Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87416
Title: Predicting Crash Rate Using Logistic Quantile Regression With Bounded Outcomes
Authors: Xu, Xuecai
Duan, Li
Keywords: Crash Rate
Logistics Quantile Regression
Issue Date: 2017
Source: Xu, X., & Duan, L. (2017). Predicting Crash Rate Using Logistic Quantile Regression With Bounded Outcomes. IEEE Access, 5, 27036-27042.
Series/Report no.: IEEE Access
Abstract: Various approaches and perspectives have been presented in safety analysis during the last decade, but when some continuous outcome variables take on values within a bounded interval, the conventional statistical methods may be inadequate, and frequency distributions of bounded outcomes cannot be used to handle it appropriately. Therefore, in this paper, a logistic quantile regression (QR) model is provided to fill this gap and deal with continuous bounded outcomes with crash rate prediction. The crash data set from 2003 to 2005 maintained by the Nevada Department of Transportation is employed to illustrate the performance of the proposed model. The results show that average travel speed, signal spacing, driveway density, and annual average daily traffic on each lane are significantly influencing factors on crash rate, and logistic QR is verified as an alternative method in predicting crash rate.
URI: https://hdl.handle.net/10356/87416
http://hdl.handle.net/10220/44424
DOI: 10.1109/ACCESS.2017.2773612
Rights: © 2017 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CEE Journal Articles

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