Date of Award
Master of Science
Daniel Gervini, Vytaras Brazauskas, David Spade
Bike-Sharing Systems, Doubly Stochastic Processes, Functional Data Analysis, Multiplicative Component Model, Multivariate Regression Analysis
Efficient fleet management is essential for bike-sharing systems. Thus, it is important to understand the impact of environmental factors on bike demand. This thesis proposes a method to analyze the influence of temperature on bike demand. Hourly temperature data are approximated by smoothed curves and modeled by functional principal components. Bike check-out times, which can be seen as realizations of a doubly stochastic process, are modeled using multiplicative component models on the underlying intensity functions. The respective component scores are then related via a multivariate regression model. An analysis of data from the Divvy system of the City of Chicago is presented as an example of application.
Tietze, Tobias, "A Statistical Model for the Influence of Temperature on Bike Demand in Bike-sharing Systems" (2019). Theses and Dissertations. 2133.