Why Dynamic Pricing Matters?
From Uber surge pricing to airline ticket prices, dynamic pricing is everywhere. The challenge? Building a system that adjusts prices in real-time based on demand, location, and external factors.
Let’s see if you can design a real-time pricing engine like Uber!
🏎️ Challenge #1: Implement Surge Pricing Based on Demand
The Problem
Your ride-sharing app needs to increase ride prices when demand is high and decrease them when demand drops.
The Solution
1️⃣ Track active ride requests in different locations.
2️⃣ Set surge rules (e.g., if demand is 2x the available drivers, increase price by 1.5x).
3️⃣ Calculate real-time fares based on demand levels.
💡 Bonus Challenge: Implement a cool-down period so prices don’t fluctuate too fast.
💰 Challenge #2: Predict Prices Using Traffic & Weather Data
The Problem
Pricing should adjust based on real-world conditions—bad weather or heavy traffic should increase fares.
The Solution
1️⃣ Fetch traffic & weather data from an external API.
2️⃣ Assign weight factors (e.g., +20% fare in heavy rain, +15% during peak traffic).
3️⃣ Integrate this into your pricing algorithm to adjust fares dynamically.
💡 Bonus Challenge: Use historical ride data to predict optimal fare adjustments for different cities.
Final Thoughts
Dynamic pricing isn’t just about raising prices—it’s about:
✅ Balancing demand & supply dynamically
✅ Using real-time data to make smart pricing decisions
✅ Ensuring fairness for riders & profitability for drivers
🚀 Want more challenges like this? Start learning here 👉 Backend Challenges
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