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http://dx.doi.org/10.12815/kits.2020.19.3.28

Forecasting of Rental Demand for Public Bicycles Using a Deep Learning Model  

Cho, Keun-min (Ajou Transp. Research Institute)
Lee, Sang-Soo (Dept. of Transportation Eng., Ajou Univ.)
Nam, Doohee (School of Social Science., Hansung Univ.)
Publication Information
The Journal of The Korea Institute of Intelligent Transport Systems / v.19, no.3, 2020 , pp. 28-37 More about this Journal
Abstract
This study developed a deep learning model that predicts rental demand for public bicycles. For this, public bicycle rental data, weather data, and subway usage data were collected. After building an exponential smoothing model, ARIMA model and LSTM-based deep learning model, forecasting errors were compared and evaluated using MSE and MAE evaluation indicators. Based on the analysis results, MSE 348.74 and MAE 14.15 were calculated using the exponential smoothing model. The ARIMA model produced MSE 170.10 and MAE 9.30 values. In addition, MSE 120.22 and MAE 6.76 values were calculated using the deep learning model. Compared to the value of the exponential smoothing model, the MSE of the ARIMA model decreased by 51% and the MAE by 34%. In addition, the MSE of the deep learning model decreased by 66% and the MAE by 52%, which was found to have the least error in the deep learning model. These results show that the prediction error in public bicycle rental demand forecasting can be greatly reduced by applying the deep learning model.
Keywords
Deep learning; Bicycle; Forecasting; Long short-term memory(LSTM); Demand;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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