Predicting Crash Rate Using Logistic Quantile Regression With Bounded Outcomes

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 us...

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Main Authors: Xu, Xuecai, Duan, Li
Other Authors: School of Civil and Environmental Engineering
Format: Article
Language:English
Published: 2018
Subjects:
Online Access:https://hdl.handle.net/10356/87416
http://hdl.handle.net/10220/44424
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-874162020-03-07T11:43:36Z Predicting Crash Rate Using Logistic Quantile Regression With Bounded Outcomes Xu, Xuecai Duan, Li School of Civil and Environmental Engineering Crash Rate Logistics Quantile Regression 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. Published version 2018-02-09T03:14:08Z 2019-12-06T16:41:24Z 2018-02-09T03:14:08Z 2019-12-06T16:41:24Z 2017 Journal Article Xu, X., & Duan, L. (2017). Predicting Crash Rate Using Logistic Quantile Regression With Bounded Outcomes. IEEE Access, 5, 27036-27042. https://hdl.handle.net/10356/87416 http://hdl.handle.net/10220/44424 10.1109/ACCESS.2017.2773612 en IEEE Access © 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. 7 p. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Crash Rate
Logistics Quantile Regression
spellingShingle Crash Rate
Logistics Quantile Regression
Xu, Xuecai
Duan, Li
Predicting Crash Rate Using Logistic Quantile Regression With Bounded Outcomes
description 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.
author2 School of Civil and Environmental Engineering
author_facet School of Civil and Environmental Engineering
Xu, Xuecai
Duan, Li
format Article
author Xu, Xuecai
Duan, Li
author_sort Xu, Xuecai
title Predicting Crash Rate Using Logistic Quantile Regression With Bounded Outcomes
title_short Predicting Crash Rate Using Logistic Quantile Regression With Bounded Outcomes
title_full Predicting Crash Rate Using Logistic Quantile Regression With Bounded Outcomes
title_fullStr Predicting Crash Rate Using Logistic Quantile Regression With Bounded Outcomes
title_full_unstemmed Predicting Crash Rate Using Logistic Quantile Regression With Bounded Outcomes
title_sort predicting crash rate using logistic quantile regression with bounded outcomes
publishDate 2018
url https://hdl.handle.net/10356/87416
http://hdl.handle.net/10220/44424
_version_ 1681040807349452800