Revisiting spatial correlation in crash injury severity: a Bayesian generalized ordered probit model with Leroux conditional autoregressive prior
Revisiting spatial correlation in crash injury severity: a Bayesian generalized ordered probit model with Leroux conditional autoregressive prior
复制标题
重新审视碰撞伤害严重程度的空间相关性：具有勒鲁条件自回归先验的贝叶斯广义有序概率模型
DOI：
10.1080/23249935.2021.1922536
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发表时间：
2021-05
期刊：
影响因子：
--
通讯作者：
Sze N. N.
中科院分区：
文献类型：
--
作者：
Zeng Qiang;Wang Qianfang;Wang Fangzhou;Sze N. N.
为了解释距离相近的事故的空间相关性，本研究提出了一种贝叶斯空间广义有序概率（SGOP）模型，该模型采用勒鲁（Leroux）条件自回归（CAR）先验用于事故严重程度分析。所提出的模型能够……
To account for the spatial correlation of crashes that are in close proximity, this study proposes a Bayesian spatial generalized ordered probit (SGOP) model with Leroux conditional autoregressive (CAR) prior for crash severity analysis. Proposed model can accommodate the ordinal nature of injury severity and relax the assumption of monotonic effects of explanatory factors. Additionally, strength of spatial correlation is considered. Results indicate that the proposed SGOP model with Leroux CAR prior outperforms the conventional ordered probit model and SGOP model with intrinsic CAR. There is moderate spatial correlation for the crashes. Results indicate that factors including vehicle type, horizontal curvature, vertical grade, precipitation, visibility, traffic composition, day of the week, crash type, and response time of emergency medical service all affect the crash injury severity. Findings of this study can indicate the effective engineering countermeasures that can mitigate the risk of more severe crashes on the freeways.
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影响因子：
4.2
作者：
M. Quddus
通讯作者：
M. Quddus
DOI：
10.1016/j.aap.2016.11.024
发表时间：
2017-02
期刊：
Accident; analysis and prevention
影响因子：
--
作者：
W. Zou;X. Wang;Dapeng Zhang
通讯作者：
W. Zou;X. Wang;Dapeng Zhang
影响因子：
1.7
作者：
M. Abdel-Aty;Xingmei Wang
通讯作者：
M. Abdel-Aty;Xingmei Wang
影响因子：
2.5
作者：
R. D. Veaux
通讯作者：
R. D. Veaux
影响因子：
2
作者：
Lee, Jaeyoung;Abdel-Aty, Mohamed;Wang, Ling
通讯作者：
Wang, Ling