Systemic Noteworthy Practice: Ohio's Machine Learning Profile Analysis for Roadway Departure and Pedestrian Crashes

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Systemic Noteworthy Practice: Ohio's Machine Learning Profile Analysis for Roadway Departure and Pedestrian Crashes

2024-07-12
By United States. Department of Transportation. Federal Highway Administration

File Language:

English

Details

Corporate Creators:

United States. Department of Transportation. Federal Highway Administration
Subject/TRT Terms:
Publication/ Report Number:

FHWA-SA-24-047
Resource Type:

Brief
Right Statement:

Public Domain
Geographical Coverage:

United States ; Ohio
Corporate Publisher:

United States. Department of Transportation. Federal Highway Administration
Abstract:

A systemic approach to safety considers both risk and crash history when making decisions on where to implement low-cost safety improvements. This approach relies heavily on data to identify locations for safety improvements that may not have been considered using other analysis methods. Analyzing the entire system using a defined set of criteria creates a more comprehensive approach that increases safety and decreases the potential for severe crashes. The systemic approach to safety has increasingly been used by agencies across the United States. This is one of a series of resources highlighting noteworthy applications of the systemic approach to safety.
Format:

PDF
Download URL:

https://rosap.ntl.bts.gov/view/dot/91892/dot_91892_DS1.pdf
File Type:

[PDF - 470.10 KB]
Collection(s):

Artificial Intelligence Deployment Center of Excellence (AI COE) ; Federal Highway Administration
Main Document Checksum:

urn:sha-512:4da1f2469c63072651f820cadcc36dd7e717624a0929bdd5c52659b08d19f624fa8146a5300c9b612cf8a8e5e82e6389b5bb2a8b7dad44181cb5a26797d9d3d6

File Language:

English

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