Assessing How Curbside Safety Varies at the City Block Level

Research Team: Offer Grembek (lead) and Aditya Medury

University: UC Berkeley

Problem Statement: 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.

Project Description: 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 (e.g., UC Berkeley SafeTREC’s Transportation Injury Mapping System), infrastructure data (e.g., curbside parking, bicycle infrastructure, lane and traffic control configuration), traffic data (e.g., TNC pickup/drop, travel time distributions from Uber’s Movement platform, estimated vehicle miles traveled), and socio-economic data. Collectively, these data sources will provide sufficient temporal and spatial resolution to assess how traffic safety varies along city blocks.

Status: In Progress

Budget: $80,000

Project Partner(s): San Francisco Department of Public Health