How We Harnessed the Wisdom of Crowds to Improve Restaurant Location Accuracy

Summary

Grab discovered that abnormally short driver wait times often indicated restaurants registered at incorrect coordinates due to moves or onboarding errors. To fix this, Grab used driver-partner GPS pings, timestamps, and order status updates to infer true food collection locations. The system cleans the data by filtering low-quality GPS pings and isolating the longest temporal streak a driver spends within a predefined radius of the venue. Clusters of inferred pick-up points are then ranked by order volume, the proportion of off-target pick-ups, and median distance errors before routing to mapping operations for verification. This periodic correction workflow achieved a fivefold reduction in order cancellations caused by unfound merchant locations.

Context

Restaurants in GrabFood were sometimes registered at incorrect coordinates due to merchant relocations or onboarding human errors, causing reduced customer visibility, inaccurate ETAs, and driver order cancellations.

Approach / What changed

Grab combined driver GPS pings and order status timestamps to infer actual pick-up points, filtering noisy GPS signals and isolating the driver's longest streak within the registered radius to generate a prioritized list of misplaced restaurants for mapping operations to verify.

Takeaways

• Defining a driver as present at a restaurant by using only the longest continuous streak within the registered radius prevents false pick-up signals caused by drivers merely passing by.
• Candidate restaurants for relocation verification are ranked based on order volume, fraction of pick-ups occurring away from the registered point, and median distance error.
• Applying crowd-derived location updates resulted in a 5x decrease in GrabFood order cancellations caused by drivers being unable to find restaurants.

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