The candidate logically breaks down the estimate by analyzing demand during peak hours, user behavior, and ride dynamics.

Step-by-step breakdown:

✅ Assumptions:

• Pre-COVID scenario

• Focusing only on Uber ride-hailing (cars, bikes, autos)

• Targeting peak hours: 8 AM – 11 AM

1. Target Population

Total Bangalore population: 13M

Focus age group (20–50): ~5M (based on age distribution: 30/80)

2. Tech-Enabled User Base

Assuming 80% smartphone + internet penetration:

4M potential users

3. Users Who Can Afford Cabs

25% of 4M = 1M

4. Users Likely to Use Ride-Hailing

Only 20% might actually use cabs (others take metro/bus/own vehicles):

0.2 x 1M = 200K

5. Uber’s Market Share

Uber holds ~40% of the ride-hailing market:

80K Uber users during peak hours

6. Ride Duration Assumption

In peak hours, 1 ride ≈ 45 mins (includes wait time)

→ Each driver does ~4 rides in 3 hours (0.75 rides/hour)

7. Drivers Needed to Serve Demand

To handle 80K rides in 3 hours:

= 25K rides/hour

= 25K / 0.75 ≈ 33.3K drivers

Rounded down: ~30,000 Uber drivers in Bangalore

⸻

✅ This approach blends demographic filtering, behavioral insights, market share, and operational feasibility. A textbook guesstimate!

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