From gridlock to green light: How can AI transform Philippines’ traffic

 

Manila's traffic congestion is amongst the world's worst, significantly impacting the city's economy and the quality of life for Filipinos. In 2023, it held the unenviable title of the world's most congested metro area according to the TomTom Traffic Index.

 

The daily struggle is a constant reality, requiring innovative solutions.

Driving change with new technology for road traffic in the Philippines

 

The Road Safety Action Plan (RSAP) in the Philippines was officially launched in 2017 as part of the government's commitment to improving road safety and addressing the high rates of traffic-related injuries and fatalities. It serves as a framework for the Philippines government to implement a series of strategies aimed at enhancing road safety.

 

With the rise of artificial intelligence (AI), how can this transformative technology play a role in advancing the government’s objectives?

 

Here are three possible ways how AI can help the Philippines move towards a more efficient, responsive, and safer transportation system:

 

1.    Intelligent traffic surveillance: Seeing beyond the gridlock

 

Current methods of monitoring traffic rely heavily on manual observation and often miss crucial details.

 

Using machine learning, vision AI can accurately count and classify vehicles, providing a real-time comprehensive picture of traffic flow. The data-driven insights will enable proactive interventions to prevent accidents and alleviate congestion.

 

For example, a city like Montreal, once struggled with severe congestion, particularly around its port, drastically improved its traffic flow using Fujitsu's AI-enabled data analysis platform to optimize the management of 2,500 traffic lights. This solution enables the city to make data-driven decisions to streamline traffic flows, lower fuel consumption and reduce air pollution. This is a success story that shows the potential of Vision AI for tackling Manila’s traffic challenges.

 

2.    Rapid incident response: Seconds that save lives

 

In a traffic emergency, rapid response is critical. Traditional methods often react too slowly, leading to escalating accidents and widespread gridlock.

 

AI-powered systems can automatically detect incidents on the road – accidents, stalled vehicles, or even unusual pedestrian behaviors – and instantly alert traffic operators. This will dramatically improve response times, enable quicker intervention, minimize disruption and potentially save lives.

 

Japan's highway system provides a compelling example of how AI can help contribute directly to the government’s objective of safer roads. Fujitsu's AI-powered traffic management system has significantly improved highway safety, especially during severe weather, by providing real-time data that allows for preemptive accident prevention.

 

3.    Advanced traffic forecasting: Seeing beyond the obvious

 

Traditional systems often struggle to analyze complex traffic conditions in real-time, particularly in areas with limited visibility.

 

AI's advanced modelling capabilities can overcome these limitations. By analyzing historical data and real-time feeds, AI can predict potential bottlenecks and congestion hotspots, enabling proactive alterations to traffic signals and infrastructure planning.

 

Fujitsu and Carnegie Mellon University's collaboration on AI-powered social digital twin technology in Pittsburgh, USA, demonstrates this potential. The system captures highly detailed real-time traffic images, identifying potential hazards even in blind spots, resulting in a substantial reduction in accidents in the city of Pittsburgh.

 

We believe that an AI-powered traffic management requires a three-pronged approach

 

Unlocking AI's full potential requires building a robust data collection system, a scalable cloud infrastructure, and a real-time alert system. This strategic approach will empower organizations to make data-driven decisions instantly.

 

·         Robust data collection: Deploying a strategically planned network of high-resolution cameras and sensors across key areas in the Philippines will provide the foundational data for AI-driven insights.

·         Scalable cloud architecture: A robust and scalable cloud infrastructure will be implemented to efficiently process the huge amount of data generated by the sensor network. This architecture will ensure the system capacity keeps pace with future growth and technological advancements.

·         Real-time actionable response systems: Seamless integration of AI-driven insights with existing and future traffic management systems will enable real-time, adaptive responses, optimizing traffic flow and enhancing safety.

 

With these elements in place, decision makers can harness the power of AI to transform the country’s approach to road safety. This will help create a more efficient, safer, and responsive transportation system, achieving the objectives of the government’s Road Safety Action Plan (RSAP) and improving the lives of all Filipinos.