Short-Term Forecast of Demand for Train Station-Based Round-Trip Bikesharing

A Case Study of OV-fiets in The Netherlands

Master thesis (2022)

Authors

F.L. Wilkesmann Civil Engineering & Geosciences

Contributors

D. Ton Transport and Planning - CEG (supervisor 1)
Rik Schakenbos (supervisor 1)
O. Cats Transport and Planning - CEG (coach)
F. Schulte Transport Engineering and Logistics - Mechanical, Maritime and Materials Engineering (supervisor 2)
Faculty
Civil Engineering & Geosciences
Copyright: © 2022 Florian Wilkesmann
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Published Date

16-05-2022

Language

English

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Faculty

Civil Engineering & Geosciences

Abstract

Around the world, authorities try to increase the attractiveness of multimodal public transport (PT)-related trips to reduce car usage. To achieve this, a seamless combination between the different modes is necessary. The Dutch train station operator NS tries to enhance the combination of the bike and train by providing a train station-based round-trip bikesharing (SBRT) scheme located at train stations throughout the country. This scheme allows users to rent a bike to connect the train station and their destination. The round-trip characteristic SBRT makes it unique in comparison to widely applied one-way bikesharing schemes. While on the latter a wide range of research exists, little research has been conducted on round-trip bikesharing, especially when being integrated into an existing public transport scheme. This paper aims to fill this gap by identifying potential temporal and weather-related determinants for SBRT-rentals of the Dutch SBRT-system OV-fiets using multiple linear regression (MLR). The results are compared with findings on one-way bikesharing schemes. The results are then used as an input to forecast short-term demand. To identify a best performing forecasting method, the statistical methods MLR and Prophet are compared with the neural-network based method Long Short-Term Memory (LSTM).
It is found that for hourly rentals in an SBRT-system, the highest explanatory power achieved with the number of train travelers leaving the corresponding train station, followed by temporal and weather-related determinants. Further, the magnitude of the correlation between the determinants and the hourly demand differs across the stations in the system. For forecasting, the performance of the methods differs across the stations and forecasted periods due to the stations' distinct characteristics. But, especially in times of uncertainty, LSTM is likely to outperform the others due to it's capability of adapting to short-term changes in the demand.