Shared-bicycle demand forecast using convolutional LSTM network

Guanlin Li; Xiao Li; Bei Zhuang; Yueying Li; Shangjing Lin; Ji Ma; Jin Tian

doi:10.1117/12.2686548

10 August 2023 Shared-bicycle demand forecast using convolutional LSTM network

Guanlin Li, Xiao Li, Bei Zhuang, Yueying Li, Shangjing Lin, Ji Ma, Jin Tian

Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 1275930 (2023) https://doi.org/10.1117/12.2686548
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China

Abstract

Accurate and real-time bicycle demand forecasting helps shared-bicycle operators to allocate shared-bicycle more reasonably and efficiently. Thus making full use of resources and providing convenience for people to the greatest extent. We use convolutional LSTM to form Encoding-Forecasting Structure, and use open-source mobike data set of Beijing to train our model. By evaluating the model through these metrics RMSE,MAE,R2, we found that the Encoding-Forecasting Structure has better fitting ability and accuracy.

Citation Download Citation

Guanlin Li, Xiao Li, Bei Zhuang, Yueying Li, Shangjing Lin, Ji Ma, and Jin Tian "Shared-bicycle demand forecast using convolutional LSTM network", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 1275930 (10 August 2023); https://doi.org/10.1117/12.2686548

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