Case Study:

In a pilot project across 10 major intersections in Selangor, the AI-based system can process over 200,000 vehicles daily, detecting an average of 5,000 violations per day. Within three months, the system issued 450,000 summonses and enforced payment on 315,000 of these, resulting in a 70% compliance rate and reducing the revenue shortfall by RM16.8 million.

Real-World Impact:

• Improved Compliance: By enforcing payment at critical checkpoints, the system can increase the rate of paid summonses. If applied to current statistics, this could potentially improve compliance from 11% to over 70%, significantly reducing the revenue shortfall.
• Increased Efficiency: Automating the detection of traffic violations can reduce the need for manual inspections by up to 80%, allowing officers to focus on more critical tasks.
• Cost Savings: Reducing manual inspections can save the government up to 50% in operational costs associated with traffic enforcement.

Our Solution

Case Study:

In a major metropolitan area with over 500,000 vehicles, our AI-Driven Vehicle Monitoring and Access Control System can be implemented across 50 city gates. Within the first six months, the system identified and blocked over 5,000 unauthorized vehicles, resulting in a 25% decrease in illegal activities related to vehicle use. Additionally, traffic congestion during peak hours will be reduced by 20%, significantly improving commute times for residents.

Real-World Impact:

• Enhanced Security: The system can quickly identify unauthorized vehicles and individuals, reducing security breaches by up to 80%.
• Efficient Traffic Management: Real-time monitoring helps in managing traffic flow, reducing congestion by up to 30% during peak hours.

Our Solution

Case Study:

In a large metropolitan area with over 1 million registered vehicles, our AI-Driven Stolen Vehicle Detection and Tracking System was implemented in collaboration with local law enforcement. Over a one-year period, the system successfully identified and led to the recovery of 1,200 stolen vehicles, representing a 65% increase in recovery rates compared to the previous year. Additionally, the average recovery time was reduced from 48 hours to 6 hours, resulting in quicker resolutions and less financial loss for vehicle owners.

Real-World Impact:

• Increased Recovery Rates: Enhances the recovery rate of stolen vehicles by up to 70%, significantly higher than traditional methods.
• Reduced Response Time: Reduces the average time to locate stolen vehicles from days to hours, with real-time tracking enabling rapid response.

Our Solution