Examining Usage Patterns of Public Biking Behavior Based on IC Card Data: Comparison Before and After the Usage of Free-floating Shared Bikes

Abstract

Construction and operation of public bike-sharing have played a central role in improving urban transportation system and facilitating residents' travel. This research examines usage patterns of biking behavior by analyzing extensive smart card data from public bike-sharing system in Ningbo, China. Existing studies consider usage patterns of a bike-sharing scheme mainly basing it on trip and weather data and therefore, few studies have taken into account how free-floating shared bikes affect public bicycles. Hypothesis testing and multinomial logistic regression model were adopted to examine trends and explore differences between the two datasets. This research found that trip frequency presents habitual patterns with an obvious morning and evening peak and that usage frequency of public bikes decreased after the emergence of free-floating shared bikes, especially in peak periods of weekdays. Concurrently, it's found that customers tend to spend significantly longer time on public bicycle trips than before emergence of free-floating shared bikes. Results indicate that free-floating shared bikes and public bicycles have competitive relationship in peak period. The findings of this research will therefore provide a good explanation of how bicycles serve residents and provide support for public bike system operational decisions.