Uber and Lyft
This blog focuses more on the Product side — where we can leverage data science better. I would be relating with Uber and different ride-hailing problems that can be solved with data science.
I created a Demand Forecasting Project a few months back one can check out about it: HERE
If you have ever booked an Uber, you might know how simple the process is –just press a button, set the pickup location and drop location, request a car category, go for a ride and pay with a click of a button. The process is simple but a lot is going on behind the scenes.
Fraud Ride Alert
A lot of times users book a ride with a cash payment method and don’t end up traveling causing loss to both Uber and driver. driver_to_client_distance is the cost with no earning.
Solution: Identifying potential fraudulent users using behavioural features and disabling the option of Cash payment on the app.
Fare Estimation
Cost of a Uber Ride is determined on multiple parameters -> Base Price (considering distance and fuel cost) + User Rating + Area Demand + Time of Ride (Night/Morning) + Weather (Rain / Cloudy / Sunny) + Traffic Enroute + Competitor pricing
Matching problems: Given a pickup location, drop-off location, and time of the day (traffic analysis), predictive models developed at Uber predict how long will it take for a driver to cover the distance. Ping potential nearby drivers within a radius of the client to accept the ride request.
Prioritising match of one user over another based on customer rating, lifetime value, frequency of getting business to Uber.
Driver Singing
Redirecting drivers to a high-demand area so that there is no shortage of supply. Improving User experience on the app. More detail in my Edition 1 Newsletter: LINK
We all have faced problems with taxi booking requests, which sometimes cannot be fulfilled or the wait time for ride arrival is very long due to the unavailability of a nearby taxi or driver disagrees to take your request. These situations may lead to user churn and lower retention on the platform because of which users are auto-appeased with special coupons/promo offers. Data Science can help in identifying which user to appease.
I hope you learned something new from this post. If you liked it, hit 👏 and share this with others. Stay tuned for the next one!
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Disclaimer: I don’t endorse any brand. OLA, Uber, Lyft, etc. names are used to make the project more relatable to the audience and know in which business this approach can be used. This project can be used at any Taxi-hailing company.
