Disparities in Active Transportation Safety in the SCAG Region

Abstract

In 2017, California had the 10th highest per capita pedestrian fatality rate in the nation, with more deaths occurring in Los Angeles and Orange counties than anywhere else in the state. The Southern California Association of Governments (SCAG) allocates statewide funds for pedestrian and bicycle infrastructure, planning, and education for these and other neighboring counties. SCAG also prepares a regional plan every four years describing how the region will reduce greenhouse gas emissions to meet climate goals, and their most recent plan aims for an increase of 28 percent in walking and 71 percent in biking between 2016 and 2040. Achieving these lofty goals will require officials to prioritize the most efficient and equitable active transportation projects and build the necessary political coalitions to complete these projects. This study finds that crashes occur disproportionately in high-poverty communities of color, and occur more frequently near bus stops. Additionally, biking and walking conditions near rail stations in high-poverty areas are more dangerous compared to stations in low-poverty areas.

About the Project

Traffic collisions are just one example of the negative externalities resulting from motorized transportation, along with noise, congestion, localized air pollution, and greenhouse gas emissions. Although some crashes involve only non-motorized modes, most pedestrian and bicycle crashes involve automobiles and other large motorized vehicles. While reducing pedestrian and bicycle collisions should be a priority everywhere, reducing them in Southern California has unique importance. In 2016, California had the 10th most pedestrian fatalities per resident in the United States, and SCAG anticipates an increase in walking and bicycling throughout the region over the next few decades.This study supports the State of California and SCAG’s objectives in decreasing traffic fatalities while increasing active transportation by identifying high-collision areas, ranking which factors predict crashes, and demonstrating that these areas tend to be low-income communities and communities of color. The research team will use linear regression models and geographic information systems software to meet their objectives.