Long Term Time Series Prediction of Bike Sharing Trips: A Cast Study of Budapest City

Bike-sharing services provide easy access to environmentally-friendly mobility reducing congestion in urban areas. Increasing demand requires more service planning based on the behavior of bike-sharing users. The Time Series models Seasonal Auto-Regressive Integrated Moving Average, Artificial Neural Network, and Exponential Smoothing have been investigated to reveal bike-sharing use for five years. Results show that weekends are attracting more trips. Summer is the most season influencing more demand. The model is predicted within a seasonal trend with a three-day lag. Compared to the Exponential Smoothing Model, SARIMA and ANN provide better predictions. Similarities are obtained in the periods of COVID-19 and after that, in the lags and highest days having bike-sharing trips. This study helps decision-makers in forecasting bike-sharing trips.