Research Team: Offer Grembek (lead), Aditya Medury, and Dimitris Vlachogiannis
UC Campus(es): 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: 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 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 model in terms of AIC and log likelihood, while the NB model outperformed both mixture models in terms of BIC due to 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.
Project Partner(s): San Francisco Department of Public Health