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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 201 - 220 of 613 results
  • Studying the Use of LowCost Sensing Devices to Report Roadway Pavement Conditions Brief
    Studying the Use of Low-Cost Sensing Devices to Report Roadway Pavement Conditions [Brief]
    Abdallah, Moatassem ;
    Clevenger, Caroline M.
    2024-09-01 | PDF
    This report investigates the application of low-cost sensing technologies, including GPS, accelerometers, and smartphones, to monitor roadway pavement conditions in real time. By leveraging widely available sensors embedded in vehicles, this research demonstrates how machine learning models can detect and classify road anomalies, such as cracks and ...
  • Traffic Light Prediction and Trajectory Planning for Congestion Reduction [supporting dataset]
    Jiang, Zhong-Ping ;
    Ozbay, Kaan
    2024-09-01 | md
    Based on the traffic light prediction and trajectory optimization techniques for a stream of CAVs, the proposed congestion-reducing scheme can increase the throughput of the transportation network by attenuating the deceleration and acceleration of vehicles before the signalized intersections, accompanied by decreased fuel consumption and emissions ...
    Dataset Supporting Files
  • SUMO Simulation, Validation Using Real-World Trajectory Data [supporting datasets]
    Jiang, Zhong-Ping ;
    Ozbay, Kaan
    2024-09-01 | ZIP
    Based on the traffic light prediction and trajectory optimization techniques for a stream of CAVs, the proposed congestion-reducing scheme can increase the throughput of the transportation network by attenuating the deceleration and acceleration of vehicles before the signalized intersections, accompanied by decreased fuel consumption and emissions ...
    Dataset Supporting Files
  • 07139 Determination of Pavement Surface Type
    0-7139: Determination of Pavement Surface Type
    Prozzi, Jorge A. ;
    Sabillon, Christian
    2024-08-31 | PDF
    This study evaluated and developed machine learning models for pavement surface classification to enhance accuracy and reliability using advanced computational techniques. Initially, a detailed literature review on pavement texture and measurement methods provided a foundation for developing a prototype system. This system, integrating laser scanne ...
  • Enabling GLOSA through Domain Knowledge Aware SPAT Prediction and Queue Length Aware Trajectory Optimization
    Enabling GLOSA through Domain Knowledge Aware SPAT Prediction and Queue Length Aware Trajectory Optimization
    Shafik, Amr ;
    Eteifa, Seifeldeen
    2024-08-29 | PDF
    This research presents a novel approach to enhancing Green Light Optimal Speed Advisory (GLOSA) systems by predicting signal phase and timing (SPaT) switching times and assessing the confidence in these predictions. The proposed architecture leverages transformer encoders, combined with various deep learning methods such as Multilayer Perceptrons ( ...
  • Enabling GLOSA through Domain Knowledge Aware SPAT Prediction and Queue Length Aware Trajectory Optimization Factsheet
    Enabling GLOSA through Domain Knowledge Aware SPAT Prediction and Queue Length Aware Trajectory Optimization [Factsheet]
    Rakha, Hesham A. ;
    Shafik, Amr
    2024-08-29 | PDF
    This research presents a novel approach to enhancing Green Light Optimal Speed Advisory (GLOSA) systems by predicting signal phase and timing (SPaT) switching times and assessing the confidence in these predictions. The proposed architecture leverages transformer encoders, combined with various deep learning methods such as Multilayer Perceptrons ( ...
  • Energy and Emissions Impacts of Atlanta's Reversible Express Toll Lanes and High-Occupancy Toll Lanes [supporting dataset]
    Lu, Hongyu ;
    Guin, Angshuman
    2024-08-28 | JSON
    This report summarizes the impact on corridor-level energy use and emissions associated with the 2018 opening of the I-75 Northwest Corridor (NWC) and I-85 Express Lanes in Atlanta, GA. The research team tracked changes in vehicle throughput on the managed lane corridors (extracted from GDOT's Georgia NaviGAtor machine vision system after comprehen ...
