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Rod-Revenue: Seeking Strategies Analysis and Revenue Prediction in Ride-On-Demand Service Using Multi-Source Urban Data

S. Guo, C. Chen, J. Wang, Y. Liu, X. Ke, Z. Yu, D. Zhang, and D.-M. Chiu

IEEE Transactions on Mobile Computing (TMC), 2019

Recent years witness 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 for passengers in order to earn more under the new pricing scheme. In this paper, 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. We hope that ROD-Revenue not only serves as an initial analysis of seeking strategies in RoD service, but also helps increasing drivers' revenue by offering useful guidance.

Rod-revenue: Seeking strategies analysis and revenue prediction in ride-on-demand service using multi-source urban data
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title={Rod-revenue: Seeking strategies analysis and revenue prediction in ride-on-demand service using multi-source urban data},

author={Guo, Suiming and Chen, Chao and Wang, Jingyuan and Liu, Yaxiao and Ke, Xu and Yu, Zhiwen and Zhang, Daqing and Chiu, Dah-Ming},

journal={IEEE Transactions on Mobile Computing},