Travel time reliability(TTR) modeling has gain attention among researchers’ due to its ability to represent road user satisfaction as well as providing a predictability of a trip travel time.Despite this significant ...Travel time reliability(TTR) modeling has gain attention among researchers’ due to its ability to represent road user satisfaction as well as providing a predictability of a trip travel time.Despite this significant effort,its impact on the severity of a crash is not well explored.This study analyzes the effect of TTR and other variables on the probability of the crash severity occurring on arterial roads.To address the unobserved heterogeneity problem,two random-effect regressions were applied;the Dirichlet random-effect(DRE)and the traditional random-effect(TRE) logistic regression.The difference between the two models is that the random-effect in the DRE is non-parametrically specified while in the TRE model is parametrically specified.The Markov Chain Monte Carlo simulations were adopted to infer the parameters’ posterior distributions of the two developed models.Using four-year police-reported crash data and travel speeds from Northeast Florida,the analysis of goodness-of-fit found the DRE model to best fit the data.Hence,it was used in studying the influence of TTR and other variables on crash severity.The DRE model findings suggest that TTR is statistically significant,at 95 percent credible intervals,influencing the severity level of a crash.A unit increases in TTR reduces the likelihood of a severe crash occurrence by 25 percent.Moreover,among the significant variables,alcohol/drug impairment was found to have the highest impact in influencing the occurrence of severe crashes.Other significant factors included traffic volume,weekends,speed,work-zone,land use,visibility,seatbelt usage,segment length,undivided/divided highway,and age.展开更多
目的采用两样本孟德尔随机化(MR)探讨417种循环代谢产物与吉兰-巴雷综合征(GBS)风险的因果关系。方法通过MRC IEU OpenGWAS项目获得3个循环代谢产物全基因组关联研究(GWAS)数据,分别为“met-a”“met-c”和“met-d”。GBS相关的单核苷...目的采用两样本孟德尔随机化(MR)探讨417种循环代谢产物与吉兰-巴雷综合征(GBS)风险的因果关系。方法通过MRC IEU OpenGWAS项目获得3个循环代谢产物全基因组关联研究(GWAS)数据,分别为“met-a”“met-c”和“met-d”。GBS相关的单核苷酸多态性位点(SNPs)数据来源于芬兰生物银行数据库,表型代码为“finn-b-G6_GUILBAR”。将来自GWAS的与循环代谢产物密切相关的遗传变异数据(SNPs)作为工具变量(IVs),与来自芬兰的GBS GWAS数据进行双样本MR分析,主要采用随机效应模型的逆方差加权(IVW)方法,根据效应指标优势比(OR)和95%CI评估结果。使用留一法、异质性检验、水平基因多效性检验验证结果的稳定性和可靠性。结果共5种循环代谢产物具有与GBS因果关系的提示性证据。其中,肌酐(OR=2.924,95%CI:1.194~7.163,P=0.019)、谷氨酰胺(OR=1.902,95%CI:1.007~3.592,P=0.048)、异戊酰基肉碱(OR=140.767,95%CI:3.510~5645.336,P=0.009)的循环水平与GBS风险较高有关。相反,基因预测葡萄糖(OR=0.308,95%CI:0.010~0.981,P=0.046)、X-11491(OR=0.069,95%CI:0.007~0.707,P=0.024)的循环水平与GBS风险呈负相关。结论肌酐、谷氨酰胺、异戊酰基肉碱、葡萄糖、X-11491可能与GBS有因果关系。展开更多
基金the Center for Accessibility and Safety for an Aging Population at Florida State UniversityFlorida A&M UniversityUniversity of North Florida for funding support in research
文摘Travel time reliability(TTR) modeling has gain attention among researchers’ due to its ability to represent road user satisfaction as well as providing a predictability of a trip travel time.Despite this significant effort,its impact on the severity of a crash is not well explored.This study analyzes the effect of TTR and other variables on the probability of the crash severity occurring on arterial roads.To address the unobserved heterogeneity problem,two random-effect regressions were applied;the Dirichlet random-effect(DRE)and the traditional random-effect(TRE) logistic regression.The difference between the two models is that the random-effect in the DRE is non-parametrically specified while in the TRE model is parametrically specified.The Markov Chain Monte Carlo simulations were adopted to infer the parameters’ posterior distributions of the two developed models.Using four-year police-reported crash data and travel speeds from Northeast Florida,the analysis of goodness-of-fit found the DRE model to best fit the data.Hence,it was used in studying the influence of TTR and other variables on crash severity.The DRE model findings suggest that TTR is statistically significant,at 95 percent credible intervals,influencing the severity level of a crash.A unit increases in TTR reduces the likelihood of a severe crash occurrence by 25 percent.Moreover,among the significant variables,alcohol/drug impairment was found to have the highest impact in influencing the occurrence of severe crashes.Other significant factors included traffic volume,weekends,speed,work-zone,land use,visibility,seatbelt usage,segment length,undivided/divided highway,and age.
文摘目的采用两样本孟德尔随机化(MR)探讨417种循环代谢产物与吉兰-巴雷综合征(GBS)风险的因果关系。方法通过MRC IEU OpenGWAS项目获得3个循环代谢产物全基因组关联研究(GWAS)数据,分别为“met-a”“met-c”和“met-d”。GBS相关的单核苷酸多态性位点(SNPs)数据来源于芬兰生物银行数据库,表型代码为“finn-b-G6_GUILBAR”。将来自GWAS的与循环代谢产物密切相关的遗传变异数据(SNPs)作为工具变量(IVs),与来自芬兰的GBS GWAS数据进行双样本MR分析,主要采用随机效应模型的逆方差加权(IVW)方法,根据效应指标优势比(OR)和95%CI评估结果。使用留一法、异质性检验、水平基因多效性检验验证结果的稳定性和可靠性。结果共5种循环代谢产物具有与GBS因果关系的提示性证据。其中,肌酐(OR=2.924,95%CI:1.194~7.163,P=0.019)、谷氨酰胺(OR=1.902,95%CI:1.007~3.592,P=0.048)、异戊酰基肉碱(OR=140.767,95%CI:3.510~5645.336,P=0.009)的循环水平与GBS风险较高有关。相反,基因预测葡萄糖(OR=0.308,95%CI:0.010~0.981,P=0.046)、X-11491(OR=0.069,95%CI:0.007~0.707,P=0.024)的循环水平与GBS风险呈负相关。结论肌酐、谷氨酰胺、异戊酰基肉碱、葡萄糖、X-11491可能与GBS有因果关系。