    Dataset
  • Shippers Behavior Study Through Developing and Calibrating Their Utility Functions
    Shippers' Behavior Study Through Developing and Calibrating Their Utility Functions
    Han, Lee ;
    Wang, Bruce
    2024-08-15 | PDF
    This study addresses the lack of granularity in traditional freight demand models by developing a comprehensive survey to collect disaggregated data on freight movements and the underlying logistics decisions made by shippers. The primary objective was to enhance the precision of freight demand forecasts and gain a deeper understanding of the compl ...
  • Developing a Portable Railroad Crossing Monitoring System Based on Artificial Intelligence and Image Processing Technology
    Developing a Portable Railroad Crossing Monitoring System Based on Artificial Intelligence and Image Processing Technology
    Qian, Yu ;
    Chen, Yuche
    2024-08-01 | PDF
    This project addresses the urgent need for improved railroad grade crossing safety, where traditional systems often fail to detect and manage unforeseen hazards such as vehicles or pedestrians obstructing tracks. This project develops a cost-effective, portable system for real-time identification, counting, and categorization of objects at railroad ...
  • Evaluation of Driver Behavior Influenced by Traffic Safety Messages From Roadside Units in Connected and Autonomous Systems
    Evaluation of Driver Behavior Influenced by Traffic Safety Messages From Roadside Units in Connected and Autonomous Systems
    Jeihani, Mansoureh ;
    Yang, Di
    2024-08-01 | PDF
    The safety of vulnerable road users (VRUs) at signalized intersections is a significant concern in urban traffic management. This study aims to enhance VRUs safety through the deployment of advanced communication and machine learning technologies. Using a Connected and Autonomous Vehicle (CAV) testbed, this research explores real-time communication ...
  • Identification of Simulation Calibration Parameters Using Urban Freeway Data
    Identification of Simulation Calibration Parameters Using Urban Freeway Data
    Golias, Mihalis M. ;
    Mishra, Sabyasachee
    2024-08-01 | PDF
    The reliability of a microsimulation model such as VISSIM depends on proper calibration and validation to accurately represent real-world traffic conditions. However, the VISSIM default values do not apply to local traffic conditions and need to be calibrated for local traffic conditions considering the higher number of car-following parameters. He ...
  • ContextVLM ZeroShot and FewShot Context Understanding for Autonomous Driving using Vision Language Models
    ContextVLM: Zero-Shot and Few-Shot Context Understanding for Autonomous Driving using Vision Language Models
    Sural, Shounak ;
    Ramakrishnan, Naren
    2024-08-01 | PDF
    In recent years, there has been a notable increase in the development of autonomous vehicle (AV) technologies aimed at improving safety in transportation systems. While AVs have been deployed in the real-world to some extent, a full-scale deployment requires AVs to robustly navigate through challenges like heavy rain, snow, low lighting, constructi ...
  • Evaluating Autonomous Vehicles Safety Benefits in Mixed Autonomy Scenarios
    Evaluating Autonomous Vehicles' Safety Benefits in Mixed Autonomy Scenarios
    Joe-Wong, Carlee ;
    Yağan, Osman
    2024-08-01 | PDF
    Connected autonomous vehicles (CAVs) are gradually advancing towards widespread deployments. CAVs promise to improve transportation safety by operating more efficiently and avoiding incidents like crashes due to human driver error. However, they may cause crashes or other safety incidents themselves, especially when interacting with humans. Our wor ...
  • Development of a Monitoring System for Driver Readiness in Prolonged Automated Driving
    Development of a Monitoring System for Driver Readiness in Prolonged Automated Driving
    Deb, Shuchisnigdha ;
    Kan, Chen
    2024-08-01 | PDF
    Fatal traffic crashes have increased significantly, largely due to human error, which automated vehicle technology aims to reduce. However, challenges such as driver fatigue and the need for quick intervention in case of system failures must be urgently addressed to ensure safety. This study aims to develop a driver fatigue monitoring system to det ...
