Skip to Content

APTA Highlights Public Transit Industry Innovation: Releases New Artificial Intelligence Primer and Guidance Briefs

New resources provide real-world applications and  a roadmap for responsible AI implementation

Washington, D.C. (May 14, 2026) — The American Public Transportation Association (APTA) today released Artificial Intelligence (AI) and Machine Learning (ML) in Public Transit: A Primer, together with four companion AI Guidance Briefs, providing public transit agencies with a comprehensive resource for understanding, evaluating, and deploying AI and ML tools across their operations.

The Primer and Guidance Briefs draw on a survey of transit agencies and staff interviews to capture current and planned AI applications across eight functional domains: back office, operations, customer support, maintenance, safety and security, customer analytics, planning, and fares and ticketing.

“Public transit agencies of all sizes are deploying AI to make service more reliable, more efficient, and more responsive to riders,” said Paul P. Skoutelas, APTA President and CEO. “These resources give our member organizations a clear picture of where the industry stands and a practical roadmap for moving forward responsibly—from the largest metro systems to small rural providers.”

Key Findings

Customer support and customer analytics are currently the most common areas of AI deployment. When including public transit agencies’ plans to deploy AI, back office and operations functions top the list, with 50 percent and 47 percent of survey respondents, respectively, indicating current or planned use.

Real-world examples documented in the Primer include:

  • Metropolitan Transportation Authority (NY) increased maintenance productivity by 75 percent and decreased material costs by 24 percent using an AI-powered predictive maintenance system for its bus fleet.
  • AC Transit (CA) used AI image recognition for bus lane enforcement, increasing violation citations from 22 to 787 over a comparable two-month period following implementation.
  • Riverside Transit Agency (CA) piloted a disruption management tool that automatically pushes real-time detour updates to riders and operators across multiple channels, cutting response times and improving schedule reliability.
  • CapMetro (TX) deployed an AI virtual agent for paratransit trip scheduling, freeing customer service staff to focus on more complex calls.
  • Prairie Hills Transit (SD), a paratransit provider, deployed an AI-based dispatch system that automated vehicle assignments and driver scheduling, replacing a process previously managed with handwritten notes.

“What stands out in this research is the breadth of practical applications already underway,” said Skoutelas. “Agencies are piloting, learning, and scaling—and that pragmatic approach is exactly what will help public transit deliver even better outcomes for riders and communities.”

Guidance Briefs

The four AI Guidance Briefs address the key challenges agencies face when implementing AI:

  • Tool and Infrastructure Needs addresses data integration challenges, real-time data access requirements, and computing infrastructure.
  • Policy and Governance Needs covers AI policy frameworks, compliance risks, data ownership, and the evolving State and Federal legislative landscape.
  • Agency Readiness and Staff Capacity outlines the skills and organizational capacity needed to evaluate, procure, and manage AI tools.
  • Implementation Guide walks agencies through a needs-based approach to scoping, procurement, piloting, and monitoring AI deployments.

“There is no single path to AI adoption,” Skoutelas said. “What matters is that agencies start from a clear understanding of their needs, have the right governance structures in place, and move forward with transparency and accountability to the riders and communities they serve.”

About the Research

The Primer and Guidance Briefs were developed with research support from EBP and Foursquare ITP. Findings are drawn from an online survey of 32 APTA member transit agencies, supplemented by interviews with agency staff and a review of publicly available documentation on agency AI deployments.

For more information, visit Artificial Intelligence and Machine Learning in Public Transit – APTA

Media Contact: Amy Thompson, athompson@apta.com, 202-285-2997

###