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Safety Criteria for Selecting a Smart Corridor: Random Forest Approach Using HSIS Data from Washington State

Product Type

Journal Article

Author

Xiaoyu Guo, Yongxin Peng, and Chaolun Ma

Date

Dec-20

Full citation

Guo, X., Peng, Y., & Ma, C. (2020). Safety Criteria for Selecting a Smart Corridor: Random Forest Approach Using HSIS Data from Washington State. ITE Journal, 90(12), pp 35-44.

Abstract

This article reports on a study that demonstrated a use of the Highway Safety Information System (HSIS) database to determine safety criteria for selecting a smart corridor using random forest, a machine-learning approach. HSIS contains a rich dataset, which includes various variables from many aspects of transportation. In this study, the authors implemented the random forest algorithm to finalize 13 safety criteria for selecting a smart corridor out of 111 variables in the HSIS and the Highway Performance Monitoring System (HPMS) from Washington State.

Available From

ITE Journal

Link To Journal Article

Link not available.


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HSIS Summaries

HSIS Summary Reports are two to eight pages in length and include a brief description of the issue addressed, data used, methodology applied, significant results, and practical implications.

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Research Reports

A variety of research studies have been performed using data from HSIS. Many of the final reports prepared are now available electronically.

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Technical Summaries

Research reports are often summarized in executive summaries, technical briefs, or other abbreviated formats. Included here are those road safety summaries that involved research using HSIS data.

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Safety Analysis Tools

In addition to conducting research, HSIS resources are also used to develop products that can be used by practitioners in the analysis of safety problems.

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Other Projects

HSIS data are sometimes used in research studies that result in other types of finished products, such as dissertations, theses, and conference proceedings.

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