AI Case Study
Uber Eats improves estimated time of delivery information accuracy by 26% using machine learning algorithms
Uber is implementing machine learning technology to more accurately estimate the time of arrival (ETA) and delivery (ETD) of its services. Using historical data on trips and considering account traffic patterns among other variables, the algorithms has enabled Uber Eats to improve the accuracy of food delivery estimated time by 26%.
Consumer Goods And Services
Food Beverage And Drugs
"At the core of the user experience in a meal service is the time to delivery. You want to know approximately how long it will take for someone to deliver this meal to your doorstep or office.
Initially, that was basically thought about as a classical computation. The distance between you and the restaurant, and the average speed in your town, and then some average time to prepare the meal. That's the classical thinking. But we actually now have the data about how long it takes to make noodles, how long it takes to make a hamburger, and how long it takes to deliver it in different parts of town at different times of day. You can start building machine learning models that can give you a more accurate prediction based on the data, not on some finite computation.
When we ruled that out, we got an overnight improvement in accuracy of 26%. There's a very low friction, very low barrier for the team to say, "Hey, let's deploy more models here." If we know when the restaurant actually started the meal, we have more information. We can actually have a machine learning model that refines your estimated time of delivery even more. You can see how an application, in a short time span, goes from being a hardwired application to becoming a smart and dynamic application that benefits from knowing your behavior and from knowing other people's behavior." (techrepublic)
26% improvement in estimated time of arrival (ETA) information accuracy.
"On the Michelangelo platform, the UberEATS data scientists use gradient boosted decision tree regression models to predict this end-to-end delivery time. Features for the model include information from the request (e.g., time of day, delivery location), historical features (e.g. average meal prep time for the last seven days), and near-realtime calculated features (e.g., average meal prep time for the last one hour). Models are deployed across Uber’s data centers to Michelangelo model serving containers and are invoked via network requests by the UberEATS microservices. These predictions are displayed to UberEATS customers prior to ordering from a restaurant and as their meal is being prepared and delivered." (eng.uber)
"Predicting meal estimated time of delivery (ETD) is not simple. When an UberEATS customer places an order it is sent to the restaurant for processing. The restaurant then needs to acknowledge the order and prepare the meal which will take time depending on the complexity of the order and how busy the restaurant is. When the meal is close to being ready, an Uber delivery-partner is dispatched to pick up the meal. Then, the delivery-partner needs to get to the restaurant, find parking, walk inside to get the food, then walk back to the car, drive to the customer’s location (which depends on route, traffic, and other factors), find parking, and walk to the customer’s door to complete the delivery. The goal is to predict the total duration of this complex multi-stage process, as well as recalculate these time-to-delivery predictions at every step of the process." (eng.uber)
"Features for the model include information from the request (e.g., time of day, delivery location), historical features (e.g. average meal prep time for the last seven days), and near-realtime calculated features (e.g., average meal prep time for the last one hour)" (eng.uber)