Preventing Pedestrian Injury Severity at Signalized Intersections: A Bayesian Multilevel Parametric Survival Model-Reference-Cited by-同舟云学术

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Preventing Pedestrian Injury Severity at Signalized Intersections: A Bayesian Multilevel Parametric Survival Model

Published:2021-10-22 Issue: Volume: Page:
ISSN:
Container-title:2021 6th International Conference on Transportation Information and Safety (ICTIS)
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Author:

Xiao Daiquan¹,Xu Xuecai¹,Yuan Quan²,Ma Changxi³

Affiliation:

1. School of Civil and Hydraulic Engineering Huazhong University of Science and Technology,Wuhan,China

2. School of Vehicle and Mobility Tsinghua University,State Key Laboratory of Automotive Safety and Energy,Beijing,China

3. School of Traffic and Transportation Engineering Lanzhou Jiaotong University,Lanzhou,China

Funder

Natural Science Foundation of China

Educational Department of Gansu Province

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9798405/9798406/09798411.pdf?arnumber=9798411

Reference21 articles.

1. Unobserved heterogeneity and the statistical analysis of highway accident data

2. Random parameters multivariate tobit and zero-inflated count data models: Addressing unobserved and zero-state heterogeneity in accident injury-severity rate and frequency analysis

3. A Bayesian spatial random parameters Tobit model for analyzing crash rates on roadway segments

4. A random thresholds random parameters hierarchical ordered pro bit analysis of highway accident injury-severities;fountas;Anal Methods Accid Res,2017

5. Bayesian approach for sample selection bias correction in regression;mokatrin;Dissertation American University Washington D C,2011

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