Assessing the Variation of Curbside Safety at the City Block Level

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, California. Findings revealed that the generalized finite mixture negative binomial (GFMNB) model provides a better statistical fit than the FMNB and negative binomial (NB) model in terms of Akaike Information Criterion (AIC) and log likelihood, while the NB model outperformed both mixture models in terms of Bayesian Information Criterion (BIC) due to model complexity of the latter. Among the significant variables, transportation network company (TNC) pick-ups/drop-offs and duration of parked vehicles were positively associated with segment-level crashes.

• Record URL:
  • http://doi.org/10.7922/G2Z899Q9
• Record URL:
  • https://rosap.ntl.bts.gov/view/dot/67222
• Corporate Authors:

  University of California, Berkeley

  Institute of Transportation Studies
  McLaughlin Hall
  Berkeley, CA  United States  94720
• Authors:
  • Medury, Aditya
  • Vlachogiannis, Dimitris
  • 0000-0001-5486-0274
  • Grembek, Offer
  • 0000-0003-1869-9457
• Publication Date: 2020-6

Language

• English

Media Info

• Media Type: Digital/other
• Edition: Final Report
• Features: Figures; Maps; References; Tables;
• Pagination: 37p

Subject/Index Terms

• TRT Terms: Binomial distributions; Crash risk forecasting; Curb side parking; Data analysis; Ridesourcing
• Geographic Terms: San Francisco (California)
• Subject Areas: Highways; Passenger Transportation; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01881606
• Record Type: Publication
• Report/Paper Numbers: UC-ITS-2019-15
• Files: NTL, TRIS
• Created Date: Apr 30 2023 4:52PM