  • Optimal Selection of Upgrade and Maintenance Interventions To Minimize LifeCycle Cost
    Optimal Selection of Upgrade and Maintenance Interventions To Minimize Life-Cycle Cost
    Abdallah, Moatassem ;
    Ozbek, Mehmet E.
    2024-08-01 | PDF
    The maintenance and upgrade of infrastructure and buildings are critical for ensuring their performance, safety, and minimizing costs. However, inadequate planning and budget allocation, as well as resource constraints, often result in delayed maintenance, leading to costly interventions. To address these challenges, this study presents two novel m ...
  • Optimal Selection of Upgrade and Maintenance Interventions To Minimize LifeCycle Cost Research Brief
    Optimal Selection of Upgrade and Maintenance Interventions To Minimize Life-Cycle Cost [Research Brief]
    Abdallah, Moatassem ;
    Ozbek, Mehmet E.
    2024-08-01 | PDF
    This study presents two novel models. The first focuses on bridges and uses machine learning techniques to predict the condition of concrete bridge elements based on the National Bridge Inventory (NBI) and National Bridge Elements (NBE) databases. The model uses binary linear programming to identify the optimal selection of maintenance intervention ...
  • Snow Plow Performance Measures in NonRWIS Locations
    Snow Plow Performance Measures in Non-RWIS Locations
    Jiang, Gang ;
    Kuang, Biao
    2024-08-01 | PDF
    This project aims to enhance traffic safety by developing an Artificial Intelligence (AI) model and the weather interpolation method to evaluate snow cover conditions on road surfaces using existing roadside Closed-Circuit Television system (CCTV) images in non-Road Weather Information System (RWIS) locations. Snow- cover significantly impacts traf ...
  • Sensitivity and Accuracy Assessment of Vehicle WeighinMotion System Measurement Errors Using InPavement StrainBased Sensors Brief
    Sensitivity and Accuracy Assessment of Vehicle Weigh-in-Motion System Measurement Errors Using In-Pavement Strain-Based Sensors [Brief]
    Lu, Pan ;
    Tolliver, Denver
    2024-08-01 | PDF
    The rapid increase in traffic volume and heavy vehicle loads is causing accelerated deterioration of pavement infrastructure worldwide. This research investigates the impact of overweight and dynamic axle loads on pavement performance, employing advanced weigh-in-motion (WIM) systems and machine learning models to enhance predictive accuracy. By in ...
  • Sensitivity and Accuracy Assessment of Vehicle WeighinMotion System Measurement Errors Using InPavement StrainBased Sensors
    Sensitivity and Accuracy Assessment of Vehicle Weigh-in-Motion System Measurement Errors Using In-Pavement Strain-Based Sensors
    Lu, Pan ;
    Tolliver, Denver
    2024-08-01 | PDF
    The rapid increase in traffic volume and heavy vehicle loads is causing accelerated deterioration of pavement infrastructure worldwide. This research investigates the impact of overweight and dynamic axle loads on pavement performance, employing advanced weigh-in-motion (WIM) systems and machine learning models to enhance predictive accuracy. By in ...
  • Use of Machine Learning Methods to Obtain a Reliable Predictive Model for Resilient Modulus of Subgrade Soil
    Use of Machine Learning Methods to Obtain a Reliable Predictive Model for Resilient Modulus of Subgrade Soil
    Khoshnevisan, Sara ;
    Norouzi, Mehdi
    2024-08-01 | PDF
    This project explores the development and optimization of predictive models for the resilient modulus (MR) of subgrade soil using advanced machine learning techniques. Comprehensive data from INDOT spanning several years was analyzed to enhance the accuracy of MR predictions. The study not only refined the modeling approach through statistical meth ...
201 - 220 / 613
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