22/05/2026
While most of the country was glued to the latest political drama in the Senate, something historic happened quietly in Lipa City that could have an even bigger effect on our daily lives.
Science officials in CALABARZON approved the deployment of an AI-powered traffic analytics system. It didn’t make headlines in the same way as a shootout and a staircase chase, but it should have. Because what just happened in Batangas, boring as it might be by comparison, might actually hold the key to solving the country’s most exhausting crisis next to corruption: traffic.
And here’s the best part. SEERMO, the platform Lipa just deployed, runs on cameras the city already has. No new infrastructure required. Plug the existing CCTV network into AI that reads traffic in real time, and what used to be unwatched footage becomes live intelligence: flow, speed, volume, congestion hotspots, all of it processed automatically and fed directly into signal timing decisions.
It completely shifts the logic of transport planning by doing what traditional systems fail to do: counting people, not just cars, with a target of a 15 to 20 percent cut in peak-hour travel times.
Now let’s put that in perspective. According to analysis done by TomTom, Metro Manila motorists lose an average of 143 hours a year to congestion. JICA projects the economic damage to reach ₱6 billion a day by 2030.
Now just try to imagine what a 20 percent more efficient system could look like.
Every forty-eight hours, we could save enough to build a specialty hospital from scratch. In less than a school year, the productivity wasted in traffic could erase the country’s entire classroom shortage.
Try to imagine a brand-new public school burning every 30 minutes. Not in fire. In brake lights. That’s the real cost of traffic.
Because each extra minute we spend unnecessarily in traffic is the hospitals we never built, the classrooms we never had, the wages that never made it home. A nation’s worth, stuck in park.
Here’s what the current problem looks like in practice. A well-meaning enforcer at a key intersection extends a green (aka the buhos system) based solely on instinct, or entirely on what he can see. But what he can’t see is the feeder road two blocks back that’s already stacking. His override on EDSA backs up Shaw. The manually extended green on Ortigas jams the side streets.
Decisions that look like solutions from where you’re standing compound into gridlock from where everyone else is sitting.
It’s not the enforcer’s fault. It’s a visibility problem. And you cannot solve a visibility problem by stationing more people who can’t see each other.
AI traffic cameras change that equation entirely.
Unlike fixed-timer signals, or even the adaptive sensor-based systems MMDA has deployed across 96-plus intersections, AI camera systems use computer vision and machine learning to read live conditions across an entire corridor simultaneously. Vehicle volume, speeds, incident detection, vehicle classification; processed in under two seconds, adjusted dynamically, without a single radio call or manual override.
The system sees everything. It adjusts for everything. And it doesn’t get tired or hungry halfway through a twelve-hour shift.
Other major cities around the world are proving that this isn’t theoretical. Pittsburgh tried it and cut intersection wait times by 40 percent. Overall travel time dropped 25 percent. Emissions from idling fell 21 percent. Lisbon ran Siemens AI across 260 intersections and recorded travel time reductions of 20 to 70 percent during peak hours. Los Angeles saves 9.5 million driver-hours annually through real-time signal syncing alone. Singapore built its entire urban mobility strategy around predictive AI and camera analytics.
It’s time we bring it to Metro Manila, the traffic capital of the world.
The logical starting point is an aggressive EDSA-C5 AI corridor, fully integrated with Metrobase, with phased automated enforcement that removes the need for uniformed officers standing in the middle of traffic to manage traffic and into safer workspaces. MMDA, DOTr, and DICT need to be in the same room with a single mandate and a real timeline and not just a working group that produces a report that produces another working group.
Manila doesn’t need wider roads.
It doesn’t need more hands at the intersection.
It needs eyes above it. Or AI’s.
