Grab and MDEC collaborate on Malaysia City Brain initiative
By Digital News Asia April 16, 2018
- Data generated daily by Grab’s drivers to will enhance urban planning and traffic management
- Information from video feeds, traffic lights and social media will also be used to teach the AI
GRAB, Malaysia’s and Southeast Asia’s leading on-demand transportation and fintech platform, and the Malaysian Digital Economy Corporation (MDEC), have formalised a memorandum of understanding for the development of the Malaysia City Brain initiative.
First announced in January this year, the Malaysia City Brain project is an MDEC-led smart city traffic management system that uses big data, artificial intelligence and cloud computing to help the fast-growing city of Kuala Lumpur better manage its urban transportation needs.
“Grab’s core mission is to help provide safe, efficient, convenient and affordable mobility solutions to cities in like Kuala Lumpur. By working with strategic partners like MDEC and the government of Malaysia, we can offer the Malaysia City Brain effort our expertise from across the region, along with the vast anonymised data that is generated daily by our drivers,” said Grab Malaysia country head Sean Goh.
This will help provide urban planners and traffic managers greater success in resolving key transportation issues, enhance emergency services response times and reduce congestion. It also allows better insights in addressing the growing transportation demands and urban transport planning of one of the leading digitally enabled capital cities in South East Asia.
According to the Moving SEA Together report, Grab influences the travel decisions of 2.5 million Southeast Asians daily. As Malaysia’s and Southeast Asia’s largest transport network, it is a key source for efficient and accurate data that represents real drivers and city dwellers in everyday conditions.
Grab’s real-time, anonymised traffic data will include traffic speeds and travel times for popular Kuala Lumpur routes. This will be coupled with existing traffic information sources such as video feeds from 500 CCTV cameras and 300 traffic lights under Kuala Lumpur City Hall, social media feeds, and traffic information from local traffic agencies and government sources to provide city planners a more comprehensive view of conditions on the roads.
When processed using cloud computing and AI-based technologies, it is expected that the Malaysia City Brain will be able to identify potential traffic challenges, and develop better predictive modeling and more efficient management of city traffic in real time.
Grab’s involvement in various Malaysian public-private initiatives in the past has been successful, including the collaboration between Malaysia SEA games Organising Committee (MASOC) and Grab during the 2017 SEA Games. Other than Grab ensuring safe, convenient and comfortable rides, the overall effect of mitigating traffic congestion during the games was met.
Traffic and congestion in Malaysia’s capital city are critical considerations; according to the World Bank’s 2015 Malaysia’s Economic Monitor report, about five million people get stuck in traffic in the Klang Valley every day. These Malaysians spend 250 million hours a year stuck in traffic, costing the country 1.1% to 2.2 %of its GDP in 2014. This has likely become even more costly in the years since.
“We are delighted that Grab is working with us to further build on the success of Malaysia City Brain, particularly as this high impact initiative to catalyse the nation’s AI ecosystem is vitally dependent on the richness and variety of its data sources.
“No two cities are the same, and the sharing of such data resources by Grab will also help our city planners to better understand the unique transportation needs of each city and neighbourhood. This will helping us to better deal with using the roads more effectively in real time, and it will gradually help us to improve our daily quality of life index. Working together, we will continue to move Malaysia ahead,” said MDEC’s director of data economy Dr Karl Ng Kah Hou.