How to pay less: A location-specific approach to predict dynamic prices in ride-on-demand services

Abstract

In emerging ride-on-demand (RoD) services, dynamic pricing plays an important role in regulating supply and demand and improving service efficiency. Despite this, it also makes passenger anxious: whether the current price is low enough, or otherwise, how to get a lower price. It is thus necessary to provide more information to ease the anxiety, and predicting the prices is one possible solution. In this study, the authors predict the dynamic prices to help passengers learn if there is a lower price around. They first use entropy of historical prices to characterize the predictability of prices in different locations and claim that different prediction algorithms should be used to balance between efficiency and accuracy. They present an ensemble learning approach to price prediction and compare it with two baseline predictors, namely a Markov and a neural network predictor. The performance evaluation is based on the real data from a major RoD service provider. Results verify that the two baseline predictors work well in locations with different levels of predictabilities, and that ensemble learning significantly increases the prediction accuracy. Finally, they also evaluate the effects of prediction, i.e., the probability that passengers could benefit from the prediction and get a lower price.