Refreshing the Travel-Time Map Behind Lyft’s Marketplace: Rebuilding Neighborhood Reachability…

Summary

Lyft’s Neighborhood Reachability Signals dataset supplies precomputed travel-time estimates between geohash-6 cells and a vocabulary of usable cells for pricing, driver heatmaps, and marketplace features. Production ETAs had remained largely based on 2018–2019 data, systematically underestimating travel times and omitting genuinely matchable pairs, while consumers maintained separate allowlists and denylists. The FORTOP team redesigned the Airflow-generated files by rebuilding the geohash universe from demand and supply history, estimating dense pairs from trip history, using Routing Simulation System (RSS) fallbacks for sparse pairs, and blending partial observations. In the second half of 2025, the refreshed dataset was adopted by Pricing after validation, with reported improvements in accuracy and completeness, a six-month refresh cadence, and plans for nine time-category files per region to represent traffic patterns.

Takeaways

1. 1

   The legacy geohash universe could include non-drivable cells, including cells over open water in the SFO region, while excluding pairs that were physically close enough to match.

2. 2

   Pairs with fewer than a handful of observations use Routing Simulation System estimates that incorporate traffic conditions; partially observed pairs receive a weighted blend of historical and simulated ETAs.

3. 3

   The planned time-aware version uses nine files per region, allowing consumers to load the current hour-of-week category while keeping read latency roughly flat at the cost of a couple of extra gigabytes of memory.