Can Bike-Sharing Systems Reduce Private Car Use? - An Exploration Through The Comparison of Discrete Choice and Machine Learning Models

The implementation of Bike-Sharing Systems (BSS) is expected to lead to modifications in the travel habits of transport users, one of which is the choice of travel mode. This change could depend on various factors, and hence, it is pertinent to identify them. Especially, given that cities aim to reduce private car use to achieve sustainable mobility, the study of factors for the mode shift from private car to BSS is of great significance. Therefore, this research focuses on the identification of factors influencing the shift of private car users to BSS, based on a stated preference survey data from the city of Alexandroupolis, Greece. A binary logit model is employed for this purpose. The estimation results indicate the impacts of gender, income, travel time, travel cost and safety related aspects on the mode shift. Based on these factors, policy measures are suggested under the following categories: (i) Finance, (ii) Regulation, (iii) Infrastructure, (iv) Campaigns and (v) Customer Targeting. In addition, a secondary objective of this research is to obtain insights from the comparison of the specified logit model with a machine learning approach. Therefore, a random forest classifier is also developed. This comparison shows a coherence between the two approaches, although a discrepancy in the feature importance for gender and travel time is observed. A deeper exploration of this discrepancy highlights the hurdles that often occur, when using mathematically more powerful models, such as the random forest classifier