Powering the Network Pricing Model with Near Real-Time Features

Powering the Network Pricing Model with Near Real-Time Features

摘要

Drivers within the same area may have quite different earnings, depending on the trips they take. For example, consider two hypothetical drivers in downtown San Francisco. Two riders request two rides: one is within downtown San Francisco, and the other is to Oakland, as shown in the image above. The distances for the two trips are similar. If we just price the trip based on distance, they will make the same amount of money for the current trip, while the driver going to Oakland will be less likely to get more trips there. Drivers tend to reject these trips if they have other choices. To reduce the variance of earnings for colocated drivers and the cancellation rate for trips going to non-busy areas, we price these trips differently, based on the network effect.

Both the rider and driver pricing flows are being changed to compute network adjustments in real time. Both these pricing systems receive adjustments based on a common network model, which returns the relative change in GB (Gross Bookings) of enabling a specific trip, compared with an average trip from that same origin.

The network model used requires some NRT (Near Real-Time) features. In this document, we will introduce some of the challenges we faced and how we solved them when building the real-time pipelines for computing and serving these features to online models.