How Uber uses Data Science: Big Data and Analytics



Check out these posts about Uber and how they leveraged data and analytics to reinvent an industry.

Tackling problems like poor transportation infrastructure in some cities, poor customer experience, late car arrivals, drivers denying rides, or to accept credit cards Uber has “eaten the world” in less than 5 years.

  • 8 million users
  • 1 billion Uber trips
  • and 160,000+ people driving for Uber
  • across 449 cities
  • in 66 countries!

Data Science Central reports that Uber’s business model is based on crowd sourcing. Anyone with a car who is willing to help someone get to where they want to go can offer to help get them there.

Uber holds a vast database of drivers in all of the cities it covers, so when a passenger asks for a ride, they can instantly match you with the most suitable drivers.

Fares are calculated automatically, using GPS, street data and the company’s own algorithms which make adjustments based on the time that the journey is likely to take. This is a crucial difference from regular taxi services because customers are charged for the time the journey takes, not the distance covered.

Uber algorithms monitor traffic conditions and journey times in real-time, so prices can be adjusted as demand for rides changes, and traffic conditions mean journeys are likely to take longer. This encourages more drivers to get behind the wheel when they are needed – and stay at home when demand is low. The company has applied for a patent on this method of Big Data-informed pricing, which is calls “surge pricing”.

Also check out:

Bernard Marr





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