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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 541 - 560 of 613 results
  • Efficient Lowcost Bridge Cracking Detection and Quantification Using Deeplearning and UAV Images
    Efficient, Low-cost Bridge Cracking Detection and Quantification Using Deep-learning and UAV Images
    Sun, Chao ;
    Meng, Xiangyu
    2021-09-01 | PDF
    Many bridges in the State of Louisiana and the United States are working under serious degradation conditions where cracks on bridges threaten structural integrity and public security. To ensure structural integrity and public security, it is required that bridges in the US be inspected and rated every two years. Currently, this biannual assessment ...
  • EarlyAge Properties of HighVolume Fly Ash Concrete Mixes for Pavement Volume 2
    Early-Age Properties of High-Volume Fly Ash Concrete Mixes for Pavement: Volume 2
    Baral, Aniruddha ;
    Roesler, Jeffrey R.
    2021-09-01 | PDF
    High-volume fly ash concrete (HVFAC) is more cost-efficient, sustainable, and durable than conventional concrete. This report presents a state-of-the-art review of HVFAC properties and different fly ash characterization methods. The main challenges identified for HVFAC for pavements are its early-age properties such as air entrainment, setting time ...
  • Deep Reinforcement Learning-based Project Prioritization for Rapid Post-Disaster Recovery of Transportation Infrastructure Systems [supporting datasets]
    Lee, Yong-Cheol ;
    Choi, Kunhee
    2021-09-01 | PDF
    Among various natural hazards that threaten transportation infrastructure, flooding represents a major hazard in Region 6's states to roadways as it challenges their design, operation, efficiency, and safety. The catastrophic flooding disaster event generally leads to massive obstruction of traffic, direct damage to highway/bridge structures/paveme ...
    Dataset Supporting Files
  • Efficient, Low-cost Bridge Cracking Detection and Quantification Using Deep-learning and UAV Images [supporting datasets]
    Sun, Chao ;
    Meng, Xiangyu
    2021-09-01 | PDF
    Many bridges in the State of Louisiana and the United States are working under serious degradation conditions where cracks on bridges threaten structural integrity and public security. To ensure structural integrity and public security, it is required that bridges in the US be inspected and rated every two years. Currently, this biannual assessment ...
    Dataset Supporting Files
  • Feasibility Analysis of Realtime Intersection Data Collection and Processing Using Drones
    Feasibility Analysis of Real-time Intersection Data Collection and Processing Using Drones
    Ozguven, Eren Erman ;
    Karaer, Alican
    2021-09-01 | PDF
    Traditional data collection techniques at intersections are known to be time consuming and costly while handling the complexity associated with the heavy traffic volume and travel demand on today's roadways. Therefore, transportation agencies have been searching for more innovative, safer, and cheaper data collection solutions to have a faster and ...
  • Collaborative Research Project to Coordinate the Data from the CRASH Predictive Analytics Program Between TDOT and TDOSHS
    Collaborative Research Project to Coordinate the Data from the CRASH Predictive Analytics Program Between TDOT and TDOSHS
    Baroud, Hiba ;
    Dubey, Abhishek
    2021-09-01 | PDF
    Emergency Response Management (ERM) necessitates the use of models capable of predicting the spatial-temporal likelihood of incident occurrence. These models are used for proactive stationing in order to reduce overall response time. Traditional methods simply aggregate past incidents over space and time; such approaches fail to make useful short-t ...
  • Evaluating Mobility Impacts of Construction Workzones on Utah Transportation System Using Machine Learning Techniques
    Evaluating Mobility Impacts of Construction Workzones on Utah Transportation System Using Machine Learning Techniques
    Mashhadi, Ali Hassandokht ;
    Rashidi, Abbas
    2021-09-01 | PDF
    Construction work zones are inevitable parts of daily operations at roadway systems. They have a significant impact on traffic conditions and the mobility of roadway systems. The traffic impacts of work zones could significantly vary due to several interacting factors such as work zone factors (work zone location and layout, length of the closure, ...
  • Detecting Driver Drowsiness With MultiSensor Data Fusion Combined With Machine Learning
    Detecting Driver Drowsiness With Multi-Sensor Data Fusion Combined With Machine Learning
    Kulhandjian, Hovannes
    ORCID ICON IMG
    2021-09-01 | PDF
    According to the National Highway Traffic Safety Administration, in 2017 drowsy driving resulted in 50,000 injuries across 91,000 police-reported accidents, as well as almost 800 deaths. Through the application of visual and radar sensors combined with machine learning, this research developed a drowsy driver detection system aimed to prevent poten ...
  • Travel Time Forecasting on a Freeway Corridor a Dynamic Information Fusion Model based on the Random Forests Approach
    Travel Time Forecasting on a Freeway Corridor: a Dynamic Information Fusion Model based on the Random Forests Approach
    Fan, Wei (David) ;
    Qiu, Bo
    2021-09-01 | PDF
    Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been employed in travel time prediction; however, such machine learning methods practically face the problem of overfitting. Tree based ensembles have been applied in various prediction ...
  • Machine Learningbased Trajectory Optimization of Connected and Autonomous Vehicles
    Machine Learning-based Trajectory Optimization of Connected and Autonomous Vehicles
    Fan, Wei (David) ;
    Liu, Pengfei
    2021-09-01 | PDF
    Connected and autonomous vehicle (CAV) technologies provide solutions to the existing problems of the transportation systems. As widely known, CAVs can communicate with each other so that they can have coordinated accelerating or decelerating movements. In this manner, CAVs only need a smaller headway which will lead to a higher roadway capacity. F ...
