I received an offer from Lyft last year to do data science. Here are some potential use cases of machine learning for a taxi company: -Predicting wait times -

I received an offer from Lyft last year to do data science. Here are some potential use cases of machine learning for a taxi company: -Predicting wait times -Predicting the best route -Predicting where to send drivers before requests come in -Predicting demand so that you can surge price predictively -Predicting what assets/requests to prefetch to the phone and when -Predicting when drivers will churn and what tactics will reduce churn -Predicting when passenger will churn and what tactics will reduce churn (e.g., special deal for 25% off next 10 rides) -Predicting which passengers are more or less price sensitive and then price discriminating accordingly -Predicting what it will cost various vehicles to reroute to pick up an additional Lyft Pool passenger on the way to their destination -Predicting the driver's position some time ahead (e.g., you don't want to send a request for them to get off the freeway right as they are passing their exit) -Predicting car location based on fusion of GPS, accelerometers, priors from past driving data (e.g., if GPS says you're 10 m right of the freeway traveling at 60 MPH, you're actually probably on the freeway) -Predicting which Facebook users will be most likely to click ads -Predicting who and when to send out email marketing -Predicting all sorts of server load balancing type stuff on the back end (proactively scaling capacity for known busy times, etc.)