This dissertation provides a comprehensive examination of the role of Transportation Networking Companies (TNC) across four dimensions. First, we examined the factors affecting Transportation Networking Companies (TNC) pricing and destination choice behavior using weekday TNC trip data from Chicago spanning January 2019 through December 2019. Towards achieving this goal, we developed a joint model framework where trip fare is modelled using linear regression model (LR) and destination choice is modelled using a multinomial logit model (MNL). Second, we build a systematic framework to analyze spatial TNC demand patterns (origins) across the urban region at the census tract (CT) level and compare them to overall transportation demand. We propose and compute a novel metric at the census tract level to identify the potential imbalance between overall transportation demand and TNC demand by developing a Generalized Ordered Logit. The model applicability is further illustrated through elasticity analysis. Third, based on earlier studies we identified that current TNC related macroscopic studies do not incorporate attributes at the microscopic resolution. We bridge the macroscopic and microscopic analysis using a bi-level modeling approaching that accommodates for the influence of microscopic attributes within the macroscopic modeling approach. In this proposed framework, the trip level destination choice model (microscopic model) takes the form of a multinomial logit model and the origin-destination flow model (macroscopic model) takes the form of a multinomial logit fractional split model. Finally, in our effort to incorporate TNCs into travel demand tools, we conducted a comprehensive literature review on studies examining the impact of TNCs on various components of travel demand models (TDM). We provide guidelines for potential travel demand model updates using three use case examples including vehicle ownership model, trip generation model, and trip level mode choice components.