Forecasting Bicycle Demand: Bologna Case Study
Date
2020
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Tartu Ülikool
Abstract
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.
Description
Keywords
dockless bike-sharing, Spatial analysis, Demand forecasting, Neural networks, Long-short term memory