Artificial Intelligence In Transportation Committee
Purpose
Welcome to the home page of ASCE T&DI Technical Committee on Artificial Intelligence. The committee has interests in all topics relevant to AI applications in transportation systems (widely covering supply and demand, freight and passenger, surface, marine, and aviation). The potential activities include conferences, workshops, and sessions in conferences and publications to promote innovative ideas and applications of AI in the transportation area.
Please note: You must be a member of ASCE and/or T&DI in good standing to serve on a committee. If you do not meet this criteria, please update your membership before you apply.
Dr. Terry Yang is an Associate Professor and Clark Faculty Fellow in the Department of Civil and Environmental Engineering at the University of Maryland, College Park. He is the Director of the Maryland Transportation Institute (MTI) and Maryland Transportation Research & AI Lab (M-TRAIL). He is also the Associate Chair of Graduate Studies at the Civil and Environmental Engineering Department. His research areas include AI/ML applications in transportation, traffic operations, traffic safety, data mining, and Quantum Computing. He is the recipient of the NSF CAREER Award. He is currently the Chair of the ASCE AI in Transportation Committee.
About the committee
Knowledge Exchange
To serve as a forum for ideas and knowledge exchange among researchers, engineers, and operators from academia, industry, and government with interests in developing and promoting Artificial Intelligence (AI) technologies and their applications in transportation
Effort Consolidation
To serve as a platform for consolidating on-going efforts in academia, industry, and government to form a collective voice and make significant impacts on the development of AI in transportation from the perspectives of both scientific and technological innovations.
Collaboration
To serve as a connecter for bridging strengths in academia, industry, and government to promote AI technology transformation, regulation development, and deployment in transportation.
Artificial Intelligence — generally referring to methodologies such as knowledge-based systems, neural networks, clustering, fuzzy sets theory, deep learning, and natural language processing—has already been successfully applied in a variety of areas, including health care, manufacturing, power systems, homeland security, and transportation.
Both public (NSF, US DOT FHWA, Vople Center, Nevada State DOT, universities, etc.) and private (Microsoft, Toyota, etc.) stakeholders have investigated the potential of AI for civil and transportation applications, including transportation system identification and function approximation, transportation system behavior prediction (i.e., traffic demand, infrastructure deterioration), signal control, traffic pattern recognition, driver classification, traffic accident prediction and detection, route navigation, and self-driving.
Considering the rapid advances that are being made in adopting and deploying AI-based technology and capabilities into transportation systems, it is important to increase awareness within the transportation community to understand their unique challenges. This will help further advance the efficiency and effectiveness of investing in AI technologies, particularly for improving civil and transportation system safety, mobility, and sustainability.
Track advancements in AI technology and evaluate their potential impacts on transportation development, policy, research and practice.
Foster collaborations between AI technology developers and transportation researchers, agencies, and private sector vendors.
Advance AI and transportation science and technology in a mutually inspiring way.
Gather and disseminate AI application topics, interim research findings, and emerging market needs.
I. Existing Relevant Committees/Task Forces
TRB ABJ70 Committee on Artificial Intelligence and Advanced Computing Applications
TRB ADB30-5 Sub-Committee on Emerging Technologies in Network Modeling
TRB ABJ30 Committee on Urban Transportation Data and Information Systems Committee
TRB Task Force on Data for Decisions and Performance Measures
American Association of State Highway and Transportation Officials (AASHTO) Committee on Data Management and Analytics
II. Research Initiatives/Survey Reports/Circular
November 2019. Whitehouse: 2016–2019 PROGRESS REPORT: ADVANCING ARTIFICIAL INTELLIGENCE R&D
January 2007: Transportation Research Circular E-C113: Artificial Intelligence in Transportation Information for Application
June 4, 2018. U.S. DOT Under Secretary of Transportation for Policy. Transportation in the Age of Artificial Intelligence and Predictive Analytics- A Volpe Thought Leadership Series
U.S. DOT Artificial Intelligence Activities
U.S. DOT Artificial Intelligence Research Highlights
FHWA: The Role of Artificial Intelligence and Machine Learning in Federally Supported Surface Transportation Initiatives
October 9, 2019, NSF, USDA, DHS, VA, DOT Partner to Foster AI Research
NSF: National Artificial Intelligence (AI) Research Institutes: Accelerating Research, Transforming Society, and Growing the American Workforce
Nevada DOT Shares Grant for AI-based Traffic Project
Presented at the 26th IEEE International Conference on Intelligent Transportation Systems
AI and machine learning (ML) have played a significant role in digital transformation and intelligent robotics in transportation. Advances in computer vision have further accelerated the real-time perception and cognition of the environment and improved the safety of roads and mobility. However, the high demand for self-driving has narrowed our focus to in-vehicle technology. “Walking” is one of the most essential activities in daily life, but AI modeling and simulation in vulnerable road users accessibility and wayfinding has been limited.
