Getting Ready: Ride-Sharing Service

Uber processes over 20 million rides per day across 70+ countries. Every ride involves a real-time dance between three parties — rider, driver, and platform — coordinated by algorithms that match supply to demand in seconds. Behind the simple "Request Ride" button lies one of the most complex object-oriented systems in the industry.

Every major tech company — Uber, Lyft, Grab, Ola, DiDi — builds some variation of this system. It's also one of the most frequently asked LLD interview problems at companies like Google, Amazon, Meta, and of course Uber itself. Interviewers love it because it tests a wide range of design skills simultaneously: state management (ride lifecycle), algorithmic thinking (driver matching, surge pricing), multiple actors with different capabilities (rider vs. driver vs. admin), and real-time event propagation.

What makes ride-sharing architecturally interesting is the multi-stakeholder coordination under real-time constraints. A rider requests a ride, the system must find the best available driver within seconds, the driver can accept or reject, and once matched, both parties track the ride through a shared state machine until completion. Add surge pricing that adjusts dynamically based on supply/demand, multiple vehicle types with different rate structures, and a rating system that feeds back into driver eligibility — and you have a design problem that exercises nearly every OOP and design pattern concept in our curriculum.

Through this 10-tutorial design problem, you will:

• Model a multi-actor system with Rider, Driver, and Admin — each with distinct capabilities and workflows
• Apply the State Pattern to manage the complete ride lifecycle: Requested, Matched, InProgress, Completed, Cancelled
• Implement the Strategy Pattern for pluggable matching algorithms (nearest-driver, highest-rated) and pricing strategies (base rate, surge multiplier)
• Use the Observer Pattern for real-time ride status notifications — riders and drivers subscribe to ride state changes
• Design a location-based matching algorithm that finds the best available driver for a ride request
• Build a surge pricing engine that dynamically adjusts fares based on the ratio of active ride requests to available drivers
• Create four UML diagrams — Use Case, Class, State Machine (ride lifecycle), and Sequence (request-a-ride flow) — each derived from requirements
• Write complete Python + Java implementations with realistic driver examples covering normal rides, cancellations, surge pricing, and rating updates

Before starting this problem, make sure you're comfortable with:

• OOP Relationships — Composition, Association, and Dependency (Fundamentals S4). The Ride object composes a route; the RideService associates with Riders and Drivers.
• State Pattern — How objects change behavior based on internal state (Fundamentals S12). This is the central pattern: a Ride transitions through 5 distinct states, each with different allowed operations.
• Strategy Pattern — Swappable algorithms behind a common interface (Fundamentals S12). We use it for both driver matching and fare calculation.
• Observer Pattern — Event-driven notification (Fundamentals S12). Riders and drivers receive real-time updates as ride state changes.
• UML Diagrams — Class, Sequence, State Machine, and Use Case diagrams (Fundamentals S5).
• Inheritance and Polymorphism — (Fundamentals S3). Vehicle types form a hierarchy; pricing strategies are polymorphic.

If you've completed the Medium problems (DP8-DP18), you're well-prepared. If the State Pattern is unfamiliar, revisit the Vending Machine (DP6) or ATM (DP11) first — they use the same pattern with simpler state machines.

We will design: The core domain model for a ride-sharing service — riders, drivers, vehicles, ride lifecycle, matching, pricing, and ratings. This is the object model that would power the backend service.

We will NOT design:

• GPS/maps integration or actual geolocation hardware — we model locations as simple latitude/longitude pairs
• Real-time tracking infrastructure (WebSockets, push notifications) — we model the notification interface, not the transport layer
• Payment gateway integration — we model the fare calculation and payment interface, not Stripe/PayPal internals
• Database schemas, REST APIs, or microservice architecture — this is a Low Level Design (OOP) exercise, not a System Design (infrastructure) problem
• Driver onboarding, background checks, or document verification — administrative workflows outside core ride flow

Difficulty: Hard. This problem has 10+ classes, 3-4 design patterns interacting, a non-trivial matching algorithm, and a multi-state lifecycle. It's a significant step up from Medium problems in terms of the number of moving parts and the interplay between patterns.

Before we design anything, let's understand the real-world flow:

1. Rider requests a ride. The rider opens the app, enters a pickup location and destination, selects a ride type (Economy, Premium, XL), and sees an estimated fare. The fare includes a base fare, per-mile charge, per-minute charge, and potentially a surge multiplier if demand is high.

2. System matches a driver. The platform searches for available drivers near the pickup location. The matching algorithm considers distance, driver rating, vehicle type compatibility, and current traffic. This must happen within 10-15 seconds or the rider loses patience.

3. Driver accepts or declines. The matched driver receives a notification with the ride details (pickup, destination, estimated earnings). They have a short window (15-30 seconds) to accept. If they decline or the timer expires, the system tries the next-best driver.

4. Ride in progress. Once accepted, the driver navigates to the pickup location. The rider sees the driver's real-time location. When the driver arrives, they confirm pickup. The ride is now in progress — the meter runs, tracking distance and duration.

5. Ride completes. The driver marks the ride as completed at the destination. The system calculates the final fare (which may differ from the estimate if the route changed), charges the rider, credits the driver (minus the platform's commission), and prompts both parties to rate each other.

6. Cancellation paths. Either party can cancel before pickup. A rider who cancels after the driver has traveled significantly may incur a cancellation fee. A driver who cancels too often may face rating penalties or temporary deactivation.

Key engineering challenges we'll tackle:

• State management: A ride passes through 5+ states with strict transition rules — you can't complete a ride that hasn't started
• Matching under constraints: Finding the "best" driver involves multiple factors — distance, rating, vehicle type — and must handle the case where no drivers are available
• Dynamic pricing: Surge pricing must balance rider demand with driver supply, but shouldn't be so aggressive that riders never request rides
• Multi-actor coordination: Rider and driver have different views of the same ride, different allowed actions at each state, and both need real-time updates