Reassessing Traffic Safety: Implications of Infrastructure and Traffic Enforcement on Creating Safe Streets and Racial Justice

Research Team: Jesus Barajas (lead), Avipsa Roy, ​​Weijing Wang, and Shengxiang Ji

UC Campus(es): UC Davis, UC Irvine

Problem Statement: Dying in a traffic crash was the fourth leading cause of death in 2019. California streets have recently gotten safer with declines in both the absolute number and share of pedestrian crashes, but still saw a pedestrian fatality rate of 2.44 per 100,000 people in 2021, higher than the national average. City governments, Caltrans, and the US have begun to implement a Safe System approach to address the crisis, which involves data-driven safety analysis combined with improved street design, improving safety culture, and enforcement of traffic laws. While this approach often identifies disadvantaged communities as sites for targeted infrastructure interventions, these communities are also disproportionately impacted by police enforcement.

Project Description: This project seeks to quantify the nature of safety and enforcement disparities to understand the net benefits or burdens of this multipronged approach to safety. Research results may help support changes in Safe System implementation and provide evidence for the need to continually address racial disparities in police enforcement, data collection and support for alternative enforcement strategies, such authorizing speed cameras. The research will focus on three related questions about the relationship of traffic safety to infrastructure and traffic enforcement in the context of racial disparities: (1) the extent of racial disparities in traffic enforcement, (2) the relative effectiveness of street infrastructure and enforcement on traffic safety, and (3) the relationships between reported and unreported crashes and implications for equity. The researchers will examine these questions in three case study cities in California: Los Angeles, Oakland, and San Jose, all of which have Vision Zero strategies and have data available about the race of people involved in traffic stops. They will use data from the state’s Racial and Identity Profiling Act (RIPA) database of police stops, the Statewide Integrated Traffic Records System (SWITRS) crash data, and crowdsourced data from to estimate spatial models for crash outcomes accounting for neighborhood characteristics.

Status: In Progress

Budget: $153,645