Published Paper Details:

Recent years have witnessed the rapidly-growing business of ride-on-demand (RoD) services such as Uber, Lyft, and Didi. Unlike taxi services, these emerging transportation services use dynamic pricing to manipulate the supply and demand and to improve service responsiveness and quality. Despite this, on the drivers' side, dynamic pricing creates a new problem: how to seek passengers in order to earn more under the new pricing scheme. Seeking strategies have been studied extensively in traditional taxi service, but in RoD service such studies are still rare and require the consideration of more factors such as dynamic prices, the status of other transportation services, etc. We develop ROD-Revenue, aiming to mine the relationship between driver revenue and factors relevant to seeking strategies and to predict driver revenue given features extracted from multi-source urban data. We extract basic features from multiple datasets, including RoD service, taxi service, POI information, and the availability of public transportation services, and then construct composite features from basic features in a product form. The desired relationship is learned from a linear regression model with basic features and high-dimensional composite features. The linear model is chosen for its interpretability to quantitatively explain the desired relationship. Finally, we evaluate our model by predicting drivers' revenue.