GrabNeeraj MishraAnnouncement
Guiding You Door-to-Door via Our Superapp!
Summary
Grab addressed passenger navigation challenges at large Southeast Asian venues such as airports and shopping centers. Satellite signals weaken through concrete and steel, creating GPS inaccuracies that caused the rendezvous distance between passengers and drivers at large venues to exceed twice the average. While introducing Entrances previously mapped over 120,000 green dots to lower rendezvous distances, passengers still struggled to locate specific pickup spots indoors. The company launched Venues, an in-app feature delivering turn-by-turn text and photo directions to designated pickup points. To support this system, operations teams surveyed sites with cameras and scanners to capture landmarks, after which in-house teams masked faces and vehicle license plates.
Context
GPS satellite signals weaken when passing through concrete and steel, causing location inaccuracies inside large buildings or skyscraper districts where rendezvous distances between passengers and waiting cars were more than twice the platform average.
Approach / What changed
Grab introduced the Venues feature, which displays available pickup points in a menu and provides in-app turn-by-turn text and photo directions based on on-ground surveys using cameras, Wi-Fi, and Bluetooth scanners, accompanied by image masking of faces and license plates.
Takeaways
Rendezvous distance between passengers and drivers at large venues was more than double the Grab average before targeted pickup point interventions.
The earlier Entrances feature established over 120,000 mapped green dots representing distinct building pickup locations to reduce rendezvous distance.
Creating the Venues feature required surveying 600 key venues to collect over 3,400 images across more than 1,900 pickup points, requiring over 3,000 man-hours and privacy masking of faces and number plates.
Source and freshness
- Source
- engineering.grab.com
- Published
- Added to Yomu
- Last updated
Summaries are generated from the original by a language model. Check the original before relying on it.
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