Date of Award

May 2019

Degree Type

Thesis

Degree Name

Master of Science

Department

Mathematics

First Advisor

Daniel Gervini

Committee Members

Daniel Gervini, Vytaras Brazauskas, David Spade

Keywords

Bike-Sharing Systems, Doubly Stochastic Processes, Functional Data Analysis, Multiplicative Component Model, Multivariate Regression Analysis

Abstract

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.

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