How Uber Uses Machine Learning To Reinvent Transportation?

Uber is undebatably the biggest cab service provider globally. Uber has been leveraging futuristic technologies for optimizing processes and enhancing customer service. Uber engineering is dedicatedly exploring methods to provide better services for maintaining the lead in market share.

Uber Engineering is always working on figuring the use of machine learning (ML), artificial intelligence (AI), and other advanced technologies to serve their customers better. Uber needs to gather masses of data to make predictions about market demand, find the best routes for drivers, quickly respond to support issues, keep updating its knowledge of changing roads and even detect and respond to potential fraud.


Uber is using ML to enable an efficient ride-sharing marketplace. Machine learning algorithms identify suspicious or fraudulent accounts along with the suggestion for convenient pickup and dropoff points. It is the machine learning that facilitates the UberEATS delivery by recommending restaurants to the users and predicting wait times etc. so that user get their food on time.


Uber uses Machine learning to optimize their maps. Maps hold high importance for Uber. Right from the destination search and prediction, generation of map tiles, ETAs, routing, and up-front fare estimates, maps are integral to every element of our logistics network. Even maps cover more than 95 percent of pixels on the rider and driver app UIs.


Uber leverages machine learning for growing its marketplace. A variety of teams such as Forecasting, Dispatch, Personalization, Demand Modeling, and Dynamic Pricing uses ML algorithms. Machine Learning enables precise coordination, real-time decision making, and learning needed to monitor the movement of the transportation network.


Machine learning algorithms enable to “see into the future” as accurately as possible across both space and time. ML enables us to generate spatiotemporal forecasts of supply, demand, and other quantities in real time for up to several weeks ahead.


Uber uses techniques such as long short-term memory (LSTM) networks, to help predict the future of the Marketplace and predicts the occurrence of extreme events even before they occur!


Uber uses ML-enabled Natural Language Processing (NLP) platform, generates actionable responses for customer support tickets, chatbots to make driver onboarding easier, and suggested in-app replies. With the commitment to driver partners, Uber has been using NLP platform along with deep learning models to optimize the recommended actions and turnaround times for our support tickets.


Uber Bridges the supply-demand gap by Leveraging machine learning. Uber predicts the time and areas of demand based on historical data. The system uses these predictions to alert drivers of the regions with future demand. Uber makes sure that there are always enough cabs present in the predicted areas of demand and thus bridges the supply-demand gap. Demand prediction systems help uber to slightly increase the prices during peak hours that result n more profitability.


Uber understands the importance of customer retention. Customers, they book a ride from a different service in case of unavailability of the cabs. Getting a new customer can take up to six to seven times more effort than retaining an existing customer. The supply-demand gap can make customer retention difficult. All thanks to Uber’s machine learning based demand predictions savs Uber from losing customers to its competitors.


Uber has truly shown the world how technology can be leveraged to optimize various business processes.