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.
This report documents the Ohio Department of Transportation’s effort to improve the reliability of safety decision-making through the development of...
Documentation of potential safety performance impacts is required for Interchange Justification Reports (IJRs) to validate the need for new or modifie...
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