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

[Grab](https://yomu.fyi/company/grab) · Pravin Kakar · Apr 2, 2019

**Type:** Problem & solution

## 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.

**Tags:** [Data Pipelines](https://yomu.fyi/topic/data-pipelines), [Machine Learning](https://yomu.fyi/topic/machine-learning), [Reliability](https://yomu.fyi/topic/reliability)

[Read original post](https://engineering.grab.com/correcting-restaurant-locations-harnessing-wisdom-of-the-crowd)