01698152

Component

This paper describes efforts to identify focus crash types, focus facility types, and associated crash contributing factors to inform applications of systemic safety improvements. Systemic safety improvements—when selected and targeted appropriately—provide a tremendous opportunity to proactively reduce crashes and their resulting harm. The main objectives of this study are to (1) select reliable and applicable data resources, statistical methodologies, analysis procedures, and tools, (2) conduct data analysis to identify and validate focus crash types and facility types and their associated contributing factors, and (3) identify potential low-cost safety strategies that may effectively be used as systemic safety improvements. The study used intersection data from Washington and Ohio and non-intersection data from California and Ohio. The data were enhanced with information from the National Oceanic and Atmospheric Administration and US Census Bureau. Random forest algorithm was adopted to analyze the data and identify the contributing factors to the focus crash types and facility types. The roadway factors uncovered by the analysis as influencing the frequencies of the different crash types were generally consistent with what was expected based on previous research and existing practice.Findings related to the socioeconomic and weather-related factors showed promise, but there is not yet a significant amount of theory to support or refute the socioeconomic-and weather-related results of this effort.A six-step countermeasure selection process is also identified to use the contributing factor findings to assist safety practitioners in making informed choices regarding countermeasures to address the focus crash types.

This paper was sponsored by TRB committee ANB20 Standing Committee on Safety Data, Analysis and Evaluation.

19-05124

English

21p

2019

Transportation Research Board 98th Annual Meeting

Digital/other

Figures; References; Tables

Classification; Crash causes; Crash types; Highway safety; Improvements

Random forest algorithm

California; Ohio; Washington (State)

Data and Information Technology; Highways; Safety and Human Factors

Transportation Research Board Annual Meeting 2019 Paper #19-05124

TRIS, TRB, ATRI

Dec 7 2018 9:47AM