BACKGROUND

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.

OBJECTIVES

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).

RESEARCH PLAN

The research plan will describe appropriate deliverables that include, but are not limited to, the following (which also represent key project milestones): 

1. Amplified Research Plan. An amplified research plan that responds to comments provided by the project panel at the contractor selection meeting.
2. Case Studies. A minimum of five case studies (to include diversity of agency sizes and geographies), including at least one non-transit industry case study.
3. Interim Report. The interim report should include the analyses and results of completed tasks, an update of the remaining tasks, and a detailed outline of the final research product(s).
4. Panel Meeting. The panel meeting will occur after the panel review of the interim report. The interim report should be submitted and the panel meeting should occur after the expenditure of no more than 40 percent of the project budget.
5. Final Deliverables. Final deliverables that present the entire research project that will be useful to practitioners and stakeholders with an executive summary.
6. Technical Memorandum. A technical memorandum titled “Implementation of Research Findings and Products”. 
7. Webinar. A webinar that presents the research findings and conclusions.

STATUS: Proposals have been received in response to the RFP. The project panel will meet to select a contractor to perform the work.