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Travel demand and distance analysis for free-floating car sharing based on deep learning method

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Listed:
  • Chen Zhang
  • Jie He
  • Ziyang Liu
  • Lu Xing
  • Yinhai Wang

Abstract

In order to address the time pattern problems in free-floating car sharing, in this paper, the authors offer a comprehensive time-series method based on deep learning theory. According to car2go booking record data in Seattle area. Firstly, influence of time and location on the free-floating car-sharing usage pattern is analyzed, which reveals an apparent doublet pattern for time and dependence usage amount on population. Then, on the basis of the long-short-term memory recurrent neural network (LSTM-RNN), hourly variation in short-term traffic characteristics including travel demand and travel distance are modeled. The results were also compared with other different statistical models, such as support vector regression (SVR), Autoregressive Integrated Moving Average model (ARIMA), single and second exponential smoothing. It showed that (LSTM-RNN) shows better performance in terms of statistical analysis and tendency precision based on limited data sample.

Suggested Citation

  • Chen Zhang & Jie He & Ziyang Liu & Lu Xing & Yinhai Wang, 2019. "Travel demand and distance analysis for free-floating car sharing based on deep learning method," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-15, October.
    • Handle: RePEc:plo:pone00:0223973
    DOI: 10.1371/journal.pone.0223973
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    Cited by:

    1. María Ampudia-Renuncio & Begoña Guirao & Rafael Molina-Sanchez & Luís Bragança, 2020. "Electric Free-Floating Carsharing for Sustainable Cities: Characterization of Frequent Trip Profiles Using Acquired Rental Data," Sustainability, MDPI, vol. 12(3), pages 1-16, February.
    2. Chang, Ximing & Wu, Jianjun & Correia, Gonçalo Homem de Almeida & Sun, Huijun & Feng, Ziyan, 2022. "A cooperative strategy for optimizing vehicle relocations and staff movements in cities where several carsharing companies operate simultaneously," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 161(C).
    3. Luca Rossi & Andrea Ajmar & Marina Paolanti & Roberto Pierdicca, 2021. "Vehicle trajectory prediction and generation using LSTM models and GANs," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-28, July.

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