Artificial intelligence is no longer a distant future for public transportation. It’s here, shaping how agencies and operators plan, optimize, and deliver mobility every day. From predictive scheduling and safety monitoring to better passenger information systems, AI and generative AI are redefining how the industry tackles efficiency, reliability, and the customer experience.
At Optibus’ global webinar “Planning for 2026: How AI Will Transform Public Transportation”, three leaders shared what innovation really looks like in practice:
The conversation drew hundreds of participants from across the public transportation ecosystem, including operators, authorities, and technology providers, reflecting a global appetite for practical insights on AI.
Polls conducted during the session painted a clear picture of where the public transportation industry currently stands on AI adoption. Most organizations are exploring use cases or running small-scale pilots, rather than deploying AI at full scale. When asked where they see the greatest impact, attendees pointed to planning and scheduling and data analytics and performance tracking as top priorities, followed by fleet maintenance and passenger experience.
The biggest challenges are strikingly consistent across regions and organization types. Limited budgets topped the list, followed closely by data quality and system integration, internal resistance, and difficulty identifying use cases. In other words, the ambition is there, but turning that ambition into measurable outcomes requires a foundation of data readiness, process change, and leadership alignment.
While moderating, I noted early in the discussion, successful AI adoption begins with why, not what. “AI adoption starts not with the technology itself, but with a clear operational or customer challenge.”
For Moventis - the division of Moventia that specialises in collective mobility - that starting point was safety. Júlia Farré explained that the company’s first major investments in AI were focused on driver assistance systems (ADAS) to reduce accidents and support operators behind the wheel.
“The quick wins,” she said, “have been in safety - in the form of cameras and sensors that help our drivers drive more safely and comfortably.”
Over the last five years, this focus has helped Moventis modernize its fleet while building trust among staff and passengers alike.
From there, they turned their attention to automation, using data to eliminate repetitive manual tasks.
“We developed a system that lets drivers forget about manual data entry,” Julia shared. “It’s now implemented across all our operations.” By digitizing trip details, start and end times, and mileage data, the operator created a more accurate, real-time picture of performance while freeing up time for frontline teams.
Finally, Julia highlighted how AI has elevated the customer experience. In Pamplona, Moventis used predictive analytics to forecast bus occupancy and share that information with passengers through its app, increasing engagement by 40%. These examples show that successful AI projects start small, target real problems, and scale once value is proven.
At GoDurham - the public bus transportation system serving Durham, North Carolina, operated under contract by RATP Dev USA - Michael Hutchins echoed a similar approach.
“We’re like a baby learning to crawl,” he said with a smile. “We want to run before we even know how to crawl.” GoDurham began its AI journey with a narrow but impactful pilot: implementing a Traffic Signal Priority (TSP) system that uses onboard data and AI to help buses move more efficiently through congested areas.
The early results were promising: “We grabbed a small number of buses to test it,” Michael explained. “Travel times decreased, and we decided to roll it out across the fleet.” The project was a simple proof of concept that demonstrated clear operational benefits and helped win internal buy-in.
His story underlines an important point about digital transformation in public transport: AI doesn’t need to start as a massive initiative. Testing, iterating, and scaling successful pilots - particularly in data-rich areas like fleet performance and on-time reliability - helps organizations build both confidence and competence in using AI.
While Moventis and GoDurham represent different parts of the mobility ecosystem, both show how critical it is to simplify complexity. Sharad Agarwal, representing the technology provider’s perspective, emphasized that AI’s biggest value lies in making complex processes intuitive and accessible. “The real target,” he said, “is to move complex inputs into natural language: that’s where adoption and training accelerate.”
For Optibus and other SaaS mobility platforms, this shift means using natural language interfaces and generative AI to streamline planning and scheduling tasks that were once time-consuming and highly technical.
“What used to take hours to model,” Sharad noted, “can now be tested 15 different ways in seconds.”
By turning sophisticated algorithms into everyday tools, AI lowers the barrier to innovation.
He also pointed out how these technologies can transform administrative and strategic work: “AI can soon auto-generate reports, grant documents, or even board minutes using data that came in six minutes ago.” This is the quiet revolution of AI in transit: enabling faster, evidence-based decisions without overburdening teams with data complexity.
Despite the progress, panelists were quick to acknowledge that digital transformation isn’t easy. Júlia Farré described how data readiness remains the biggest hurdle: “Even before processing anything, you need to know the data source is reliable… Legacy systems block data from being homogeneous.” Without consistent, high-quality data, even the best AI tools struggle to deliver value.
Funding is another constraint, especially for private operators working within public contracts. “Sometimes tenders don’t allow the investment,” Júlia said. “Technology isn’t always valued in the project budget.” When innovation competes with essential service delivery, AI can seem like a luxury unless funders recognize its long-term operational benefits.
For Michael Hutchins, the challenge is often about expectations. “From discovery to implementation, it’s meeting after meeting,” he admitted. “You wonder if you’re really saving time.” Implementing AI means re-thinking workflows, retraining staff, and building patience for incremental progress.
Ultimately, as the discussion revealed, AI success depends as much on culture as it does on code. The more leadership teams invest in education and alignment, the faster innovation becomes part of daily operations.
Perhaps the most encouraging insight from the panel was that AI isn’t replacing people; it’s redefining how they work. “Driving roles will evolve toward monitoring and customer service,” Júlia Farré said, describing a shift already visible in pilots involving semi-autonomous vehicles and automated monitoring systems.
Michael Hutchins added that planners, too, will see their roles change. “AI will help planners bring all the siloed data together, from ridership to driver availability, to build smarter routes and runtimes.” Instead of being overwhelmed by manual analysis, planners can focus on strategic service design.
Sharad Agarwal summed up the sentiment succinctly: “You won’t lose your job to AI, you’ll lose it to someone who knows how to use AI.”
His comment captured a truth that resonates across the sector;
The future of mobility belongs to those who learn how to collaborate with technology, not compete against it.
So, what comes next? According to the panelists, three developments will define the next phase of AI in public transport.
Across all perspectives, one message stood out: AI success in public transportation depends on people, data, and purpose. The most effective projects start small, focus on solving tangible problems, and empower teams to learn along the way. Reliable data remains the foundation for any meaningful progress, while a culture of collaboration and curiosity ensures that technology supports, rather than replaces, human expertise.
We therefore concluded the discussion highlighting that: “AI enhances decision-making, safety, and service quality, but it’s the human insight behind it that makes the difference.”
Catch the full recording of Planning for 2026: How AI Will Transform Public Transportation, and join the conversation about how cities, agencies, and operators can turn AI from potential into practice.