Fine-grained dynamic price prediction in ride-on-demand services: Models and evaluations

Abstract

Ride-on-demand (RoD) services use dynamic prices to balance the supply and demand to benefit both drivers and passengers, as an effort to improve service efficiency. However, dynamic prices also create concerns for passengers: the “unpredictable” prices sometimes prevent them from making quick decisions at ease. It is thus necessary to give passengers more information to tackle this concern, and predicting dynamic prices is a possible solution. We focus on fine-grained dynamic price prediction – predicting the price for every single passenger request. Price prediction helps passengers understand whether they could get a lower price in neighboring locations or within a short time, thus alleviating their concerns. The prediction is performed by learning the relationship between dynamic prices and features extracted from multi-source urban data. There are linear or non-linear models as candidates for learning, and using different models leads to varying implications on accuracy, interpretability, model training procedures, etc. We train one linear and one non-linear model as representatives, and evaluate their performance from different perspectives based on real service data. In addition, we interpret feature contribution, at different levels, based on both models and figure out what features or datasets contribute the most to dynamic prices. Finally, based on evaluation results, we provide discussions on model selection under different circumstances, and propose a way to combine the two models. Our hope is that the study not only serves as an accurate prediction for passengers, but also provides concrete guidance on how to choose between models to improve the prediction.