The history of public transportation is one of constant evolution and reinvention.  Since the origin of the American Public Transportation Association (APTA) in 1882, transit providers have been at the center of continuous technology and institutional change.  Through each ensuing decade, transit systems have been ever evolving.   

Over the past ten years, technologies enabling mobility-on-demand (TNCs, bike-share, micro transit) have become part of the urban mobility landscape.  Today, automated vehicles (AV), artificial intelligence (AI),  and machine learning (ML) are at the cutting edge of another wave of technology-driven innovation. 

Transit agencies are eager to better understand the potential for AI so APTA has been working to provide resources and opportunities for information sharing. The recent APTAtech Conference in St. Louis (August 9-12, 2026) provided the opportunity for  a deep-dive into the various ways transit agencies are putting Artificial Intelligence to work today to boost efficiency, service delivery, security, and the overall rider experience.   

Prior to that, in May 2026, APTA released Artificial Intelligence (AI) and Machine Learning (ML) in Public Transit: A Primer, providing public transit agencies with a comprehensive resource for understanding, evaluating, and deploying AI and ML tools across their operations. Findings were based on a survey of transit agencies and staff interviews to capture current and planned AI applications.  

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 in a test fleet, 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. 

Key Challenges 

Transit agencies face a number of key challenges implementing AI. These issues are the focus of the AI Guidance Briefs that accompany APTA’s AI Primer.  The Guidance briefs address topics including data integration, the evolving State and Federal legislative / regulatory landscape, and procurement issues.   

• Data governance: Strong governance is central to public trust. Governance frameworks need to address risks specific to data-driven and AI-enabled systems, including protections against data poisoning, model manipulation, and unintended cross-system data exposure, and AI users should adopt risk-based data governance principles. 

• Data Management: Modern transit agencies are building data platforms with open source tools while navigating the real-world challenges of scaling analytics. Effective data management in transit environments depends on clearly defined roles for ownership, stewardship, and custodianship across operational and enterprise systems..   

• Workforce Reskilling:   As agencies deploy increasingly sophisticated AI-enabled decision tools, workforce capabilities must evolve accordingly.  Workforce reskilling, change management, and temporary staffing support can help agency employees to develop new technical skills while continuing to sustain safe and reliable service. In addition to traditional IT roles, agencies require expertise in modern data engineering, machine learning operations, digital systems architecture, and data governance to manage the full lifecycle of AI-enabled systems.  

AI and AV as Simultaneous Technology-Driven Forces  

Although tech companies were proven to have been overly optimistic in the timelines they predicted for vehicle automation, companies continue to invest billions with an eye on nascent mobility markets. Waymo has provided nearly 30 million driverless trips, and ridership is rapidly growing as they test new business models (fares, vehicle ownership, maintenance, and third-party partnerships). Tech leaders have shown artist renderings depicting visions of cities freed from parking garages as AV services have the potential to reduce auto ownership and parking demand.  Vehicle automation also offers the opportunity to improve first-mile and last-mile connections to transit, expand access in underserved and lower density communities, and enhance customer convenience through flexible, on-demand service.  

At the same time, collaborative efforts are needed to write the rulebooks for vehicle automation at both the technical and policy levels. Issues that require continued attention include workforce transformation, cybersecurity, regulatory frameworks, financial sustainability, and the future interaction of public transportation networks as tech company business models unfold and as they pursue markets.  

The Importance of Public Policy Direction

I recall reading in High School Literature Class a short story “There Will Come Soft Rains” by the renowned science fiction writer Ray Bradbury. Written in  1950, the setting for the story is August 4, 2026 (the same month as this article is being assembled). The story is a warning of the dangers of self-destruction through the misdirected use of technology. Accordingly, AI and AV policies need to be linked to measurable public outcomes so that investments translate into tangible improvements in safety, reliability, operational efficiency, community life, and overall passenger experience. Identifying these outcomes as core goals is critical. Because if we don’t know where we want to go, any path can get you there.

Art Guzzetti is the Vice President for Policy, Mobility, Technical Services, and Innovation at the American Public Transportation Association