T3 Webinar Overview
Raising Awareness of Artificial Intelligence for Transportation System Management and Operations
Date: Thursday, February 13, 2020
Time: 1:00 PM – 2:30 PM ET
Cost: All T3 webinars are free of charge.
PDH: 1.5 View PDH Policy
T3 and T3e Webinars are brought to you by the Intelligent Transportation Systems (ITS) Professional Capacity Building (PCB) Program of the U.S. Department of Transportation’s (USDOT) ITS Joint Program Office (JPO). References in this webinar to any specific commercial products, processes, or services, or the use of any trade, firm, or corporation name is for the information and convenience of the public, and does not constitute endorsement, recommendation, or favoring by the USDOT.
Artificial intelligence (AI) broadly encapsulates technologies for human-like reasoning, knowledge representation, pattern matching, planning, learning, perception, natural language processing, and moving and manipulating objects. Machine learning applications offer the potential to support human work in a variety of areas of transportation system management and operations (TSMO), including traffic imagery analysis, incident detection, traffic control, traffic management center (TMC) function automation, data analysis, driverless vehicles, and airborne drones.
This webinar summarizes a report by the Federal Highway Administration (FHWA) to raise awareness of the potential for applications of AI in TSMO. The presentation contains a brief history of AI, a summary of key technologies, a summary of major current platforms for AI system development, and key tips for getting started.
A presentation by the Delaware Department of Transportation (DelDOT) supplements the report with active examples of AI applications for TSMO. FHWA and USDOT will also present their current research and planned activities related to AI for transportation applications.
The target audience includes ITS and TSMO practitioners and researchers; State and local departments of transportation; and others with an interest in AI for TSMO.
The objectives of the webinar are to provide the audience with:
- A better understanding of how AI could improve TMC and TSMO operations.
- A brief introduction of AI technology and the opportunities of DOT applications.
- Some examples of successful applications in transportation fields, as well as in other industries.
Jimmy Chu, Transportation Specialist, U.S. Department of Transportation, Federal Highway Administration (FHWA)
Mr. Chu is a Transportation Specialist on the Traveler Information Management Team at FHWA in Washington, D.C. In this position, Mr. Chu oversees the Travel Time on Dynamic Message Signs program. He also manages the Transportation Management Center Pooled-Fund Study. He provides technical support to State agencies relating to traveler information programs. Prior to joining FHWA, Mr. Chu worked at the Virginia Department of Transportation for 25 years and was the TMC Manager in Northern Virginia. He holds a BS in Civil Engineering from the University of Maryland.
Douglas Gettman, PhD, Global Director of Smart Mobility and AV/CV Consulting Services, Kimley-Horn
Dr. Gettman has more than 25 years of experience with AI, ITS technologies, adaptive traffic control, automated and connected vehicles, and ATMS software development for State and local TSMO agencies across North America and abroad. Douglas first developed and analyzed neural network models in 1992. He is author or co-author of over 40 research papers and FHWA, National Cooperative Highway Research Program (NCHRP), and USDOT guidance publications. Dr. Gettman has a BS, MS, and PhD in Systems Engineering from the University of Arizona.
Gene Donaldson, Transportation Management Center (TMC) Operations Manager, Delaware Department of Transportation
Mr. Donaldson has been with the Delaware Department of Transportation since 1997. His primary responsibility is managing DelDOT’s transportation management program to include operation of the 24-hour statewide TMC, planning and implementation of Delaware’s ITS, incident and event management, emergency management, and transportation homeland security. Prior to working at the Delaware Department of Transportation, Gene retired from the Montgomery County, Maryland Department of Public Works and Transportation after 27 years. He retired as Chief of the Transportation Management Section where he was responsible for managing the implementation and operation of the Nation’s first fully integrated advanced transportation management system. Gene is a past president of District 2 and the Washington, D.C. section of the Institute of Transportation Engineers.
Bob Sheehan, U.S. Department of Transportation, Intelligent Transportation Systems Joint Program Office (ITS JPO), Multimodal ITS Research and Deployment Program Manager
Mr. Sheehan began working as the Multimodal ITS Research Program Manager at the USDOT’s ITS JPO in August 2013. Bob co-leads the USDOT’s Mobility on Demand Program and Accessible Transportation Technologies Research Initiative with the Federal Transit Administration (FTA) and FHWA. Prior to joining the ITS JPO, Bob was with the FHWA leading the USDOT’s Integrated Corridor Management (ICM) initiative, and before that spent eight years at the Virginia Department of Transportation as the Field Operations Manager for the Northern Virginia Smart Traffic Signal System and the Freeway Operations Engineer for the Smart Traffic Center. He received his Professional Engineer license in 2004 and his Professional Traffic Operations Engineer certification in 2008. He holds a master’s degree in Transportation Systems from Virginia Tech and a bachelor’s degree in Civil Engineering from West Virginia University.
Peter Huang, PhD, Transportation Operations, Concepts & Analysis Team Member, Federal Highway Administration (FHWA)
Dr. Huang is currently leading many research projects on AI applications in transportation systems, including AI applications for TMC operations, for vehicle tracking using inductive loops signature marching, for dynamic Origin-Destination (O-D) matrix generation, and for hardware-in-the-loop simulations, etc. Peter has conducted research on AI applications for transportation engineering for 30 years, and has 10 publications on the topic.