Mapping the most convenient ways to meet someone with public transit
Details
- External ID
- 47503693
- Source
- HN
- Company
- —
- Product
- Mapping the most convenient ways to meet someone with public transit
- Website domain
- commutometer.com
- Launched
- March 24, 2026
- Cohort
- —
- Upvotes
- 16
- Upvotes percentile
- 0.6968019680196802
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:26 p.m.
- Updated at
- Sept. 7, 2026, 9:26 p.m.
Description
Hello!I built a map that generates a heatmap of the most convenient meeting spots between several people, based on public transit travel times.Move the markers anywhere in the map; all cities with sufficient transit data are supported!
Enrichment
- Theme
- meeting scheduling and notetaking tools
- Vertical
- Travel & hospitality
- Function
- Search & retrieval
- Audience
- B2C
- AI stance
- AI feature
- Project type
- Commercial product
- Normalized one-liner
- public transit route planner for meeting up
- Manually corrected
- False
Could you build this?
Partial While rendering maps and heatmaps in frontend libraries is simple, computing multi-origin isochrones across real-world transit networks requires high-performance routing engines and heavy GTFS data processing.
What it would actually take: The stack would involve Leaflet/Mapbox on the frontend with a specialized transit routing backend like OpenTripPlanner, Valhalla, or custom RAPTOR implementations. Generating multi-origin heatmaps involves computing many-to-many travel times over full GTFS schedules, which quickly creates combinatorial scaling issues without efficient graph algorithms. Processing and caching national/global GTFS datasets in memory requires transit data pipeline engineering.
Discussion
2 comments analyzed.
Concerns raised: Infinite loading when insufficient data coverage, Limited transit data availability in some regions
Feature requests: Add warning when area lacks sufficient routing data
Competitors
Other products that read as similar to this one — 57 launches clear the similarity bar, closest 8 shown.
Attention rank: #22 of 58 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 103 days after the earliest competitor.
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Other launches for this product
- No other launches for this product.