APTA releases resources to help public transit agencies utilize AI, ML tools across operations

The American Public Transportation Association (APTA) released Artificial Intelligence (AI) and Machine Learning (ML) in Public Transit: A Primer, alongside four companion AI Guidance Briefs, offering transit agencies a resource for evaluating and deploying AI and ML tools across their operations.

The primer and guidance briefs utilize survey data of transit agencies and staff interviews to gather a picture on the current state of and planned implementations of 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 APTA President and CEO Paul Skoutelas. “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.”

Findings

APTA reports that customer support and customer analytics are the most common areas of AI deployment currently. When including public transit agencies’ plans to deploy AI, back office and operations functions top the list, with 50% indicating current use and 47% indicating planned use.

Real-world examples documented in the primer include:

• Metropolitan Transportation Authority increased maintenance productivity by 75% and decreased material costs by 24% using an AI-powered predictive maintenance system for its bus fleet.
• Alameda-Contra Costa Transit District 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 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.
• Capital Metropolitan Transportation Authority deployed an AI virtual agent for paratransit trip scheduling, enabling customer service staff to focus on more complex calls.
• Prairie Hills Transit, 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,” Skoutelas said. “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 seek to address the 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

APTA notes that 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.