research report

Assessing the Variation of Curbside Safety at the City Block Level

Publication Date

June 1, 2020

Author(s)

Aditya Medury, Offer Grembek, Dimitris Vlachogiannis

Areas of Expertise

Safety, Public Health, & Mobility Justice

Abstract

Investigating the dynamics behind the likelihood of vehicle crashes has been a focal research point in the transportation safety field for many years. However, the abundance of data in today’s world generates opportunities for a deeper comprehension of the various parameters affecting crash frequency. This study incorporates data from many different sources including geocoded police-reported crash data, curbside infrastructure data, and socio-demographic data for the city of San Francisco, CA. Findings revealed that the GFMNB model provides a better statistical fit than the FMNB and NB models in terms of AIC and log-likelihood, while the NB model outperformed both mixture models in terms of BIC due to the model complexity of the latter. Among the significant variables, TNC pick-ups/dropoffs and duration of parked vehicles were positively associated with segment-level crashes.