Travel and tourism industry is undergoing transformation with the flourishing of online sharing economy marketplaces such as Bike Share services, Uber/Lyft (for taxi services), Eatwith (for community restaurants), and AirBnB (for accommodation). The current research effort contributes to literature on sharing economy service flow analysis by formulating and estiamting econometric approaches for analyzing frequency variables. The sharing economy alternatives investigated include: (a) accommodation service (AirBnB), (b) bikeshare service (Citi bike, NYC) and (c) ride hailing service (UBER/LYFT/Taxi). In the first part of the dissertation, we develop a copula based negative binomial count model framework to count AirBnB listings at census tract level to capture the snapshot of accommodation supply for tourists in NYC. In the second part, considering bike sharing as one of the transportation sharing systems, the dissertation identifies two choice dimensions for capturing the bike share system demand: (1) station level demand and (2) how bike flows from an origin station are distributed across the network. In the third part of the dissertation on ride sharing systems, we identify two choice dimensions: a demand component that estimates origin level transportation newtwork company (TNC) demand at the taxi zone level and (2) a distribution component that analyzes how these trips from an origin are distributed across the region. A linear mixed model is considered to estimate station or taxi zone level demand while a multiple discrete continuous extreme value (MDCEV) model to analyze flows distribution is employed. In the final part of this dissertation, we develop an innovative joint econometric model system to examine two components of the rapid ride share market transformation: (a) the increase in ride hailing demand and (b) the shift from traditional taxi services to TNC services. The first component is analyzed adopting a negative binomial (NB) count model while the second component is analyzed using a multinomial fractional split (MNLFS) model
