Vision Zero Risk Analysis Model: ODN RAM Model Documentation

Details

• Creators:
  Nadeau, Carrie Anne
• Corporate Creators:
  Open Data Nation ; Howard University
• Corporate Contributors:
  District Department of Transportation, Research, Development, & Technology Transfer Program ; United States. Department of Transportation. Federal Highway Administration
• Subject/TRT Terms:
  • Crash risk forecasting
  • Highway safety
  • Literature reviews
  • Risk analysis
  • Crash exposure
  • Logits
  • Machine learning
  • Predictive models
• Publication/ Report Number:
  DDOT-RDT-2020-01
• Resource Type:
  Tech Report
• Geographical Coverage:
  Washington (District of Columbia) ; United States
• Edition:
  Final Report April 2019-May 2020
• Corporate Publisher:
  District Department of Transportation, Research, Development, & Technology Transfer Program
• Abstract:
  Open Data Nation (ODN) worked with Howard University, on behalf of the District Department of Transportation (DDOT), to construct a predictive model to anticipate which roads are most likely to have traffic crashes in Washington, DC. This work is in support of the city’s broader Vision Zero initiative and its deliverables are valuable tools for city planners to prioritize traffic safety engineering, education, and enforcement activities to prevent crashes and save lives. This document aims to describe these deliverables and provide the technical methodology and requirements to operationalize them. The deliverables outlined here, include an: • Exposure model, estimating average annual daily traffic • Crash model, estimating the likelihood of a crash on a road segment Separately a literature review documented the academic research that guided ODN’s approach. In addition, a prototype application was developed to make interactions with data user-friendly.
• Format:
  PDF
• Funding:
  DCKA-2015-C-0011 Task 52
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/56418/dot_56418_DS1.pdf
• File Type:
  [PDF - 647.93 KB]
• Collection(s):
  US Transportation Collection
• Main Document Checksum:
  urn:sha256:04b7cb069a3249a6af9d9d3c697f579cb1f13b1bb5d376bf8e775930d81a1600