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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 301 - 320 of 613 results
  • Deep Learning Machine Learning or Statistical Models for WeatherRelated Crash Severity Prediction Research Brief
    Deep Learning, Machine Learning, or Statistical Models for Weather-Related Crash Severity Prediction [Research Brief]
    Ogungbire, Abimbola ;
    Kalambay, Panick
    2023-12-01 | PDF
    In the realm of traffic safety, weather-related road traffic crashes pose a significant public health concern, leading to numerous injuries and fatalities. Past research has explored crash severity prediction using statistical machine learning (ML) and deep learning (DL)models, each with its strengths and limitations. This study strategically selec ...
  • Field Implementation of Concrete Strength Sensor to Determine Optimal Traffic Opening Time
    Field Implementation of Concrete Strength Sensor to Determine Optimal Traffic Opening Time
    Kong, Zhihao ;
    Lu, Na (Luna)
    2023-12-01 | PDF
    In the fast-paced and time-sensitive fields of construction and concrete production, real-time monitoring of concrete strength is crucial. Traditional testing methods, such as hydraulic compression (ASTM C 39) and maturity methods (ASTM C 1074), are often laborious and challenging to implement on-site. Building on prior research (SPR-4210 and SPR-4 ...
  • CorridorWide Surveillance Using Unmanned Aircraft Systems Policy Brief
    Corridor-Wide Surveillance Using Unmanned Aircraft Systems [Policy Brief]
    Zhang, Yu ;
    Kourtellis, Achilleas
    2023-12-01 | PDF
    The main objective was to provide the outcome in traffic engineering terms that can be used by the TMC, the team needed to translate the traffic congestion recorded into Level of Service (LOS), and traffic density, or occupancy. The team enhanced the algorithm developed in Phase I by following a specific framework and pieces of algorithm to perform ...
  • CorridorWide Surveillance Using Unmanned Aircraft Systems
    Corridor-Wide Surveillance Using Unmanned Aircraft Systems
    Zhang, Yu ;
    Kourtellis, Achilleas
    2023-12-01 | PDF
    In the second phase of the project, the research team continued traffic data collection with unmanned aerial systems (UAS) and dual sensors (RGB and thermal cameras) for roadways under normal operational conditions and during incident clearance. The research team conducted a thorough literature review of incident detection methods. Based on the rev ...
  • Deep Learning, Machine Learning, or Statistical Models for Weather-Related Crash Severity Prediction [supporting dataset]
    Ogungbire, Abimbola ;
    Kalambay, Panick
    2023-12-01 | JSON
    Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under ...
    Dataset
  • Enhancing Automated Vehicle Safety Through Testing with Realistic Driver Models
    Enhancing Automated Vehicle Safety Through Testing with Realistic Driver Models
    Garcia, Alfredo ;
    McDonald, Anthony D
    2023-12-01 | PDF
    Driver process models play a central role in the testing, verification, and development of automated and autonomous vehicle technologies. Prior models developed from control theory and physics-based rules are limited in automated vehicle applications due to their restricted behavioral repertoire. Data-driven machine learning models are more capable ...
  • Development of an Infrastructure Based Data Acquisition System to Naturalistically Collect the Roadway Environment
    Development of an Infrastructure Based Data Acquisition System to Naturalistically Collect the Roadway Environment
    Sarkar, Abhijit ;
    Papakis, Ioannis
    2023-12-01 | PDF
    Automatic traffic monitoring is becoming an important investment for transportation specialists, especially as the overall volume of traffic continues to increase, as do crashes at intersections. Infrastructure cameras can be a good source of information for automatic monitoring of traffic situations at intersections. However, efficient computer vi ...
  • Intelligent Safety Assessment of Rural Roadways Using Automated Image and Video Analysis Research Brief
    Intelligent Safety Assessment of Rural Roadways Using Automated Image and Video Analysis [Research Brief]
    Markovic, Nikola ;
    Rashidi, Abbas
    2023-12-01 | PDF
    This project introduces an innovative solution employing computer vision and machine learning algorithms to automate the roadside safety evaluation process. Utilizing pre-trained models such as VGG16 and images captured from Utah roadways, the research team develops a robust algorithm for automated safety evaluation that aligns with the FHWA rating ...
  • Review of Illinois Multiple Stress Creep and Recovery Data for Future Implementation
    Review of Illinois Multiple Stress Creep and Recovery Data for Future Implementation
    Hajj, Ramez ;
    Asadi, Babak
    2023-12-01 | PDF
    The multiple stress creep recovery (MSCR) test, per AASHTO M 332, or as a supplement to M 320, has been widely implemented across the United States and in other parts of the world for assessing the rutting resistance of asphalt binder. Meanwhile, the Illinois Department of Transportation (IDOT) has conducted wide-ranging MSCR testing since 2006 but ...
  • UASAssisted Inspection of Bridges for Corrosion Effects
    UAS-Assisted Inspection of Bridges for Corrosion Effects
    Tien, Iris ;
    Herndon, Hana
    2023-12-01 | PDF
    New technologies, such as unmanned aerial vehicles (UAVs) and machine learning, have been used to conduct imagery-based bridge inspections and evaluate damage on bridges. However, corrosion detection is still an open problem, and corrosion detection algorithms have only proven adequate in certain environments and conditions. The main goal of this p ...
