Forecasting Bicycle Demand: Bologna Case Study



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Tartu Ülikool


Although there are a large number of academical studies conducted about demand forecasting in docked bike-sharing programs, there is scarce literature on the dockless bikesharing programs and especially in forecasting demand using a deep learning approach. Dockless bike-sharing programs have been growing rapidly during the past few years and having a model that can accurately predict bike usage is becoming essential for bikesharing companies and governmental institutions. This research paper aims to develop a model to forecast the usage of private bicycles with a deep learning approach and fill the research gap mentioned above. For predicting the number of rides, long short-term memory (LSTM) neural networks model was developed. The model was used to predict bike usage for 30-minute and 60-minute intervals. Besides the historical number usage of bikes, the prediction model considers air temperature, precipitation amount, and national holidays. The study results suggest that prediction with the LSTM model gives a more accurate outcome than more widely used machine learning algorithms such as linear regression, Random Forrest, and XGBoost. LSTM model that was developed by this study can be used to predict the utilization of bike lanes, which can be essential for governmental institutions and can also help bike-sharing companies to distribute bikes across the city to provide more convenient experience to the users.



dockless bike-sharing, Spatial analysis, Demand forecasting, Neural networks, Long-short term memory