Workshop
The Frontiers of Artificial Intelligence-Empowered Methods and Solutions to Urban Transportation Challenges
Phase II: Best Practice, Education, and Workforce Development
Dec 15, 2022
Hilton University of Florida Conference Center
1714 SW 34th St
Gainesville, FL 32607 Sponsored by the National Science Foundation
Synopsis
With quickly growing quantity and variety of transportation data, artificial intelligence (AI) technologies are revolutionizing transportation research from system management to automated vehicle and infrastructure control. Emerging AI technologies combined with other analytical methods will lead to improved scientific understandings, transformative informed decisions, and innovative, proactive management solutions of urban transportation infrastructure systems (UTIS). In this workshop, researchers from transportation and computer science disciplines will be invited to explore the frontiers of AI-empowered methods, solutions, and workforce development to urban transportation challenges. The workshop aims to identify the tremendous research and education needs and challenges, and further stimulate transformative research in both transportation and AI communities.
Agenda
Welcome/Opening Remarks: Yueyue Fan, NSF CIS Program Director
Keynote: Joe Conway, Director for Transportation Workforce Development at FHWA
Session Phase II-I: Emerging AI Technology Applications in Transportation Practices
Moderator: Lili Du, Associate Professor, UF
Speakers: Lian Ji, Transportation Planner, AECOM; Mecit Cetin, Professor, Old Dominion University; Balaji Yelchuru, Smart Infrastructure and Mobility Solutions Leader, Accenture; Emmanuel Posadas, PE, Traffic Operations Manager, City of Gainesville
Session Phase II-II: AI Technology Training Program Development Moderator: Scott Washburn, Professor, UF
Speakers: Alina Zare, Professor, Associate Dean of HWCOE, Professor of ECE, University of Florida; Mo Malakoutian, Sr. Program Manager at Amazon; Natarajan Janarthanan, Manager, Travel Data, Modeling, and Analysis Office, WSDOT; Heng Wei, Professor, University of Cincinnati
Poster Video and Presentation Session
Session Phase II-III: New Generation Workforce Development for AI-empowered UTIS
Moderator: Heng Wei, Professor, University of Cincinnati
Speakers: David Kuehn, Program Manager, FHWA; Muhammad Amer, Managing Director, T&DI, ASCE; Larry Rilett, Director of the Auburn University Transportation Research Institute; Ahmed Abdul-Rahim, Professor at the University of ldaho
Session Phase II-IV: Roundtable Discussions: Gains and Lessons from Existing Practices and Identify Future Direction
Group Leaders: Ors. Zhaomiao Guo, Yan Wang, Xianfeng Yang
Flash Presentation (3 minutes per presentation, open to all workshop attendees, focusing on gaps of AI implementation, workforce, and education)
Discussion Reporting and Closing Remarks
Organizing Committee
Yinhai Wang, Ph.D. (Chair), University of Washington
Lili Du, Ph.D. (Chair) - University of Florida
Hani Mahmassani, Ph.D., Northwestern University
Xianfeng Yang, Ph.D., University of Maryland, College Park
Yaw Adu-Gyamfi, Ph.D., University of Missouri
Osama Osman, Ph.D., Leidos Inc.
Samiul Hasan, Ph.D., University of Central Florida
Zhe Jiang, Ph.D. - University of Florida
Simon Du, Ph.D. - University of Washington
Organized by
ASCE T&DI Artificial Intelligence in Transportation Committee
TRB AED50 Artificial Intelligence and Advanced Computing Applications Committee
TRB AEP40-4 Emerging Technologies in Network Modeling Subcommittee
