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Improving Strategic Policies for Pedestrian Safety Enhancement Using Classification Tree Modeling

Title
Improving Strategic Policies for Pedestrian Safety Enhancement Using Classification Tree Modeling
Author
정소영
Keywords
Pedestrian safety enhancement; Injury severities; Classification tree; Contributing factors; Strategic policies; TRAFFIC INJURY SEVERITY; NEW-YORK-CITY; VEHICLE CRASHES; CLASSIFIERS; MULTICLASS; REGRESSION; IMPACT
Issue Date
2016-01
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Citation
TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE, v. 85, Page. 53-64
Abstract
Pedestrian safety enhancement is a key component in reducing traffic fatalities in the Republic of Korea. The purpose of this study was to review, validate, specify, and prioritize Korea’s strategic policies for pedestrian safety enhancement using the classification tree method to model pedestrian injury severities. The findings show that pedestrian age and movement type are the two primary variables contributing to pedestrian fatalities and severe injuries. Traffic operation, road class, crash location, driver violation, and at-fault vehicle type are all secondary variables associated with pedestrian fatalities and severe injuries. Factors that contributed to crashes were compared with strategic polices for senior zones and school zones, road safety facilities, safe walking environments, and legal obligations of the driver in order to understand why certain polices are ineffective versus effective. Consequently, this study provides prescriptive analysis and specific insights pertaining to strategic policies for pedestrian safety enhancements, which can be employed in other countries for the similar purpose. For further research, this study suggests combining several other data-mining techniques with nationwide data collection.
URI
https://www.sciencedirect.com/science/article/pii/S0965856416000021http://hdl.handle.net/20.500.11754/48525
ISSN
0965-8564
DOI
10.1016/j.tra.2016.01.002
Appears in Collections:
RESEARCH INSTITUTE[E](부설연구소) > ETC
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