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.


Let’s delve into the root causes of the acute traffic congestion issue. Malaysia has experienced a swift surge in motorization over recent decades, witnessing a significant spike in vehicle ownership. Unfortunately, the growth in road infrastructure has not kept pace. The reliance on old manual traffic light systems with fixed timing plans has proven inadequate in adapting to fluctuating traffic volumes, leading to prolonged waits, heightened congestion, and extended journey times.

This pressing issue necessitates innovative solutions, and that’s where SASCOO comes into play. Developed by Malaysian company LED Vision, SASCOO stands as a groundbreaking AI-powered smart traffic management system designed to address and alleviate the challenges posed by outdated traffic control systems.

The economic toll of traffic jams is staggering, amounting to over RM 20 billion in annual losses attributed to fuel waste, productivity decline, logistics delays, and environmental impact. SASCOO emerges as an urgent and transformative solution to this multifaceted problem, showcasing the potential to significantly mitigate economic losses and enhance overall traffic management efficiency.


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.


How it Works

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.


SASCOO has been deployed at over 20 intersections in Malaysia to date, including in Putrajaya, Johor and Ipoh. The results have been outstanding:


  • Reduced congestion – Travel times during peak hours decreased by 30-50% after installing SASCOO. Traffic throughput improved by 20-35%.
  • Faster response to incidents – Traffic operators were able to spot bottlenecks in real-time and remotely adapt signals to reduce delays.
  • Improved safety – With smoother traffic flow, accident rates at SASCOO-enabled junctions decreased by 10-15%.
  • Enhanced enforcement – Traffic police used SASCOO’s analytics to better plan enforcement and reduce violations.
  • Lower emissions – Reduced congestion and idling time led to 10-15% lower CO2 emissions around optimized junctions.
  • Data-driven insights – Rich data collected by the system helped cities understand traffic patterns and needs better.


The early success of SASCOO highlights the immense potential of using AI and real-time data to overhaul legacy traffic management systems. LED Vision has ambitious plans to extend SASCOO to over 20 traffic signals in Malaysia this year itself. The goal is to cover the entire country in phases.

International expansion to Southeast Asia is also on the cards, starting with Indonesia and Thailand. The company is also exploring developing new solutions beyond traffic light optimization, such as smart parking systems, traffic enforcement applications, and integrated transit management.

With ever-worsening congestion in Malaysian cities, the need for innovative solutions like SASCOO is urgent. The technology demonstrates how leveraging AI, cloud computing, and big data can help solve real-world problems. SASCOO’s success is a testament to Malaysian innovation and provides a blueprint for other cities globally to transform traffic management.


Developer LED Vision Transport Research Laboratory, UK Roads and Maritime Services, NSW (Australia)
Adaptive Control
Optimizes Split
Optimizes Cycle
Optimizes Offset
Cameras for Detection Partial
Loop Detector X
Central Processing Cloud-based Central computer Central computer
Green Wave Coordination
Machine Learning X X
Vehicle Tracking X X
Journey Time Data X X
Origin-Destination Data X X
Open Standard X X

*SCATS – Sydney Coordinated Adaptive Traffic System
*SCOOT – Split Cycle Offset Optimization Technique

In summary, all three systems provide adaptive signal control optimization using different architectures. SASCOO uses more advanced computer vision and machine learning techniques compared to SCOOT and SCATS. It also collects more data like vehicle tracking and origin-destination matrices. SCOOT and SCATS uses centralized processing while SCATS. SASCOO was developed by LED Vision while SCOOT and SCATS are owned by research labs/government.



Comparison table including details on the AI implementation for the Pasir Gudang and Ipoh City Council projects

Metric (Majlis Perbandaran Pasir Gudang Project Ipoh City Council Project (Majlis Bandaran Ipoh)
Number of Junctions 7 4
Duration Nov 2022 – April 2023 (5 months) Aug – Oct 2022 (3 months)
AI Cameras Used Yes, AdvanCTi Clearsight Likely used AI cameras
AI Software SASCOO AI Traffic Optimizer SASCOO AI Traffic Optimizer
AI Features Adaptive signal control, journey time calculation Adaptive signal control
Traffic Improvement 17-47% 25-30%
Queue Reduction Up to 43% Up to 37%
Key Partners LED Vision, Majlis Bandaraya Pasir Gudang LED Vision, Ipoh City Council
Connectivity Sponsor Multiple telcos Maxis


  • Both projects used AI cameras and SASCOO AI software for traffic optimization.
  • Pasir Gudang had more junctions upgraded over longer duration.
  • Pasir Gudang used additional AI features like journey time calculation.
  • Both achieved significant improvements in traffic flow and queue reduction.