Uber has transformed the way we think about going from X to Y. Imagine dominating one of the biggest sectors of the US economy, without any significant working capital or inventory in just five and a half years!

The Uber app has some amazing features such as knowing when and where you will be picked up, tracking your driver’s ETA and progress, estimating your fare and paying automatically.

But no matter how great these features are, one of the prime challenges that the company used to dread in the early days was the unavailability of the drivers when customers demanded for a cab!

What is Surge Pricing?

“You want supply to always be full, and you use price to basically either bring more supply on or get more supply off, or get more demand in the system or get some demand out. It’s classic Econ 101.” — Travis Kalanick, Founder of Uber

While booking an Uber on a Saturday night in Bandra (Mumbai) or Kormanagala(Bangalore), you might have noticed that the cab fares to your home being a bit higher than the normal fares. This pricing mechanism is known as surge pricing or dynamic pricing.

The reasons for surge pricing are normal peak-hours, bad weather conditions (rain, snow, etc), events (concerts, movie-premiere), traffic conditions, unseen emergencies and so on.

Surge Pricing is an algorithmically fuelled technique that Uber (and now a lot of other on-demand companies) use when there is a demand-supply imbalance. A demand-supply imbalance occurs when there is a downward shift in both the rider’s demand and driver’s availability.


During such a time of the rise in demand for rides, fares tend to usually soar high to make sure that those who need a ride can get one reliably and not rely on luck or the driver’s choice. At the core it is closely related to the principle of a free market economy i.e. it helps ensure that the consumers who really want the thing they looking for, get it.

Why Surge Pricing?

The company could have attacked the car availability problem by forcing drivers to sign up for quotas, forced schedu

ling or even night shifts. But, they rather took an innovative, offbeat approach towards it: Monetization!

Surge pricing is essential in a way that it helps in matching the drivers' efforts with the demand from consumers. It ensures that drivers are not idle or roaming around the city searching for a potential customer. Surge pricing brings about three changes to the market:

  • Reduces the demand for cars (fewer people want a car at a higher price )
  • Creates a new stream of supply (by providing incentives for new drivers to hit the road and skip New Years Party)
  • Shifts the supply of drivers to areas of high demand.

Surge pricing for any trip is based on riders’ location. As in, a driver might get a ride request when he is in a surging area but it is possible that the rider is in a non-surging area.

If Uber had not adopted for surge pricing, it would have resulted in numerous customers complaining about availability and reliability and would have drastically impacted adoption. This model ensures that the supply is not created by Uber itself but rather the drivers and this is still the company's magic recipe!

How are surge prices calculated?

An ideal scenario is when the number of riders and drivers are equal. So if there are M drivers and M riders then they can be easily mapped to one another. The crisis situation arises when the number of riders is M and the number of available drivers is M-N.

As Uber’s surge pricing algorithm increases the prices, this, in turn, motivates N more drives to hit the roads. As the utilization rates increase, Uber drives the cost down to normalcy.

Here is an official video by Uber shows how price surges actually work:

Uber maps every city into granular hyperlocal zones which are basically small hexagonal blocks. When demand in an area increases the block will start changing the color.

To know about why they use hexagons, head here:

Spatial Modelling Tidbits: Hexbins vs Geohashes?
Why we at Locale.ai are fond of hexagonal grids?

The colored areas of the map range from light orange to dark red. Light orange denotes a low surge while dark red suggests an area of a high surge. Quantitatively, the surge is denoted by multipliers of X.X. A rider in a surging area may accept a surged price for a ride if he/she wants a cab immediately.


If you want to read more about how supply demand gaps, check this out:

How analyzing supply-demand gaps can optimize your unit economics!
After all, we all need our own Google Maps.

How does Uber ensure efficiency?

It’s very intuitive to think that Uber’s aim with surge was to earn more dollars just as resorts or flights do. Rather, they wanted to assure that cars are available as and when to the customers when they requested it and they pass on the surge to the drivers. But, the surge multiplier only applies to the base fare of the trip. Even extra charges like cancellation fees or toll fees have no surges applied to them.

Today, Uber knows when people are likely to pay a price at a certain time. It predicts if a given rider is sensitive to surge or in other words, Uber figures out if a rider will accept a surged price or will wait for 15–20 minutes for prices to fall back to normal.

One way it does so is by finding out the rider’s phone battery level (if the phone battery is low the app operates in power saving mode) and then predicting if he/she is likely to pay or not. It is obvious that if your phone battery is about to die, you will accept a surged price fare because you need to get to your destination immediately. [Source]

Another interesting thing to note is that how Uber fixes the surge multipliers. It is a human tendency to accept fares if the multipliers are not rounded off. As per reports, there seems to be a large drop in demand for rides when the surge multipliers were changed from 1.9 to 2. [Source]

The black-box behind the price surge!

Surge pricing is specific in different areas of a city. Some neighborhood might have a surge pricing while other neighborhoods have normal pricing. Uber uses a lot of data such as information about events, weather, historical data, holiday time and traffic to have a forecast of future market conditions.

The multipliers are also quite different for different individuals because Uber predicts the “willingness to pay” for a rider by combining various real-time data along with user history with Uber.

It might be noted that although it is thought that surge pricing motivates drivers to come on roads but in reality what actually happens is a re-allocation of available drivers in surrounding areas/streets.

However, very often what has happened is that prices escalate very high and a cab ride can only be accepted at an exorbitant price, leading to angry users. The most recent example is that of the controversial price surge during the 2012 New Year’s Eve! [Source]

How do riders and drivers benefit from price surge?

Due to surge pricing, the time one spends waiting for an Uber decreases and it also ensures that pickup is readily available. Riders can choose to either pay a higher price or wait for some time for prices to come down or can take an alternative mode of transportation. In this way, cab-sharing companies promise a ride for everyone who wants one, though at a higher price tag.

Drivers benefit from price surging too. Since drivers get a certain percentage of the total trip fare as earnings hence during the period of the price surge their earnings also step up. Also, rides that have the surge element rewards the driver more in terms of incentives.

It also has a long-time benefit as it communicates drivers when they should, in general, get on roads for planned events, holidays or periods of expected high demand like poor weather.

Hence, surge pricing plays a major role in cutting down idle time and making unit economics profitable. It tells people when to be where!

At Locale.ai, our vision is to make “space-time” decision-making really simple for our users. To learn more about what we do, check these out:

How we’re building our geospatial analytics product using first principles
Our philosophy on analytics at Locale!
A Product for Operational Analytics using Geospatial Data!
What led to the birth of Locale.ai?

Hi! I am a final year CS undergrad at KIIT University, passionate about data science, economics, gender studies, climate change and starting up. I love to read anything and everything I lay my eyes and cursor on! You can reach out to me on LinkedIn or Twitter.