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How AI is Redefining Transit Operations and Signal Priority

In a recent episode of METROspectives, LYT CEO Timothy Menard discusses how artificial intelligence, cloud connectivity, and real-time data are transforming traffic management, boosting bus reliability, and enabling system-wide transit optimization across cities.

Alex Roman
Alex RomanExecutive Editor
Read Alex's Posts
January 16, 2026
Transit signal priority and public transit agencies.

Unlike legacy TSP systems, which depend on static detection and outdated hardware, LYT’s cloud-based system learns over time using machine learning. 

Credit: METRO

4 min to read


In the latest episode of METROspectives, METRO Magazine Executive Editor Alex Roman sat down with Timothy Menard, Founder and CEO of LYT, to explore how artificial intelligence (AI), cloud computing, and real-time data are reshaping the way cities manage traffic, prioritize transit, and support the return to public transportation post-pandemic.

From transit signal optimization to real-world success stories in places like Portland, Oregon, and San Jose, California, Menard explained why the next generation of transit signal priority (TSP) goes beyond simply reacting to traffic — and instead anticipates it.

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Solving Real-World Challenges with Real-Time Precision

LYT, which marks its 10th anniversary this year, began with a clear mission: to use data and predictive analytics to make public transit faster and more reliable. 

According to Menard, the company’s approach to TSP isn't just about green lights; it’s about accurately predicting bus arrivals and making traffic systems smarter.

In Portland, LYT’s technology was deployed along TriMet’s FX bus rapid transit line. On opening day, the difference between the two demonstration buses — one prioritized and one not — was dramatic. 

“The first bus moved like a train,” Menard recalled. “We were only stopping at bus stops, not at lights. That’s the kind of impact we’re creating.”

Unlike legacy TSP systems, which depend on static detection and outdated hardware, LYT’s cloud-based system learns over time using machine learning. It uses fleet data, location tracking, and operational inputs to dynamically adjust signal timing, improving speed, reducing delays, and ultimately transforming the rider experience.

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Transit Is Back and Changing

Menard also shared insights from agency partners, including Santa Clara VTA in San Jose, noting a clear post-COVID ridership shift, particularly evident in weekday commutes and weekend event travel. 

“People are coming back to the office — and we're seeing it in the data,” he said. “Weekend ridership is also surging thanks to more frequent, comfortable service.”

He cited Caltrain’s electrification and Amtrak’s new Acela service as evidence that when transit is fast, clean, and reliable, people will choose it. 

“Nobody wants to drive into downtown San Francisco or fly between Boston and New York if there’s a better option,” he said.

LYT CEO Timothy Menard.

Agencies should come into projects with a mindset for success and embrace a more transparent, collaborative approach to vendor relationships, said LYT's Timothy Menard.

Credit: METRO

From Prioritization to Optimization

LYT’s long-term vision goes beyond simple priority. 

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“When you’re optimizing multiple bus routes at once, you’re no longer doing signal priority,” Menard said. “You’re doing transit signal optimization — a full system-level approach.”

This approach is already underway in San Jose, where VTA has prioritized all 29 citywide routes, creating conditions for multiple buses to move through intersections without delay. 

Menard noted that this scale of deployment pushes agencies to adapt quickly, rethink scheduling, and align planning with real-time operations.

Implementation: It’s Not the Tech, It’s the Teamwork

Menard emphasized that the biggest hurdle isn’t technology, it’s coordination. Transit agencies don’t own traffic signals, so success depends on early alignment among transit, traffic, and IT teams.

“We act as the Rosetta Stone between emergency services, transit, and traffic departments,” he said. “The faster everyone agrees on a shared vision and goals, the faster we can implement.”

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Menard also advised agencies to start small: choose one corridor, identify pain points, and pilot the solution before expanding. 

“We’re not here to sell you cool tech,” he said. “We’re here to solve real problems like lost service hours, fuel costs, and schedule delays.”

LYT technology in action.

LYT, which marks its 10th anniversary this year, began with a clear mission: to use data and predictive analytics to make public transit faster and more reliable. 

Credit: LYT

The Future: Interoperability and Digital Twins

Looking ahead, Menard is most excited about the growing potential for interconnected systems and digital simulations. 

“We can now model 95% of a deployment in a digital environment before we even touch a traffic light,” he said. This shift enables agencies to test, iterate, and plan more effectively and faster.

He also predicted more ecosystem-level collaboration across software providers, vendors, and public agencies. 

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“We’re finally seeing true interoperability in transportation tech,” he said. “That’s going to unlock incredible gains in efficiency and rider experience.”

Advice to Agencies: Be Ready, Be Open, Be Collaborative

Menard closed the conversation with a call to action: agencies should come into projects with a mindset for success and embrace a more transparent, collaborative approach to vendor relationships.

“Bring everyone to the table, including vendors, third parties, and your own teams,” he said. “Know what problem you want to solve and be ready to define success. That’s how we move faster and make transit better for everyone.”

This article, generated using OpenAI, was edited for accuracy and style and based on an episode of METROspectives

 

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