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Fine-grained Dynamic Price Prediction in Ride-on-demand Services: Models and Evaluations

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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.

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Acknowledgements

The work was supported by the National Natural Science Foundation of China (No. 61872050, 61602067, 61572059, 61825204), the National Key Research and Development Program of China (No. 2017YFC0820405), the Fundamental Research Funds for the Central Universities (No. 21619310, 2018cdqyjsj0024), the Chongqing Basic and Frontier Research Program (No. cstc2018jcyjAX0551), the Science and Technology Project of Beijing (No. Z181100003518001), the Open Foundation of TUCSU (No. TUCSU-K-17002-01), and Beijing Outstanding Young Scientist Project. Chao Chen is the corresponding author.

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Authors and Affiliations

  1. College of Information Science and Technology, Jinan University, Guangzhou, China

    Suiming Guo

  2. College of Computer Science, Chongqing University, Chongqing, China

    Chao Chen

  3. Beijing Advanced Innovation Center for Big Data and Brain Computing, School of Computer Science and Engineering, Beihang University, Beijing, China

    Jingyuan Wang

  4. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Yaxiao Liu & Ke Xu

  5. BNRist, Beijing, China

    Ke Xu

  6. Department of Information Engineering, The Chinese University of Hong Kong, Hong Kong, China

    Dah Ming Chiu

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  1. Suiming Guo
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  4. Yaxiao Liu
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  5. Ke Xu
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Correspondence to Suiming Guo or Chao Chen.

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Guo, S., Chen, C., Wang, J. et al. Fine-grained Dynamic Price Prediction in Ride-on-demand Services: Models and Evaluations. Mobile Netw Appl 25, 505–520 (2020). https://doi.org/10.1007/s11036-019-01308-5

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