For the past century, mobility in the United States has been dictated by cars. Furthermore, cars—and even more so, the storage of cars—have dictated urban form. With cities dedicating more space to parking than even streets and roads, parking has become baked into city land use, regulations, codes, ordinances, master plans, and even finances. What happens when a car trip no longer ends in a parking space? Both transportation network companies (TNCs) and, eventually, autonomous vehicles (AVs), enable personal mobility without nearly as much parking. As such, these new mobility services necessitate the rethinking of parking policy for a future in which demand for parking will likely be greatly reduced or eliminated. However, very little research has investigated the relationship between TNCs and parking or examined how TNCs may help us better prepare for the future of AV mobility.
In this project, we use Seattle as a case city to develop data, models, and tools to aid in understanding how TNCs are already affecting the demand for parking and the revenue it generates for the city. Specifically, we ask three questions. First, what is the relationship between TNC use and parking demand and revenue over a five-year period (2012 – 2017)? Second, what localized factors—such as transit ridership, land use, TNC use, and car ownership—explain weekly temporal and spatial patterns of parking demand and revenue? And third, how can TNCs inform planning for the future introduction of AVs? This project builds off of a NITC Small Starts project underway, directed by one of the co-PIs of this proposed project, Benjamin Clark. In the small starts project, Clark and his team examined Seattle’s parking demand and revenue implications for several downtown neighborhoods. In the proposed project, we take a deeper dive into how new transportation technologies affect parking demand and revenues and what other localized factors, including TNCs, might explain these changes. Specifically, we expand our analysis to the entire City of Seattle and introduce tract-level data from Uber and Lyft to assess the relationship between parking and TNC use between 2012 and 2017. The long timeframe combined with detailed TNC data allows for an assessment of both the short-term patterns and long-term trends of TNC use and parking demand and revenue. Findings of weekly spatial and temporal patterns will inform parking policy today, while the multi-year analysis will yield insights into how cities should manage parking infrastructure to prepare for a new age of AV mobility.