With the latest update now Uber is showing trip destinations to drivers before they decide to accept a ride to enable them to make informed choices. You will likely not have to pay Uber trip cancellation fee and the driver is unlikely to cancel the trip either.
The thing now from change from here, it has been always hard to predict if a driver would cancel a ride or not as the conversation of user-driver used to be on a phone call. Since the driver is now more informed about the pick and drop location of the user and is incentives separately for the distance traveled to pick up the user, the focus now shifts from cancellation to driver accepting a ride.
Let’s understand from a Data Science (Machine Learning) perspective what happens behind the scene to match user requests to a driver whose probability to accept the ride is high and get your request fulfilled in the minimum time possible.
In summary, to find the best cab drivers for you — within a few seconds; these ride-hailing companies (Uber, Lyft, Ola, Rapido, etc.) run a matching algorithm and also check a driver’s ride acceptance probability before pushing a request to them.
In this Newsletter, we shall discuss how we can build a driver ride acceptance probabilistic model.
Objective: Predict if a driver will accept ride request or not and find the probability of acceptance?
In order to figure out the features required to solve this problem in a ride-hailing business, a data scientist must be well-versed with domain knowledge. Product thinking is always important for a data scientist.
a) Enroute or Available
b) Historic Features
Interesting read profile image matters on both (driver and rider) side 😂: ‘Zombie’ drivers are scamming people out of cash with horrible profile pictures
We now have a rich feature set that can help us predict whether a driver will accept a client’s ride request or not. We use standard statistical machine learning supervised classification algorithms(with spot-checking):
Model Metrics: AUC-ROC, F-beta score (beta = 2; if Recall is twice important as Precision)
I hope you understood the business problem and can relate to the features we picked for modeling out the patterns. While there is no silver bullet solution and these problems are way more complex, our aim was to improve the user experience and minimize the user-driver matching time as even a millisecond of change in the driver-user matching algorithm can help save millions of dollars.
According to a paper entitled The Cost of Latency in High-Frequency Trading, a 1-millisecond advantage in latency can be worth upwards of $100 million per year.
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Also published here.