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
气候变暖已经引起全球降水格局改变。土壤呼吸作为陆地生态系统向大气释放CO_(2)最大的碳库,对降水变化的响应将进一步影响碳循环,从而对全球气候变化产生反馈。尽管以往已有大量关于土壤呼吸与降水变化关系的相关研究,但存在较大争议...气候变暖已经引起全球降水格局改变。土壤呼吸作为陆地生态系统向大气释放CO_(2)最大的碳库,对降水变化的响应将进一步影响碳循环,从而对全球气候变化产生反馈。尽管以往已有大量关于土壤呼吸与降水变化关系的相关研究,但存在较大争议。因此,亟待进一步深入探究土壤呼吸对降水改变的响应。基于此,研究Meta分析方法,整合了来自Web of Science英文数据库和中国知网文献数据库(CNKI)的284篇已发表的论文和367组数据,进而分析全球中低纬度地区土壤呼吸对降水改变的响应。研究结果表明,土壤呼吸对降水改变的响应呈现出非对称特征,降水量增加能够提高16.7%的土壤呼吸,而降水量减少则会抑制17.88%的土壤呼吸。研究还发现,不同生态系统和气候区域的土壤呼吸对降水改变的响应存在较大差别。其中,降水量增加能够提高草地生态系统22%的土壤呼吸,比森林生态系统土壤呼吸高出12%;而降水量减少则会削弱草地生态系统28%的土壤呼吸,这要比森林生态系统土壤呼吸还高16%。与湿润地区相比,降水量的增加对干旱地区土壤呼吸的促进作用更加明显。而降水量的减少对干旱地区和湿润地区土壤呼吸的影响均无显著差异。此外,本研究也证实了土壤呼吸对不同降水强度和年限的响应也存在差异。在不同降水强度上,无论增加降水还是减少降水,重度增减雨的土壤呼吸均改变最大,即:重度增减雨(>75%)>中度增减雨(25%—75%)>轻度增减雨(<25%);在不同降水年限上,长期增雨对土壤呼吸的促进作用尤为突出,但长期减雨对土壤呼吸影响无显著差异。研究结果可为未来气候情景下陆地生态系统土壤呼吸变化的准确预测以及模型模拟和改进提供重要的科学依据和理论基础。展开更多
Adaptive fractional polynomial modeling of general correlated outcomes is formulated to address nonlinearity in means, variances/dispersions, and correlations. Means and variances/dispersions are modeled using general...Adaptive fractional polynomial modeling of general correlated outcomes is formulated to address nonlinearity in means, variances/dispersions, and correlations. Means and variances/dispersions are modeled using generalized linear models in fixed effects/coefficients. Correlations are modeled using random effects/coefficients. Nonlinearity is addressed using power transforms of primary (untransformed) predictors. Parameter estimation is based on extended linear mixed modeling generalizing both generalized estimating equations and linear mixed modeling. Models are evaluated using likelihood cross-validation (LCV) scores and are generated adaptively using a heuristic search controlled by LCV scores. Cases covered include linear, Poisson, logistic, exponential, and discrete regression of correlated continuous, count/rate, dichotomous, positive continuous, and discrete numeric outcomes treated as normally, Poisson, Bernoulli, exponentially, and discrete numerically distributed, respectively. Example analyses are also generated for these five cases to compare adaptive random effects/coefficients modeling of correlated outcomes to previously developed adaptive modeling based on directly specified covariance structures. Adaptive random effects/coefficients modeling substantially outperforms direct covariance modeling in the linear, exponential, and discrete regression example analyses. It generates equivalent results in the logistic regression example analyses and it is substantially outperformed in the Poisson regression case. Random effects/coefficients modeling of correlated outcomes can provide substantial improvements in model selection compared to directly specified covariance modeling. However, directly specified covariance modeling can generate competitive or substantially better results in some cases while usually requiring less computation time.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.
文摘气候变暖已经引起全球降水格局改变。土壤呼吸作为陆地生态系统向大气释放CO_(2)最大的碳库,对降水变化的响应将进一步影响碳循环,从而对全球气候变化产生反馈。尽管以往已有大量关于土壤呼吸与降水变化关系的相关研究,但存在较大争议。因此,亟待进一步深入探究土壤呼吸对降水改变的响应。基于此,研究Meta分析方法,整合了来自Web of Science英文数据库和中国知网文献数据库(CNKI)的284篇已发表的论文和367组数据,进而分析全球中低纬度地区土壤呼吸对降水改变的响应。研究结果表明,土壤呼吸对降水改变的响应呈现出非对称特征,降水量增加能够提高16.7%的土壤呼吸,而降水量减少则会抑制17.88%的土壤呼吸。研究还发现,不同生态系统和气候区域的土壤呼吸对降水改变的响应存在较大差别。其中,降水量增加能够提高草地生态系统22%的土壤呼吸,比森林生态系统土壤呼吸高出12%;而降水量减少则会削弱草地生态系统28%的土壤呼吸,这要比森林生态系统土壤呼吸还高16%。与湿润地区相比,降水量的增加对干旱地区土壤呼吸的促进作用更加明显。而降水量的减少对干旱地区和湿润地区土壤呼吸的影响均无显著差异。此外,本研究也证实了土壤呼吸对不同降水强度和年限的响应也存在差异。在不同降水强度上,无论增加降水还是减少降水,重度增减雨的土壤呼吸均改变最大,即:重度增减雨(>75%)>中度增减雨(25%—75%)>轻度增减雨(<25%);在不同降水年限上,长期增雨对土壤呼吸的促进作用尤为突出,但长期减雨对土壤呼吸影响无显著差异。研究结果可为未来气候情景下陆地生态系统土壤呼吸变化的准确预测以及模型模拟和改进提供重要的科学依据和理论基础。
文摘Adaptive fractional polynomial modeling of general correlated outcomes is formulated to address nonlinearity in means, variances/dispersions, and correlations. Means and variances/dispersions are modeled using generalized linear models in fixed effects/coefficients. Correlations are modeled using random effects/coefficients. Nonlinearity is addressed using power transforms of primary (untransformed) predictors. Parameter estimation is based on extended linear mixed modeling generalizing both generalized estimating equations and linear mixed modeling. Models are evaluated using likelihood cross-validation (LCV) scores and are generated adaptively using a heuristic search controlled by LCV scores. Cases covered include linear, Poisson, logistic, exponential, and discrete regression of correlated continuous, count/rate, dichotomous, positive continuous, and discrete numeric outcomes treated as normally, Poisson, Bernoulli, exponentially, and discrete numerically distributed, respectively. Example analyses are also generated for these five cases to compare adaptive random effects/coefficients modeling of correlated outcomes to previously developed adaptive modeling based on directly specified covariance structures. Adaptive random effects/coefficients modeling substantially outperforms direct covariance modeling in the linear, exponential, and discrete regression example analyses. It generates equivalent results in the logistic regression example analyses and it is substantially outperformed in the Poisson regression case. Random effects/coefficients modeling of correlated outcomes can provide substantial improvements in model selection compared to directly specified covariance modeling. However, directly specified covariance modeling can generate competitive or substantially better results in some cases while usually requiring less computation time.
