{"id":180549,"date":"2023-03-08T16:51:39","date_gmt":"2023-03-08T08:51:39","guid":{"rendered":"https:\/\/www.grab.com\/sg\/?post_type=editorial&p=180549"},"modified":"2025-12-31T11:51:35","modified_gmt":"2025-12-31T03:51:35","slug":"data-science-smarts-grab-maps","status":"publish","type":"editorial","link":"https:\/\/www.grab.com\/sg\/inside-grab\/stories\/data-science-smarts-grab-maps\/","title":{"rendered":"Data science brings the smarts to Grab Maps"},"content":{"rendered":"\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t

Mapping apps have become an essential part of everyday life, from helping people find the best route for their commute, to optimising business logistics. The mapping engine that powers Grab\u2019s deliveries and transportation services makes decisions based on terabytes of data points. Building a platform that effortlessly derives simplicity from complexity is hardly a simple job; it\u2019s core to a data scientist\u2019s job.<\/p>

We sat down with Victor Liang, Grab\u2019s Head of Data Science for Geo Road and Edge Computing, who\u2019s been part of driving the company\u2019s data innovations for over six years. From ETA predictions to mapping, Victor is focused on making sense of the data and using it to refine efficiency and productivity for Grab\u2019s delivery network.<\/p>

Making data-driven decisions<\/h5>

While Grab\u2019s driver-partners use the app to navigate pickups and drop-offs, Grab is also able to collect data such as traffic situations from their activity. This helps the backend system to derive a more accurate estimated time of arrival (ETA) for a ride or delivery.<\/p>

\u201cETA is everywhere if you use our app,\u201d Liang said. \u201cThis is where we interact with our users to manage expectations. ETA is also used internally for our decision-making.\u201d<\/p>

Liang and his team analyse large quantities of data on a daily basis, in search of trends or possible areas for improvement.<\/p>

The data generated by users is then fed into various predictive models. Grab employs a data science model for every city that the company operates in. \u201cWe get input from real-time traffic, weather data, users, driver speeds, routes\u2026 The more features, the more accurate our model,\u201d he explained. \u201cThis is our daily work, how we continuously improve the accuracy of the Grab application.\u201d<\/p>

The team uses the data to derive useful, actionable insights. \u201cWe need to have an accurate map, we need to have good routing\u2026 these are all the fundamentals.\u201d<\/p>

Data and the human touch<\/h5>

All this information that\u2019s gathered is ultimately used to help the app communicate more effectively with users. \u201cBeing able to provide an ETA reduces anxiety for our passengers,\u201d Liang said. The more accurate the data, the more relaxed a passenger will be because they\u2019re confident they\u2019ll arrive at the time specified on the app.<\/p>

Data also helps Grab consider every aspect of each delivery-partner or order\u2014including (but not limited to) the weight of the food, the proximity of the consumer, and the type of vehicle most suited to the order. For example, a cake would be better suited for a motorcycle than with someone on a bicycle, he said.<\/p>

\u201cIf we see that an order is relatively light, it\u2019ll favour a delivery-partner on foot or a bicycle. A car or motorcycle might take the same amount of time, but you would have to factor in parking time, which might be longer than the trip itself,\u201d he said. \u201cWe have to treat [every use case] differently.\u201d<\/p>

14 patents and counting<\/h5>

A doctoral graduate in Computer Science from the Hong Kong Polytechnic University, Liang started at Grab when it was a much smaller start-up. Since 2016, he has been honing not only his own skills but also refining the technology that consumers have come to know.<\/p>

Some of his current projects include ETA predictions, positioning, and mapping technologies, and edge computing\u2013running machine learning models with mobile devices. \u201cIn my team, we mainly work on traffic-related projects, such as ETA predictions, real-time traffic, routings, and so on,\u201d he said.<\/p>

Inventor is not a title we often see these days, but with 14 patent applications to his name, Liang is living up to the name. \u201cWhen I joined Grab\u2026 we were less than 10 mappers. At that time\u20142016\u2014we didn\u2019t have tech [divisions]. So when we launched the Geo-tech teams, I was [one of] the pioneers.\u201d<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t

\n\t\t\t\t\"You can solve 80 per cent of problems with a very simple approach.\"<\/b>\t\t\t<\/p>\n\t\t\t\t\t<\/blockquote>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t

Of his work at Grab, he is most proud of the KartaCam technology. Built by Grab\u2019s IoT team, this is an in-house camera hardware and software that Grab uses to capture street images for Grab Maps.\u00a0<\/p>

KartaCam allows riders to take real-time photographs of routes and streets; the resulting database of images has since become a useful tool to make Grab\u2019s maps as accurate as possible. \u201cThe idea is very simple,\u201d he said. And it all has to do with instantaneity: \u201cCan we capture another image immediately? Can we do decision-making [on image quality] immediately when we get the picture?<\/p>

\u201cI never thought we\u2019d have the chance to build hardware,\u201d he added.<\/p>

In the world of tech, change is a constant. \u201cWhen I think about solutions, I try to think of different ways [to tackle a problem],\u201d he said, when asked about his own work process. \u201cMy biggest takeaway is how we can embrace change. I would say my biggest learning is how we can adapt quickly to different solutions.<\/p>

\u201cSometimes, you can solve 80 per cent of problems with a very simple approach,\u201d said Liang.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"parent":180237,"menu_order":0,"template":"grab21-default","acf":[],"_links":{"self":[{"href":"https:\/\/www.grab.com\/sg\/wp-json\/wp\/v2\/editorial\/180549"}],"collection":[{"href":"https:\/\/www.grab.com\/sg\/wp-json\/wp\/v2\/editorial"}],"about":[{"href":"https:\/\/www.grab.com\/sg\/wp-json\/wp\/v2\/types\/editorial"}],"version-history":[{"count":19,"href":"https:\/\/www.grab.com\/sg\/wp-json\/wp\/v2\/editorial\/180549\/revisions"}],"predecessor-version":[{"id":256087,"href":"https:\/\/www.grab.com\/sg\/wp-json\/wp\/v2\/editorial\/180549\/revisions\/256087"}],"up":[{"embeddable":true,"href":"https:\/\/www.grab.com\/sg\/wp-json\/wp\/v2\/editorial\/180237"}],"wp:attachment":[{"href":"https:\/\/www.grab.com\/sg\/wp-json\/wp\/v2\/media?parent=180549"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}