What API and data model would support a new Rides workflow for commuters at global scale

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 new Rides workflow designed specifically for commuters who travel repeatedly between predictable locations, such as home, office, transit hubs, campuses, or business districts. The workflow must support global usage patterns, including different commute schedules, city regulations, pricing models, pickup constraints, payment methods, safety expectations, and marketplace supply conditions.

You are being asked to think as a Technical PM: define what API and data model would be needed to support this commuter-focused rides experience at Uber scale. The emphasis is not on UI design, but on how rider intent, trip preferences, scheduling, matching, pricing, driver availability, notifications, reliability, privacy, and operational controls would be represented and exchanged across systems.

Assume this workflow must integrate with existing Rides capabilities while allowing new commuter-specific behavior. It should be resilient to high-volume peak commute periods, regional differences, marketplace volatility, and safety or compliance requirements. Your answer should clarify the product requirements that drive the technical design and the trade-offs between flexibility, simplicity, latency, reliability, and future extensibility.

The experience should consider:

- Core user entities and data objects, such as commuter profiles, saved routes, recurring schedules, pickup/dropoff preferences, eligibility, trip state, payments, and consent settings.

- API surfaces needed for creating, updating, previewing, booking, modifying, canceling, and tracking commuter rides or commute plans.

- How the workflow handles real-time and scheduled demand, pricing estimates, matching constraints, driver supply, surge, cancellations, and fallback states.

- Data privacy, safety, and security considerations, especially around home/work locations, commute routines, identity, payment data, and location history.

- Reliability requirements for peak commute windows, idempotency, retries, state transitions, service degradation, and cross-region availability.

- Instrumentation and observability needed to monitor API health, marketplace outcomes, user experience, latency, error rates, and operational incidents.

- Rollout considerations across cities and countries, including experimentation, backwards compatibility, localization, compliance, and integration with existing Uber systems.

The goal is to demonstrate how you would translate a commuter Rides product concept into a scalable technical architecture: clear requirements, well-scoped APIs, a robust data model, thoughtful system boundaries, and product-aware trade-offs suitable for a global mobility marketplace.