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

Ahmed Jaber · Bálint Csonka · János Juhász

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Urban Transport and Accessibility Forecasting Techniques and Applications Traffic Prediction and Management Techniques

Abstract

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.

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References

2015Bikeshare: A Review of Recent Literature 2019A review on bike-sharing: The factors affecting bike-sharing demand 2013Are Bikeshare Users Different from Regular Cyclists?2014Barriers to bikesharing: an analysis from Melbourne and Brisbane 2013The Role of Bicycle-sharing in the City: Analysis of the Irish Experience 2018A GIS-based MCDM approach for the evaluation of bike-share stations 2011Use of a New Public Bicycle Share Program in Montreal, Canada 2019Unravel the landscape and pulses of cycling activities from a dockless bike-sharing system

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2025Spatiotemporal Data-Driven Hourly Bike-Sharing Demand Prediction Using ApexBoost Regression 2025Assessing the impacts of transit systems and urban street features on bike-sharing ridership: A graph-based spatiotemporal analysis and prediction model 2023Discrete wavelet transform application for bike sharing system check-in/out demand prediction

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