  • Guidelines for Obtaining AADT Estimates from NonTraditional Sources
    Guidelines for Obtaining AADT Estimates from Non-Traditional Sources
    Schewel, Laura ;
    Co, Sean
    2021-09-01 | PDF
    The use of passive data from location-based smartphone applications (LBS) and Global Positioning Services (GPS) to collect Annual Average Daily Traffic (AADT) has the potential to greatly reduce costs to State Department of Transportations (DOTs) and Metropolitan Planning Organizations (MPOs) and expand the coverage of up-to-date counts. This repor ...
  • NonTraditional Methods to Obtain Annual Average Daily Traffic AADT
    Non-Traditional Methods to Obtain Annual Average Daily Traffic (AADT)
    Schewel, Laura ;
    Co, Sean
    2021-09-01 | PDF
    The use of passive data from location-based smartphone applications (LBS) and Global Positioning Services (GPS) to collect Annual Average Daily Traffic (AADT) has the potential to greatly reduce costs to State Department of Transportations (DOTs) and Metropolitan Planning Organizations (MPOs) and expand the coverage of up-to-date counts. This repor ...
  • Largescale and Longterm Forecasting of Performance Measurement of Public Transportation Systems Research Brief
    Large-scale and Long-term Forecasting of Performance Measurement of Public Transportation Systems [Research Brief]
    Shahabi, Cyrus
    ORCID ICON IMG
    2021-09-01 | PDF
    The project objective is to build a machine learning approach and a system to achieve accurate estimations of public bus arrival times by forecasting long-term traffic flows for the entire Los Angeles Metropolitan Area (LAMA).
  • Freight Volume Modeling on Major Highway Links
    Freight Volume Modeling on Major Highway Links
    Anastasiou, Chrysovalantis ;
    Kim, Seon Ho
    2021-08-02 | PDF
    With this research the authors would like to validate the feasibility of freight volume estimation on major highways links from accurate but sparse sensor data, by studying a restricted control area covering approximately 12 square miles around the Ports of Los Angeles and Long Beach where freight volume is most relevant. The authors researched cre ...
  • An Automated System for Inspecting Rock Faces and Detecting Potential Rock Falls Using Machine Learning [supporting datasets]
    Moreu, Fernando ;
    Stormont, John
    2021-08-01 | PDF
    Rockfall is a hazard in mountainous areas threatening infrastructure and human lives. Rockfall hazards are often mitigated by manual inspections using pry bars. The inspector must access the rock face, hit the rock surface, detect, and remove the loose rocks. This method is very labor demanding, unsafe, and challenging. This research presents a met ...
    Dataset Supporting Files
  • Cooperative Perception of Roadside Unit and Onboard Equipment with Edge Artificial Intelligence for Driving Assistance
    Cooperative Perception of Roadside Unit and Onboard Equipment with Edge Artificial Intelligence for Driving Assistance
    Wang, Yinhai ;
    Sun, Wei
    2021-08-01 | PDF
    Recently, 2D detection in images has made significant progress owing to the emergence of a convolutional neural network (CNN), which can extract high-level features from the images. However, detecting objects in 3D instead of 2D space is an essential topic when building perception systems for autonomous driving. An autonomous vehicle (AV) needs to ...
  • An Automated System for Inspecting Rock Faces and Detecting Potential Rock Falls Using Machine Learning
    An Automated System for Inspecting Rock Faces and Detecting Potential Rock Falls Using Machine Learning
    Moreu, Fernando ;
    Stormont, John
    2021-08-01 | PDF
    Rockfall is a hazard in mountainous areas threatening infrastructure and human lives. Rockfall hazards are often mitigated by manual inspections using pry bars. The inspector must access the rock face, hit the rock surface, detect, and remove the loose rocks. This method is very labor demanding, unsafe, and challenging. This research presents a met ...
  • Green Waves, Machine Learning, and Predictive Analytics: Making Streets Better for People on Bikes [supporting datasets]
    Fickas, Stephen
    ORCID ICON IMG
    2021-08-01 | PDF
    The project builds on a prior app that was designed for Green Light Optimized Speed Advisory (GLOSA). This is more colloquially known as keeping a vehicle in the green wave: you are at a location and moving at a speed that will allow you to (theoretically) have a green light at each intersection you encounter along a corridor. Our long-term goal is ...
    Dataset Supporting Files
  • Computational GraphBased Framework for Integrating Econometric Models and Machine Learning Algorithms in Emerging DataDriven Analytical Environments
    Computational Graph-Based Framework for Integrating Econometric Models and Machine Learning Algorithms in Emerging Data-Driven Analytical Environments
    Zhou, Xuesong (Simon) ;
    Pendyala, Ram M.
    2021-08-01 | PDF
    In an era of big data and emergence of new technologies such as app-based ride services, there are growing opportunities for better understanding human mobility patterns from newly available data sources. Statistical models have been mainly utilized to uncover and rigorously calibrate the influence of significant factors; and machine learning algor ...
  • Green Waves Machine Learning and Predictive Analytics Making Streets Better for People on Bikes
    Green Waves, Machine Learning, and Predictive Analytics: Making Streets Better for People on Bikes
    Fickas, Stephen
    ORCID ICON IMG
    2021-08-01 | PDF
    This project focuses on giving bicyclists a safer and more efficient path through a city's signalized intersections. It builds on a prior NITC project that tested an app for a fixed-time corridor. The goal of this project is to lay the groundwork for extending this earlier app to include actuated signals. Two machine-learning algorithms are introdu ...
541 - 560 / 613
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