Can Artificial Intelligence Solve Bangkok’s Traffic Jams?

Citizens and tourists may soon find out, as tech giant Google partners with Bangkok’s traffic authorities to harness big data, analytics, and artificial intelligence     in an effort to ease congestion on the city’s famously clogged streets.

Thailand’s capital, home to over 11 million residents, has long been notorious for its traffic jams. Despite having a vehicle population comparable to that of        New York or Paris, Bangkok has only about one-third as many roads. Over the years, various city governors and national leaders have pledged to solve the traffic crisis - only to eventually concede defeat.

Since the late 1990s, Bangkok’s steadily growing network of commuter rail lines has helped alleviate some pressure and improve urban mobility. Yet traffic snarls persist, and almost anyone who has traveled across the city by car or bus has a story of being stuck in gridlock, often for what feels like an eternity.

Now, a new initiative - aptly named Project Green Light - seeks to change that.

The project will apply big data and AI to optimize traffic signal management citywide. By analyzing real-time traffic patterns, the system can dynamically adjust signal timings, adapting to the actual flow of vehicles rather than following fixed intervals.

Ms. Saranee Boonritthongchai of Google Thailand likened the system to a traffic light that can “think.” Rather than relying on pre-set schedules, these AI —enhanced signals will assess current traffic conditions and respond accordingly— resulting in shorter wait times at intersections and smoother overall traffic flow.

City officials have already piloted the system at several intersections over the past three months. Encouraged by its performance, they now believe the time is right to scale it up across the city.

According to The Nation newspaper, Project Green Light uses advanced tools to recommend optimized signal timings. Traffic engineers then evaluate each recommendation, considering factors such as safety, feasibility, and potential impact before implementing changes.