Assessing the Impact of App-Based Ride Share Systems in an Urban Context: Findings from Austin

App-driven ridesharing platforms are gaining popularity and are transforming urban movement patterns in cities throughout the world. Because of privacy and business considerations, their owners have released little information about riders' trip-making characteristics. This lack of data prevents planners and modelers from understanding and quantifying the impact of these new modes on regional travel patterns. In 2016, RideAustin, a not-for-profit company, was established to provide mobility-on-demand services in the Austin region. RideAustin released its dataset of over one million trips to researchers to support transportation planning through a better understanding of urban travel flows. This paper presents findings from an in-depth analysis of this dataset and summarizes key aspects of interest to the transportation research community such as the number of riders, drivers, and trips; total vehicle miles including deadhead miles; and terminal times. The paper also presents findings from two case studies that show the competitiveness of RideAustin versus transit and the utilization of the RideAustin system during the South by Southwest festival. While some of the metrics cannot be readily transferred to other regions, several findings can be used by planners and modelers as they integrate rideshare systems within their planning and modeling frameworks. We also believe that some of the research findings may provide insights into a future system of autonomous and shared vehicles.