Quick-Response Research on Long-Term Strategic Issues. Task 51. Enhancing Transit Operations with Artificial Intelligence

In the past decade, Artificial Intelligence (AI) has initiated a technological transformation in the transit industry. Public transit agencies are adopting AI to automate various planning, operations, maintenance, workforce, and customer service processes and to continue enhancing safety. Transit operators are shifting from manual data collection and analytics toward AI-based solutions powered by machine learning models. Examples of AI applications in transit include, but are not limited to, vehicle automation, transit signal priority, and computer vision. These and other applications have the potential to enhance route and resource optimization, employee and customer user experience, predictive maintenance, safety and security, and coordination with private transportation companies. Limited knowledge exists on how transit agencies use and evaluate AI for potential risks. More information is needed about where opportunities exist for AI to be integrated into existing transit operations and data sources. This project shall explore the intersection of public transit operations and AI. The primary goal is to better understand how public transit agencies leverage AI to enhance service quality, efficiency, and safety; improve the customer and employee experience; and reduce cost. At a minimum, this project shall: Document the current state of practice for adopting AI in transit planning, operations, maintenance, workforce, traveler information and wayfinding, safety and security, customer service, and vehicle automation for all transit services. Describe three to five near-to-midterm future use cases based on current trends in the transit industry. Assess the following opportunities and challenges for the application of AI in the transit industry to include, but not limited to, technical, cybersecurity, regulatory, workforce, financial, privacy, ethics, equity, and bias. Describe how the transit sector can capitalize on the lessons learned from other transportation sectors and industries, such as defense, healthcare, hospitality, and retail, in applying AI. Identify common transit data sources that could support the AI data-driven approach (i.e., collection, stewardship, analysis, and sharing) and describe related opportunities and challenges in the data space (without duplicating prior TCRP work on data).

• Record URL:
  http://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=5622
• Supplemental Notes:
  • Contract to a Performing Organization has not yet been awarded.

Language

• English

Project

• Funding: $125000
• Contract Numbers:

  Project J-11, Task 51

• Sponsor Organizations:

  Transit Cooperative Research Program

  Transportation Research Board
  500 Fifth Street, NW
  Washington, DC    20001

  Federal Transit Administration

  1200 New Jersey Avenue, SE
  Washington, DC  United States  20590
• Project Managers:

  Schoby, Jamaal

• Start Date: 20240731
• Expected Completion Date: 0
• Actual Completion Date: 0

Subject/Index Terms

• TRT Terms: Artificial intelligence; Costs; Implementation; Public transit; Risk assessment
• Subject Areas: Data and Information Technology; Operations and Traffic Management; Planning and Forecasting; Public Transportation;

Filing Info

• Accession Number: 01902067
• Record Type: Research project
• Source Agency: Transportation Research Board
• Contract Numbers: Project J-11, Task 51
• Files: TRB, RIP
• Created Date: Dec 13 2023 1:25PM