Project Summary
With transportation network companies (TNCs) like Uber and Lyft on the rise, the need for effective policy guidance to address operational and safety concerns is most noticeable on roadside curbs, which are in constant demand from a variety of multimodal road users. In light of this, it is important to understand how safety varies at the city block level, as an input to develop safe curbside management policies. This study will conduct a rigorous data analysis combining crash data along street segments with curbside infrastructure (e.g., parking infrastructure, bicycle infrastructure, and lane and traffic control configuration) and TNC mobility data to parse out how crash dynamics vary within a block. The study will focus on the City of San Francisco, but will also consider other cities as needed. Potential data sources that will be used as part of this research include:
Crash data: UC Berkeley SafeTREC’s Transportation Injury Mapping System (TIMS) project provides geocoded police-reported crashes in California.
Infrastructure data: curbside parking infrastructure, infrastructure details in San Francisco, bicycle infrastructure, and lane and traffic control configuration.
Traffic data: TNC pickup/drop distribution, travel time distributions from Uber’s Movement platform, and estimated vehicle miles traveled.
Socio-economic data from Census/American Commuter Survey
Collectively, the data sources listed above provide sufficient temporal and spatial resolution to assess how traffic safety varies along city blocks.
