Grab and Malaysian Digital Economy Corporation (MDEC), formalised a Memorandum of Understanding (MoU) for the development of the Malaysia City Brain initiative.<\/p>\n
First announced in January, the Malaysia City Brain project represents the development of an MDEC led smart city traffic management system, which uses big data, artificial intelligence and cloud computing to help the fast growing city better manage its urban transportation needs.<\/p>\n
\u201cGrab\u2019s core mission is to help provide safe, efficient, convenient and affordable mobility solutions to cities in like Kuala Lumpur.\u00a0<\/p>\n
\u201cBy 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.\u00a0<\/p>\n
\u201cThis will help provide urban planners and traffic managers greater success in resolving key transportation issues, enhance emergency services response times, and reduce congestion,\u201d said Sean Goh, country head of Grab Malaysia.<\/p>\n
He said this 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.<\/p>\n
According to the Moving SEA Together report, Grab influences the travel decisions of 2.5 million Southeast Asians daily.\u00a0<\/p>\n
As Malaysia and Southeast Asia\u2019s largest transport network, it is a key source for efficient and accurate data that represents real drivers and city dwellers in everyday conditions.<\/p>\n
Grab\u2019s real-time, anonymised traffic data will include traffic speeds and travel times for popular Kuala Lumpur routes.<\/p>\n
This will be coupled with existing traffic sources like 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.<\/p>\n
When processed using cloud computing and Artificial Intelligence based technologies, it is expected that the Malaysia City Brain will be able to identify potential traffic challenges, develop better predictive modeling, and more efficient management of city traffic in real time.<\/p>\n
Traffic and congestion in Malaysia\u2019s capital are critical considerations, as according to the World Bank\u2019s 2015 Malaysia\u2019s Economic Monitor report, it was estimated that about five million people get stuck in the Klang Valley traffic every day.\u00a0<\/p>\n
They spend 250 million hours a year stuck in traffic, costing the country 1.1 to 2.2 percent of its GDP in 2014 but this has likely become even more costly in the years since.<\/p>\n
\u201cWe 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\u2019s Artificial Intelligence (AI) ecosystem is vitally dependent on the richness and variety of its data sources.\u00a0<\/p>\n
\u201cNo 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.\u00a0<\/p>\n
\u201cThis 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!\u201d said Dr Karl Ng Kah Hou, director of Data Economy at MDEC.<\/p>\n
Grab\u2019s involvement in various Malaysian public-private initiatives in the past have been successful including the collaboration between Malaysia SEA games Organising Committee (MASOC) and Grab during the 2017 SEA Games.\u00a0<\/p>\n
Other than Grab ensuring safe, convenient and comfortable rides, the overall effect of mitigating traffic congestion during the games was met.\u00a0<\/p>\n
\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"
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