Road localisation in GrabMaps

Summary

Grab needed to localise nearly 30 million road segments across more than 10,000 area boundaries in Southeast Asia to attach hyperlocal attributes like language, driving side, and vehicle access rules. Testing direct geometric inclusion between complex road polylines and boundary polygons proved computationally prohibitive for daily map generation pipelines. To optimize this process, Grab introduced geohashes as rectangular proxies, precomputing geohash coverage across borders and road segments before joining the datasets in parallel. To resolve misclassification errors near borders without sacrificing performance, the team evaluated geohash coverage percentages and refined boundary-touching geohashes with polygon clipping. A laptop benchmark on a subset of data reduced runtime from 38 minutes with geometric intersection to 78 seconds using the geohash approximation.

Takeaways

1. 1

   Replacing polyline-in-polygon intersections with geohash proxy joins reduced a sample dataset's execution time from 38 minutes to 78 seconds on a laptop benchmark.

2. 2

   Roads located near border lines are susceptible to misclassification when using coarse geohashes because an entire cell is assigned to an area even if only a small fraction falls inside the boundary.

3. 3

   A hybrid precision strategy applies simple geohash matching for cells fully enclosed by an area and limits computationally heavier border-shape cutting strictly to geohashes intersecting border boundaries.