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Predictions of Taxi Demand Based on Neural Network Algorithms

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Abstract

To increase the profit both of taxi drivers and operators, this paper proposes an approach that efficiently collects the features of a customized-shape dispatch area to build the multivariate time-series prediction models for forecasting taxi demands. We also considered population distribution obtained from IMSI (International Mobile Subscriber Identity) data as the spatial correlations feature. The predictive models are built on some neural network algorithms and analyzed statistically. The experiments show that the predictions of the taxi demand in the next 30 minutes are successfully achieved. It is noteworthy that our approach outperforms the forecasting accuracy proved by a real-world error metric.

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Authors and Affiliations

  1. Chunghwa Telecom Laboratories, Ltd., No.99, Dianyan Rd., Yangmei District, Taoyuan City, 326402, Taiwan

    Chung-Yi Lin, Shen-Lung Tung, Po-Wen Lu & Tzu-Cheng Liu

  2. Graduate Institute of Networking and Multimedia, National Taiwan University, Taipei, Taiwan

    Chung-Yi Lin

  3. Department of Electrical Engineering, National Central University, Taoyuan City, Taiwan

    Shen-Lung Tung

  4. Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu, Taiwan

    Po-Wen Lu

  5. Department of Information Management, National Taiwan University of Science and Technology, Taipei, Taiwan

    Tzu-Cheng Liu

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  1. Chung-Yi Lin
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  2. Shen-Lung Tung
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  4. Tzu-Cheng Liu
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Correspondence to Chung-Yi Lin.

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Lin, CY., Tung, SL., Lu, PW. et al. Predictions of Taxi Demand Based on Neural Network Algorithms. Int. J. ITS Res. 19, 477–495 (2021). https://doi.org/10.1007/s13177-020-00248-9

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  • DOI: https://doi.org/10.1007/s13177-020-00248-9

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