Assessing the Variation of Curbside Safety at the City Block Level

Medury, Aditya; Vlachogiannis, Dimitris; Grembek, Offer; University of California, Berkeley. Safe Transportation Research and Education Center; University of California, Berkeley. Institute of Transportation Studies

doi:uc/item/46n9669d

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Assessing the Variation of Curbside Safety at the City Block Level

2020-06-01
By Medury, Aditya ; Vlachogiannis, Dimitris ; Grembek, Offer

[PDF-4.01 MB]

English

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Details:

Creators:

Medury, Aditya ; Vlachogiannis, Dimitris ; Grembek, Offer

Medury, Aditya ; Vlachogiannis, Dimitris ; Grembek, Offer Less -
Corporate Creators:

University of California, Berkeley. Safe Transportation Research and Education Center ; University of California, Berkeley. Institute of Transportation Studies

University of California, Berkeley. Safe Transportation Research and Education Center ; University of California, Berkeley. Institute of Transportation Studies Less -
Corporate Contributors:

Senate Bill 1, the Road Repair and Accountability Act of 2017 ; University of California Institute of Transportation Studies

Senate Bill 1, the Road Repair and Accountability Act of 2017 ; University of California Institute of Transportation Studies Less -
Subject/TRT Terms:

[+]

Cities Crash Data Crash Risk Forecasting Curbs Mathematical Models Ridesourcing Traffic Safety
Publication/ Report Number:

UC-ITS-2019-15
DOI:

https://escholarship.org/uc/item/46n9669d
Resource Type:

Tech Report
Geographical Coverage:

California ; United States

California ; United States Less -
Edition:

Final Report (July 2018 – June 2020)
Corporate Publisher:

University of California Institute of Transportation Studies
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 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/drop offs and duration of parked vehicles were positively associated with segment-level crashes.
Format:

PDF
Funding:

UC-ITS-2019-15
Collection(s):

US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:e5c22976e5b4954c5a6ff43e10d7ada118f6bfb5f56f62ce8c24c0cb013ad34c
Download URL:

https://rosap.ntl.bts.gov/view/dot/67222/dot_67222_DS1.pdf
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Assessing the Variation of Curbside Safety at the City Block Level

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