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Machine Learning
67 posts about Machine Learning. Every summary links to the original.
Grab ·
Recipe for Building a Widget: How We Helped to “Peak-Shift” Demand by Helping Passengers Understand Travel Trends
Transport demand spikes during regular commuting hours often outpace driver availability, resulting in passenger wait times and fare surges. To mitigate these imbalances, Grab created the Travel Trends Widget for its mobile feed to redistribute ride requests toward off-peak windows. The widget uses machine learning forecasting to present historical supply-demand patterns alongside pricing trends for the upcoming two hours. To handle anticipated high query rates across millions of database entries, engineers periodically load precomputed trend data into an in-memory data structure rather than querying the database per request. The feature rolled out to feeds in Singapore and Jakarta within four weeks of initial development.
Lara PuReum YimGrab ·
Understanding Supply & Demand in Ride-hailing Through the Lens of Data
Grab measures ride-hailing supply and demand across space and time to resolve geo-temporal allocation mismatches between moving drivers and ride-seeking passengers. The analytics pipeline defines supply as idle online drivers and demand as passengers checking fares within brief time slots, aggregating locations into geohashes. Each driver is mapped across neighbouring demand units and inversely weighted by straight-line distance, which yields the effective supply, supply-demand ratio, and supply-demand difference for each geographic polygon. Grab uses these aggregated metrics to identify marketplace imbalances, deploying driver heatmaps to shift excess supply and passenger travel trend widgets to defer time-insensitive ride requests.
Aayush GargGrab ·
Grab Senior Data Scientist Liuqin Yang Wins Beale-Orchard-Hays Prize
Grab Senior Data Scientist Dr. Liuqin Yang, Professor Defeng Sun, and Professor Kim-Chuan Toh received the 2018 Beale-Orchard-Hays Prize for their research paper introducing SDPNAL+. The software employs a majorised semismooth Newton-CG augmented Lagrangian method to solve large-scale semidefinite programming problems with nonnegative constraints. While traditional methods struggled beyond matrix dimensions of 2,000 and 5,000 constraints, SDPNAL+ successfully scales to matrix dimensions of 9,261 and over 12 million constraints. In benchmark testing, the software solved a problem on a desktop PC in 1.5 hours that required 122 hours on a 56-core CPU and 128-GPU cluster using a traditional solver. Grab implements these optimisation techniques to accelerate its passenger-driver allocation algorithms by hundreds of times.
Yang LiuqinGrab ·
The Data and Science Behind GrabShare Part I: Verifying Potential and Developing the Algorithm
Expanding from point-to-point dispatch services to dynamic carpooling requires matching independent passenger requests traveling in similar directions without causing unacceptable delays. Grab evaluated the feasibility of its GrabShare service by analyzing historical trip data with DBSCAN clustering on coordinates projected into a Universal Transverse Mercator system. This analysis demonstrated that 35% to 46% of rides across typical daytime windows fell into tight geographic clusters with near-identical pickup and drop-off coordinates. The resulting matching framework adapts the baseline dispatch flow by searching for in-transit drivers and enforcing real-time seat reservation constraints. Route assignment decisions subsequently evaluate detour times, trip angles, and expected arrival times to ensure driver utilization improves while total driving distance decreases.
Tang MuchenGrab ·
How to Go from a Quick Idea to an Essential Feature in Four Steps
Grab engineered an in-app messaging platform, GrabChat, to help drivers and passengers coordinate pickups across Southeast Asian markets characterized by weak 2G connectivity and high packet loss. The team developed an in-house TCP messaging architecture consisting of a TCP gateway named Gundam and a message dispatcher named Hermes connected to internal backend services over HTTPS. To protect backend server resources from resend loops during poor connection states, the communication protocol adopts a "server only push once" model that delegates retry handling to the client. Data science evaluations using a pre-trained cancellation prediction model confirmed that GrabChat adoption correlated with reduced booking cancellations. Following early usage feedback, the team further iterated on the feature by introducing pre-written message templates to reduce driver distraction on the road.
Da HuangGrab ·
Battling with Tech Giants for the World's Best Talent
Grab maintains research and development centres across Singapore, Seattle, and Beijing to develop mobility and transaction platforms for Southeast Asia. Engineer Brandon Gao turned down an offer from a major Seattle-based technology corporation to remain at the company due to rapid growth and high engineering impact. During backend infrastructure changes starting in May 2015, Grab transitioned core services from Node.js and Ruby to Golang. A weekend Golang prototype created by Gao evolved into the primary data service linking backend servers to 580,000 drivers across the region. Additionally, his initial work on the User Trust team established a machine-learning risk and fraud detection system supporting large-scale mobile transaction volumes.
Grab EngineeringGrab ·
GrabPay Wins Best Fraud Prevention Innovation at the Florin Awards
Grab won the Best Fraud Prevention Innovation (Community Votes) Award at the 2016 Florin Awards for its GrabPay platform. To support secure cashless transactions across Southeast Asia, Grab deployed a dedicated risk and fraud detection engine when launching GrabPay earlier in the year. The system utilizes machine learning algorithms that continually evolve by analyzing driver, passenger, and travel pattern data across massive transaction volumes. Furthermore, Grab provides full financial protection by covering any unauthorized fraudulent transactions for both passengers and drivers. At the time of the announcement, the platform supported up to 1.5 million daily bookings and exceeded 23 million app downloads.
Foo Wui Ngiap