OLA, Rapido, Uber Sudden Price Surge Happens Like This

Ola’s 2.5x surge isn’t aimed at you.

It’s aimed at the drivers.

I only understood this after getting caught in the rain last week and going down a rabbit hole about how surge pricing actually work.

The moment rain hit, thousands of people in my area would have opened Ola simultaneously.

Ola doesn’t see “it’s raining in Chennai.”

It sees this:

Zone: Pallavaram Requests: 47 Available Drivers: 6

Ratio: 7.8 Surge: 2.5x

Every zone in the city has its own number. Updated every 30 seconds independently.

The surge didn’t come from a person making decision.

It came from a formula:

requests / drivers = ratio -> ratio maps to multiplier.

Ratio above 5? You’re in 2.1x territory. Ratio above 8? 2.5x. Zero drivers in the zone? Hard cap 3.0x.

Here’s the plot twist I didn’t expect:

The surge wasn’t aimed at customers. It was aimed at the drivers.

The moment 2.5x appeared, every driver within 3 zones saw their app heat map go red.

Red colour denotes more money.

Drivers started moving toward Pallavaram.

Supply increased, Ratio dropped, Surge came down.

That’s why it went from 2.5x to 2.3x while I was waiting.

I thought I was outsmarting the algorithm by waiting but the algorithm was already fixing itself.

And this is how driver matching happens:

Multiple factors scored per candidate driver:

• Distance to you
• Driver rating
• Direction of travel (heading side)
• Trip acceptance rate
• Predicted ETA accuracy

All collapsed into one score. Highest score gets the ride request first.

ETA Prediction:

Same Dijkstra + real-time traffic weight as Zomato’s engine.

But Ola adds few more parameters: Driver’s historical speed on that route, time of the day, road type (highway vs lane).

ML model corrects the graph estimate using millions of past trips on that exact stretch.