Shared Bike Demand Prediction Based on Combined Deep Learnings

The shared bike demand prediction can support shared bike scheduling activities and provide more convenient services for users. In this paper, a combined deep learning model, i.e., CNN-GRU-Attention model, is established. The model uses CNN network to extract local features of shared bike demand, GR...

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Bibliographic Details
Published in2023 4th International Conference on Electronic Communication and Artificial Intelligence (ICECAI) pp. 200 - 203
Main Authors Ren, Chuanxiang, Xu, Hui, Chai, Chunxu, Fu, Fangfang
Format Conference Proceeding
LanguageEnglish
Published IEEE 12.05.2023
Subjects
Artificial intelligence
attention mechanism combined deep learnings
CNN network
Deep learning
Feature extraction
GRU network
Neurons
Predictive models
shared bike demand prediction
Simulation
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Summary:The shared bike demand prediction can support shared bike scheduling activities and provide more convenient services for users. In this paper, a combined deep learning model, i.e., CNN-GRU-Attention model, is established. The model uses CNN network to extract local features of shared bike demand, GRU network to make predictions, and attention mechanism to extract important features. The parameters such as the number of neurons in the model are set experimentally. The simulation results show that the model has higher accuracy compared with other baseline models. It can fit the demand trend of shared bikes well and has good performance.
DOI:10.1109/ICECAI58670.2023.10176751