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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 441 - 460 of 613 results
  • Using Artificial Intelligence to Improve Traffic flows with Consideration of Data Privacy
    Using Artificial Intelligence to Improve Traffic flows, with Consideration of Data Privacy
    Chassiakos, Anastasios ;
    Shaffer, Gwen
    2022-09-01 | PDF
    This project develops an artificial neural network (ANN), a class of Artificial Intelligence (AI) systems, to accurately model and predict future delays at an intersection. Developing such modeling and prediction systems raises considerable data privacy concerns and it is incumbent upon municipal, state, and federal branches of government to priori ...
  • RealTime Freeway Speed Prediction Based on Deep Learning in Connected and Autonomous Vehicles Environment
    Real-Time Freeway Speed Prediction Based on Deep Learning in Connected and Autonomous Vehicles Environment
    Fan, Wei ;
    Hua, Chengying
    2022-09-01 | PDF
    The main objective of this research project is to enhance the accuracy of traffic speed prediction in ITS. The objectives of this project include: (1) Conducting a comprehensive review of traffic prediction techniques for CAVs. (2) Identifying a potential freeway segment and collecting the features of the selected scenario. (3) Developing and apply ...
  • Investigating the Effects of Cooperative Driving for CAVs in Different Driving Scenarios Using MultiDriver Simulator Experiments
    Investigating the Effects of Cooperative Driving for CAVs in Different Driving Scenarios Using Multi-Driver Simulator Experiments
    Yue, Lishengsa ;
    Abdel-Aty, Mohamed
    2022-08-29 | PDF
    Cooperative driving powered by connected vehicle (CV) technology is expected to improve traffic safety and efficiency, especially at locations with dense vehicle interactions. Although lots of research have developed their cooperative driving algorithms for different locations, the effects of human drivers in the loop and multi-agent driving decisi ...
  • Lane Changing of Autonomous Vehicles in Mixed Traffic Environments: A Reinforcement Learning Approach [Supporting Dataset]
    Jiang, Zhong-Ping ;
    Ozbay, Kaan
    2022-08-10 | BIN
    The purpose of this proposal is to develop innovative reinforcement learning control methods for lane changing of connected and autonomous vehicles (CAVs) in mixed traffic. In the proposed framework, before the CAV changes to the target lane, it needs to predict most likely behavior of surrounding vehicles related to the lane change and then determ ...
    Dataset
  • Traffic Congestion Identification and Prediction Based on Image Processing and Deep Learning Methods
    Traffic Congestion Identification and Prediction Based on Image Processing and Deep Learning Methods
    Whalin, Robert W ;
    Hu, Guojing
    2022-08-02 | PDF
    Short-term traffic flow/speed prediction is a significant and challenging research topic as it is closely related to the application of intelligent transportation systems. Due to the variable and random characteristics of the transportation system, raw traffic flow/speed data often contains noise, and predicting it directly may reduce the accuracy ...
  • Driving Behavioral Learning Leveraging Sensing Information from Innovation Hub
    Driving Behavioral Learning Leveraging Sensing Information from Innovation Hub
    Di, Xuan ;
    Jin, Peter
    2022-08-01 | PDF
    With the accelerated deployment of connected and automated vehicle (CAV) technologies, public agencies have urgent needs on how to utilize these rich data sources of CAVs to improve traffic mobility, safety, and environmental and energy impact. This research will tackle one of the big data challenges, which is mining driving behavior patterns using ...
  • Detecting Foundation Pile Length of HighMast Light Towers
    Detecting Foundation Pile Length of High-Mast Light Towers
    Kennedy, Daniel ;
    D'Agostino, Mollie
    2022-08-01 | PDF
    The goal of the project is to establish a non-destructive field testing technique, including a data analysis algorithm, for determining in-place pile lengths by way of seismic waves. The length of each pile supporting a high-mast light tower (HMLT) will be identified through a systematic sensing approach that includes (i) collection and classificat ...
  • Bridge Monitoring Through a Hybrid Approach Leveraging a Modal Updating Technique and an Artificial Intelligence AI Method Research Brief
    Bridge Monitoring Through a Hybrid Approach Leveraging a Modal Updating Technique and an Artificial Intelligence (AI) Method [Research Brief]
    Cho, Chunhee ;
    Moon, DoSoo
    2022-08-01 | PDF
    The objective of this project is to develop a post processing damage detecting algorithm by leveraging a modal updating technique and an artificial intelligence (AI) method. Problem Statement: The proposed project integrated two methods (i.e., a model updating technique and a data-driven prediction method) that can compensate for each other's the w ...
  • Prediction of Traffic Mobility Based on Historical Data and Machine Learning Approaches
    Prediction of Traffic Mobility Based on Historical Data and Machine Learning Approaches
    Deng, Yong ;
    Chen, Chuang
    2022-08-01 | PDF
    The goal of this project is to develop predictive models for traffic mobility using ML approaches. The focus is placed on two essential components - traffic speed and traffic volume. To this end, this project addresses the following objectives: (1) identifying appropriate WSDOT highway segments for this modeling study and collecting the relevant hi ...
  • Postdisaster Damage Assessment of Bridge Systems
    Post-disaster Damage Assessment of Bridge Systems
    Liang, Xiao ;
    Sajedi, Seyed Omid
    2022-08-01 | PDF
    The existing ML models for vibration-based damage assessments often use highly compressed features or are restricted to preprocessed signals with fixed durations and sampling rates. Additionally, the learning capacities of ML models and computational resources are limited, which restricts using raw signals as direct input. This report studies Mel F ...