  • Influence of Pavement Conditions on Commercial Motor Vehicle Crashes [supporting dataset]
    Arhin, Stephen ;
    Manandhar, Babin
    2023-12-01 | JSON
    Commercial motor vehicle (CMV) safety is a major concern in the United States, including the District of Columbia (DC), where CMVs make up 15% of traffic. This research uses a comprehensive approach, combining statistical analysis and machine learning techniques, to investigate the impact of road pavement conditions on CMV accidents. The study inte ...
    Dataset
  • Intelligent Safety Assessment of Rural Roadways Using Automated Image and Video Analysis
    Intelligent Safety Assessment of Rural Roadways Using Automated Image and Video Analysis
    Mashhadi, Ali Hassandokht ;
    Markovic, Nikola
    2023-12-01 | PDF
    Roadside safety is a critical aspect of transportation management, with elements like rigid obstacles, guardrails, clear zones, and side slopes significantly impacting accident outcomes. The Federal Highway Administration (FHWA) provides a valuable rating system for Department of Transportation (DOTs), but the manual rating process is time-consumin ...
  • Realtime Risk Prediction at Signalized Intersections Using a Graph Neural Network Report
    Real-time Risk Prediction at Signalized Intersections Using a Graph Neural Network [Report]
    Sonth, Akash ;
    Sarkar, Abhijit
    2023-12-01 | PDF
    Intersection-related traffic crashes and fatalities are major concerns for road safety. This project aimed to understand the major causes of conflicts at intersections by studying the intricate interplay between roadway agents. The approach involved using the current traffic camera systems to automatically process traffic video data. As manual anno ...
  • Rapid Safety Assessment Tool for NonConventional Roadway Designs and Emerging Technologies Innovative Artificial Intelligence Application
    Rapid Safety Assessment Tool for Non-Conventional Roadway Designs and Emerging Technologies: Innovative Artificial Intelligence Application
    Ahmed, Mohamed ;
    Mohamed, Ahmed
    2023-12-01 | PDF
    Traditional safety assessment methodologies are profoundly dependent on reactive crash data. The Highway Safety Manual (HSM) approach recommends 3-5 worth of crash data before and after the implementation of safety countermeasures. Waiting for such a long period of time might not be feasible to address safety issues at various roadway facilities. F ...
  • The Use of Artificial Intelligence in Pavement Engineering [supporting dataset]
    Kassem, Emad
    ORCID ICON IMG
    2023-11-30 | JSON
    The performance of asphalt pavements decreases with time because of traffic loading and environmental conditions. Performance decay models are needed in pavement management systems to program pavement preservation and rehabilitation treatments to extend the service life and improve the performance of flexible pavements. Many factors affect pavement ...
    Dataset
  • AIBased Bridge and Road Inspection Framework Using Drones
    AI-Based Bridge and Road Inspection Framework Using Drones
    Kulhandjian, Hovannes
    ORCID ICON IMG
    2023-11-01 | PDF
    There are over 590,000 bridges dispersed across the roadway network that stretches across the United States alone. Each bridge with a length of 20 feet or greater must be inspected at least once every 24 months, according to the Federal Highway Act (FHWA) of 1968. This research developed an artificial intelligence (AI)-based framework for bridge an ...
  • AIBased Bridge and Road Inspection Framework Using Drones Research Brief
    AI-Based Bridge and Road Inspection Framework Using Drones [Research Brief]
    Kulhandjian, Hovannes
    ORCID ICON IMG
    2023-11-01 | PDF
    There are over 590,000 bridges dispersed across the roadway network that stretches across the United States alone. Each bridge with a length of 20 feet or greater must be inspected at least once every 24 months, according to the Federal Highway Act (FHWA) of 1968. This research developed an artificial intelligence (AI)-based framework for bridge an ...
  • Autonomous Aircraft Challenges and Opportunities Brief
    Autonomous Aircraft: Challenges and Opportunities [Brief]
    Bridgelall, Raj ;
    Tolliver, Denver
    2023-11-01 | PDF
    The emerging field of Advanced Air Mobility (AAM) presents myriad opportunities for disrupting traditional modes of transport, including passenger travel to cargo logistics. However, its path to full-scale adoption is fraught with regulatory, market, and logistical challenges. This report presents a nuanced understanding of AAM's complexities and i ...
  • Evaluating Different Methods for Estimating Queue Length on Access Ramps
    Evaluating Different Methods for Estimating Queue Length on Access Ramps
    Tiwari, Sushant ;
    Rashidi, Abbas
    2023-11-01 | PDF
    Understanding the queue length and queuing time of ramp is important for transportation agencies to manage and operate the ramps with optimum performance. Since these data are collected with conventional sensors system such as coil, which are prone to error specially during the congestion. The increased deployment of cameras and recent advancements ...
  • Evaluating Different Methods for Estimating Queue Length on Access Ramps Research Brief
    Evaluating Different Methods for Estimating Queue Length on Access Ramps [Research Brief]
    Markovic, Nikola ;
    Rashidi, Abbas
    2023-11-01 | PDF
    Researchers used surveillance camera videos from four locations along Interstate Highway 15 in Utah to develop and evaluate the framework for using object detection and tracking algorithms. Researchers developed the framework by combining object detection and tracking to extract data from the access ramps. The framework uses the videos as input to ...
301 - 320 / 613
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