As thousands of Washington, D.C. drivers headed to Arlington National Cemetery for the Armistice Day ceremony, they found themselves stuck in the world’s first traffic jam. On November 11, 1921, the congestion trapped motorists in their cars for hours—along with one very displeased President Harding, whose limousine had been caught up in the middle of it all. People were frustrated, tired, and unaware that they were making history.

Just 100 years later, urban traffic chaos persists. The systems may offer a new solution to this century-old problem, while at the same time addressing the sustainability challenges of the future.

There are good reasons why cities have struggled to solve traffic management challenges.

Taiwan’s Ability Enterprise, a leading OEM/ODM solution provider for smart AI cameras, has partnered with LED Vision, a leading provider for LED products and IoT systems in Malaysia. The partnership sees the deployment of Clear-Sight edge-based AI cameras along with LED Vision’s AdvanCTi, a central management software platform for city lighting, camera, and traffic intelligence, in Malaysia for smart city traffic management.

Malaysia is facing a growing traffic congestion crisis. Major cities like Kuala Lumpur, Johor Bahru, and Penang frequently face crippling jams during peak hours. This wastes time, hurts productivity, increases accidents, and causes immense frustration for commuters.

To tackle this complex issue, Malaysian company LED Vision has developed an innovative AI-powered smart traffic management system called SASCOO. I recently had the chance to learn more about this pioneering made-in-Malaysia technology that is already transforming traffic conditions in cities deploying it.

SASCOO (Step Adaptive Split Cycle Offset Optimizer) is an AI-powered adaptive traffic control system developed by LED Vision to reinvent traffic management.

Real-time traffic optimization

SASCOO uses data from sensors and cameras to monitor traffic continuously and optimize signals accordingly

Adaptive signal control

Signal phases are adjusted based on actual traffic demand. Green time extended for high volumes or shortened for low volumes.

Green wave coordination

Signals along a route synchronized to enable continuous traffic flow without stops.

Machine learning engine

Self-learns from data to improve signal plans based on evolving traffic patterns.

Remote monitoring and control

Traffic operators can view real-time traffic dashboards and override signals remotely if needed.

Edge sensors and cameras at the intersection to collect traffic data like vehicle counts, wait times, queue length etc.

On-premise SASCOO controller with AI software for localized real-time optimization of signals at each junction.

Central cloud analytics platform to aggregate data from all intersections and coordinate signals across the wider network.

The adaptive AI algorithms continuously analyze the incoming data and adjust signal plans to optimize for traffic flow. This includes extending or contracting green phases based on actual demand, coordinating greens to enable smooth flow through multiple intersections, and learning from daily traffic patterns to predict optimization needs.