You open a cab app.
Destination:
2 km away.
Expected fare?
Maybe ₹70.
Actual fare?
₹487
At that point…
Walking starts looking premium. 😅
So I turned this into a coding challenge.
And yes…
This is a real system problem.
🚨 The Problem
Basic fare logic sounds easy:
- Calculate distance fare
- Add surge pricing
- Add weather charges
Done.
Right?
Not really.
⚠️ What Goes Wrong?
In real ride apps:
• Demand spikes suddenly
• Driver supply drops
• Surge multipliers stack badly
• Weather fees pile up
And suddenly:
👉 A short ride costs like airport travel.
Users feel pricing is random.
Sometimes… it almost is.
🧠 What I Observed
When exploring this:
- Basic solutions handled formula math
- Many ignored pricing caps
- Some missed fairness logic
- Very few thought about trust in pricing
The code works.
But users feel robbed 😭
🔍 The Real Issue
This is not just fare calculation.
It’s about:
• Dynamic pricing
• Fairness
• Supply-demand modeling
• User trust
Because:
👉 Correct math can still produce terrible pricing.
💡 What Better Systems Need
A smarter fare engine should include:
- Surge caps
- Fairness constraints
- Dynamic but bounded pricing
- Transparency rules
- Prediction smoothing
This ensures:
👉 Fewer pricing shocks
👉 Happier users
👉 Less rage-closing the app
🔥 Try My Challenge
I turned this into a challenge on VibeCode Arena.
👉 Try it here:
https://vibecodearena.ai/duel/bcde820c-cf40-432b-ae57-7df3b3d51d13
Can you:
- Fix crazy surge pricing?
- Prevent absurd fares?
- Build a fairer pricing system?
🎯 Final Thought
Some algorithms optimize systems.
Some optimize users into walking.
This one…
👉 Might be doing both. 😭
Tell me honestly:
What’s worse?
Surge pricing…
or seeing fare increase while you're watching it? 👀
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