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Unifying Uber and taxi data via deep models for taxi passenger demand prediction

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Abstract

Taxi passenger demand prediction is of great significance to perceive citywide human mobility and make a lot of urban sensing applications more convenient. There are two major challenges to develop accurate predictive models, i.e., the complexity of the spatial-temporal dependencies as well as the dynamicity caused by some unpredictable dependencies. Although existing work uses various methods such as time series analysis, machine learning, and deep learning, most of them ignore two facts: the uncertainty of taxi demands and the impact of the parallel car-hailing markets (e.g., Uber demands) on taxi demands. In this paper, in order to deal with these two facts systematically, we design a unified framework that can use multi-source data to improve prediction accuracy. Specifically, we analyze the correlations between taxi and Uber demands and design two deep models, each of which containing a specific feature fusion method. The first model adaptively aggregates features of each grid according to the correlations. To realize the feature fusion among adjacent grids, the other method contains an additional local convolution. Besides, we also study the impact of Uber demand trends on taxi demands and aggregate the impact into the second model to improve prediction accuracy. We evaluate our models based on both taxi and Uber datasets collected from New York City, USA. Results show that our models achieve superior performance compared to the state of the art.

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Notes

  1. We delicately differentiate the two kinds of passenger demands in this paper, i.e., taxi passenger demands and Uber-passenger demands. For simplicity, we just use taxi demands and Uber demands for short in the rest of the presentation.

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Acknowledgements

Jie Zhao and Chao Chen contributed equally to this work. The work was supported by the National Natural Science Foundation of China (No. 61872050), the Chongqing Basic and Frontier Research Program (No. cstc2018jcyjAX0551), and the Fundamental Research Funds for the Central Universities (No. 21619310). Chao Chen is the corresponding author for this paper.

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  1. College of Computer Science, Chongqing University, Chongqing, 400044, China

    Jie Zhao, Chao Chen, Hongyu Huang & Chaocan Xiang

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  1. Jie Zhao
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  2. Chao Chen
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  3. Hongyu Huang
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  4. Chaocan Xiang
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Correspondence to Chao Chen or Hongyu Huang.

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Zhao, J., Chen, C., Huang, H. et al. Unifying Uber and taxi data via deep models for taxi passenger demand prediction. Pers Ubiquit Comput 27, 523–535 (2023). https://doi.org/10.1007/s00779-020-01426-y

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