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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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 |
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Crash Rate Logistics Quantile Regression Xu, Xuecai Duan, Li Predicting Crash Rate Using Logistic Quantile Regression With Bounded Outcomes |
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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. |
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School of Civil and Environmental Engineering |
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School of Civil and Environmental Engineering Xu, Xuecai Duan, Li |
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Article |
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Xu, Xuecai Duan, Li |
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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 |
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Predicting Crash Rate Using Logistic Quantile Regression With Bounded Outcomes |
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Predicting Crash Rate Using Logistic Quantile Regression With Bounded Outcomes |
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predicting crash rate using logistic quantile regression with bounded outcomes |
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2018 |
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https://hdl.handle.net/10356/87416 http://hdl.handle.net/10220/44424 |
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