Multivariate Time Series Analysis Using Recurrent Neural Network to Predict Bike-Sharing Demand

Kanokporn Boonjubut, Hiroshi Hasegawa

研究成果: Conference contribution

抜粋

The bike-sharing service system is a service that allows a customer to rent a bike from a bike-sharing station and then return it to another bike-sharing station in a short time after they reach their destination. Thus, the impact of the bike distribution system based on the frequency of bike usage needs to be assessed. The bike-sharing system operator needs to predict the demand to accurately know how many bikes are needed in every station so as to assist the planner in the management process of the bike-sharing stations. This paper proposes an efficient and accurate model for predicting the bike-sharing service usage using various features of a machine learning algorithm. We compared the exiting techniques for the sequential data predicting of artificial intelligence for time series data and analysis. Then, we considered the use of the multivariate model with a recurrent neural network (RNN), a long short-term memory (LSTM), and a gated recurrent unit (GRU). In addition, we considered combining the LSTM and GRU methods together to improve the model’s effectiveness and accuracy. The results showed that all the RNNs, including the LSTM, GRU, and the model combining the LSTM and GRU, are able to achieve high performance using the mean square mean absolute, mean squared error, and root mean square error. However, the mixed LSTM–GRU model accurately predicted the demand in this case.

元の言語English
ホスト出版物のタイトルSmart Transportation Systems 2020 - Proceedings of 3rd KES International Symposium, KES-STS 2020
編集者Xiaobo Qu, Lu Zhen, Robert J. Howlett, Lakhmi C. Jain
出版者Springer
ページ69-77
ページ数9
ISBN(印刷物)9789811552694
DOI
出版物ステータスPublished - 2020
イベント3rd KES International Symposium on Smart Transportation Systems, KES-STS 2020 - Split, Croatia
継続期間: 2020 6 172020 6 19

出版物シリーズ

名前Smart Innovation, Systems and Technologies
185
ISSN(印刷物)2190-3018
ISSN(電子版)2190-3026

Conference

Conference3rd KES International Symposium on Smart Transportation Systems, KES-STS 2020
Croatia
Split
期間20/6/1720/6/19

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Computer Science(all)

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  • これを引用

    Boonjubut, K., & Hasegawa, H. (2020). Multivariate Time Series Analysis Using Recurrent Neural Network to Predict Bike-Sharing Demand. : X. Qu, L. Zhen, R. J. Howlett, & L. C. Jain (版), Smart Transportation Systems 2020 - Proceedings of 3rd KES International Symposium, KES-STS 2020 (pp. 69-77). (Smart Innovation, Systems and Technologies; 巻数 185). Springer. https://doi.org/10.1007/978-981-15-5270-0_6