale image datasets, each containing more than 6,000 self-collected images. Faded pavement markings were classified into white and yellow ...
41 - 60 / 613
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