  • Realtime Decision Support System for Transportation Infrastructure Management Under a Hurricane Event
    Real-time Decision Support System for Transportation Infrastructure Management Under a Hurricane Event
    Li, Shaopeng ;
    Wu, Teng
    2022-08-01 | PDF
    Under hurricane weather and traffic conditions, stakeholders need to make a series of decisions to close or restrict the traffic of vulnerable components in the transportation network for balancing traffic safety and mobility. To manage these critical components for minimizing the overall network-level losses, a deep reinforcement learning (RL)-bas ...
  • Management of Supply Chain Disruption of Freight Network Using Advanced Algorithms
    Management of Supply Chain Disruption of Freight Network Using Advanced Algorithms
    Kaisar, Evangelos I. ;
    Machado, Aline Anacleto
    2022-08-01 | PDF
    The growing complexity of global supply chains and the rising frequency of transportation disruptions have underscored the need for predictive tools that enhance freight network reliability. This study explores the integration of big data analytics and machine learning to support short-term travel time prediction and incident detection in freight t ...
  • Enabling a New Data Science for Urban Accessibility for All
    Enabling a New Data Science for Urban Accessibility for All
    Froehlich, Jon E
    ORCID ICON IMG
    2022-07-31 | PDF
    We are developing new data collection approaches that use a combination of remote crowdsourcing, machine learning, and online map imagery. Our newest effort, called Project Sidewalk, enables online crowdworkers to remotely label pedestrian-related accessibility problems by virtually walking through city streets in Google Street View. Aided, in part ...
  • Pilot Study Machine Learning and Deep Learning Study for Fluid Structure Interaction Problems
    Pilot Study: Machine Learning and Deep Learning Study for Fluid Structure Interaction Problems
    Kasi, Zarak K. ;
    Simpson, Barbara G
    2022-07-29 | PDF
    Coastal bridges are critical to emergency response after extreme events and are vulnerable to cascading seismic-tsunami events. After the 2011 earthquake and subsequent tsunami in Japan, instances of damage and collapse were observed in Japanese bridges that survived the earthquake but failed under the hydrodynamic loads induced by the tsunami. The ...
  • Automated RealTime Weather Detection Systems Using Artificial Intelligence
    Automated Real-Time Weather Detection Systems Using Artificial Intelligence
    Ahmed, Mohamed M. ;
    Khan, Md Nasim
    2022-07-01 | PDF
    Adverse weather has long been recognized as one of the major causes of motor vehicle crashes due to its negative impact on visibility and road surface. Providing drivers with real-time weather information is therefore extremely important to ensure safe driving in adverse weather. However, identification of road weather and surface conditions is a c ...
  • Automated RealTime Weather Detection Systems Using Artificial Intelligence Research Brief
    Automated Real-Time Weather Detection Systems Using Artificial Intelligence [Research Brief]
    Ahmed, Mohamed M ;
    Khan, Md Nasim
    2022-07-01 | PDF
    The primary objective of this research was to develop cost-effective systems capable of providing accurate weather and surface conditions in real time. First, a trajectory-level weather detection system was developed using a single video camera mounted on a vehicle dashboard. Two texture-based features, histogram of oriented gradient (HOG) and loca ...
  • Bridge Monitoring Through a Hybrid Approach Leveraging a Modal Updating Technique and an Artificial Intelligence AI Method
    Bridge Monitoring Through a Hybrid Approach Leveraging a Modal Updating Technique and an Artificial Intelligence (AI) Method
    Cho, Chunhee ;
    Moon, DoSoo
    2022-07-01 | PDF
    An early damage identification process in bridge structures may offer an opportunity to slowdown progressive failure and thus prevent catastrophic collapses. With a structural health monitoring system which allows real-time measurement of structural responses, early damage in bridge structures can be identified with proper techniques. Recently, dat ...
  • DataDriven Bridge Management Using Descriptive and Predictive Machine Learning Models Research Brief
    Data-Driven Bridge Management Using Descriptive and Predictive Machine Learning Models [Research Brief]
    Banaei-Kashani, Farnoush ;
    Rens, Kevin
    2022-07-01 | PDF
    Since the 1970's, several U.S. Acts have mandated all local and state transportation agencies across the nation to perform regular inspections of the bridges (and culverts) in their jurisdiction. These have generated valuable historical databases of bridge performance data which have remained considerably underutilized.
  • Improving Methods to Measure Attentiveness through Driver Monitoring
    Improving Methods to Measure Attentiveness through Driver Monitoring
    Miller, Marty ;
    Herbers, Eileen
    2022-07-01 | PDF
    Driver inattention poses a significant problem on today's roadways, increasing risk for all road users. This report details our efforts in developing algorithms to detect driver inattention. A benchmark dataset was developed based on video review of driving events. Buffer-based algorithms were developed and compared using this benchmark dataset. Th ...
  • Landslide Risk Assessment in Cut Locations Using Artificial Intelligence Based on RightofWay Videos and Geophysical Data
    Landslide Risk Assessment in Cut Locations Using Artificial Intelligence Based on Right-of-Way Videos and Geophysical Data
    Palese, Michael E ;
    Pei, Te
    2022-06-30 | PDF
    Sidehill and through cuts are often used in the construction of new railroad rights-of-way to limit the length, curvature, and grade of the route. However, rights-of-way that utilize cuts are susceptible to damage from falling debris driven by slope failure events such as shallow landslides and rockfalls. At-risk slopes, or geohazards, are traditio ...
441 - 460 / 613
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