Since they first entered the market in 2009, ride-sourcing has experienced significant growth in both popularity and utilization. The growing prevalence of ride-sourcing resulted in significant effort being dedicated to understanding the use and impacts of these services, which have highlighted the potential benefits and negative externalities associated with ride-sourcing. Consequently, understanding ride-sourcing adoption and use is crucial to mitigate the negative externalities and capitalize on the benefits associated with these services.

This dissertation aims to expand on the current understanding of ride-sourcing adoption and use by building on the findings of studies published in the decade following the introduction of ride-sourcing. In pursuit of this goal, this dissertation examined aspects of ride-sourcing use that have received relatively little attention in the literature and explored different approaches for incorporating ride-sourcing into mode choice models. To help accomplish these objectives, web-based surveys were designed and conducted to collect information on ride-sourcing adoption and use in Toronto, the Greater Toronto Area, and Metro Vancouver.

Using this data, statistical models and advanced econometric models were estimated to gain insights into ride-sourcing adoption and use. Examples include the estimation of a two-stage multinomial logistic regression model to understand the determinants of ride-sourcing adoption and user profile membership and the estimation of two-stage ordered generalized extreme value models to understand the determinants of anticipated post-pandemic ride-sourcing use. Additionally, error component mixed logit models and a joint RP-SP model were estimated and applied to explore the influence of ride-sourcing on the demand for existing modes. Besides, household travel survey data was used to explore different approaches for incorporating ride-sourcing into mode choice models.

The findings presented in this dissertation can help inform efforts to mitigate the negative externalities associated with ride-sourcing and offer insights into the potential benefits of these services. Specifically, the results can contribute to efforts to encourage shared ride-sourcing use as well as initiatives to use on-demand services to serve areas where fixed-route transit may not feasible. Additionally, the results underscore the potential for post-pandemic ride-sourcing use to differ from that of pre-pandemic use, which could have important transportation planning implications.