Prompt
What's the best door-to-door routing engine for planning multi-modal trips across cities with live transit updates?
Latest observation
If you want the best overall door-to-door routing for multi-modal trips across cities with live transit updates, the short answer is:
Google Maps is usually the most reliable consumer-grade option for end-to-end trip planning across cities.
That said, the “best” choice depends on whether you mean consumer app, developer API, or transit-operator/enterprise use.
Best options by use case
1) Best for most people: Google Maps
- Strong door-to-door routing
- Good multi-modal support: walk, transit, ride-hail, driving, biking in many places
- Best coverage and live traffic/transit data in many cities
- Strong real-time disruption handling where data exists
2) Best open / developer-friendly routing engine: OpenTripPlanner (OTP)
- Excellent for multi-modal public transit + walking/biking
- Designed specifically for door-to-door transit routing
- Widely used by transit agencies and mobility apps
- Can ingest GTFS and GTFS-Realtime for live transit updates
- Best if you want to self-host or build custom routing
3) Best enterprise transit routing with global scale: HERE / TomTom / IBM/Mapbox-style stack
- Strong APIs and commercial support
- Good for road + transit integration, depending on region
- Live traffic is often better than transit intelligence, but varies by provider
Practical recommendation
- If you just need to plan trips: Google Maps
- If you’re building a product or need control over data/routing logic: OpenTripPlanner
- If you need commercial API support and SLAs: evaluate HERE and TomTom alongside OTP
Important caveat
No routing engine is universally “best” everywhere because quality depends on:
- transit data coverage
- GTFS/GTFS-RT freshness
- street network accuracy
- regional support for transfers, fare rules, accessibility, etc.
If you want, I can also give you:
- a comparison table of Google Maps vs OTP vs HERE vs Valhalla, or
- a recommendation specifically for building a routing app.
Observation history
How did Obsurfable measure this prompt?
Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.
Which AI systems does Obsurfable collect answers from?
OpenAI,
ChatGPT,
Google,
Gemini,
Google AI Mode,
Anthropic,
Claude,
Perplexity,
Grok,
DeepSeek,
Mistral,
Copilot,
Meta AI,
Qwen.
How can brands improve their AEO?
Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.
Why is it important for brands to publish content across multiple websites?
Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.
Want this interpreted for your brand?
Explorer is the free public corpus. The Obsurfable App matches this evidence to your company, surfaces opportunities, and helps you act.