What API and data model would support a new Transit workflow for commuters

Problem Statement Description

Product context: Uber is a mobility and delivery platform; its products include rides, Uber Eats, grocery and retail delivery, freight, driver and courier tools, and marketplace pricing.

Uber is exploring a Transit workflow for commuters who use public transportation as part of their daily trips. The experience may need to help riders discover transit options, combine transit with Uber rides for first-mile or last-mile needs, understand timing and reliability, and make informed decisions during peak commute windows.

As a Technical PM, your task is to describe the API and data model that would support this workflow. Focus on the underlying product and technical requirements needed to represent commuters, routes, stops, schedules, real-time updates, trip plans, pricing or fare information where relevant, and integrations with external transit agencies or mobility partners.

Your response should stay at the system-design and product-requirements level. You do not need to write code, but you should be clear about the core entities, relationships, request/response patterns, data freshness needs, reliability expectations, privacy considerations, and trade-offs required to make the workflow useful inside Uber’s broader mobility marketplace.

The experience should consider:

- The commuter journey from origin selection through transit discovery, trip planning, live updates, and completion.

- Core data entities such as riders, locations, stops, routes, agencies, schedules, service alerts, vehicle arrivals, and multimodal trip legs.

- APIs needed for search, route planning, real-time arrival estimates, trip status, alerts, and handoff between transit and Uber ride options.

- External data dependencies, including public transit feeds, real-time location data, outages, accessibility information, and regional variability.

- Reliability, latency, caching, fallback behavior, and how stale or missing transit data should be handled.

- Privacy and security considerations for commuter location, trip history, saved routines, and third-party integrations.

- Observability requirements such as data freshness, API success rates, latency, alert accuracy, and user-facing error states.

- Product trade-offs between completeness, accuracy, speed, regional launch complexity, and operational maintainability.

The goal is to define a practical API and data model foundation that can support a commuter-focused Transit experience at Uber, while making clear what must be true technically for the workflow to be reliable, safe, scalable, and useful for everyday riders.