The Experts below are selected from a list of 8535 Experts worldwide ranked by ideXlab platform
Qiang Zeng - One of the best experts on this subject based on the ideXlab platform.
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a bayesian spatial random parameters Tobit Model for analyzing crash rates on roadway segments
Accident Analysis & Prevention, 2017Co-Authors: Qiang Zeng, Helai Huang, Mohamed AbdelatyAbstract:Abstract This study develops a Bayesian spatial random parameters Tobit Model to analyze crash rates on road segments, in which both spatial correlation between adjacent sites and unobserved heterogeneity across observations are accounted for. The crash-rate data for a three-year period on road segments within a road network in Florida, are collected to compare the performance of the proposed Model with that of a (fixed parameters) Tobit Model and a spatial (fixed parameters) Tobit Model in the Bayesian context. Significant spatial effect is found in both spatial Models and the results of Deviance Information Criteria (DIC) show that the inclusion of spatial correlation in the Tobit regression considerably improves Model fit, which indicates the reasonableness of considering cross-segment spatial correlation. The spatial random parameters Tobit regression has lower DIC value than does the spatial Tobit regression, suggesting that accommodating the unobserved heterogeneity is able to further improve Model fit when the spatial correlation has been considered. Moreover, the random parameters Tobit Model provides a more comprehensive understanding of the effect of speed limit on crash rates than does its fixed parameters counterpart, which suggests that it could be considered as a good alternative for crash rate analysis.
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a multivariate random parameters Tobit Model for analyzing highway crash rates by injury severity
Accident Analysis & Prevention, 2017Co-Authors: Qiang Zeng, Helai Huang, S C WongAbstract:In this study, a multivariate random-parameters Tobit Model is proposed for the analysis of crash rates by injury severity. In the Model, both correlation across injury severity and unobserved heterogeneity across road-segment observations are accommodated. The proposed Model is compared with a multivariate (fixed-parameters) Tobit Model in the Bayesian context, by using a crash dataset collected from the Traffic Information System of Hong Kong. The dataset contains crash, road geometric and traffic information on 224 directional road segments for a five-year period (2002–2006). The multivariate random-parameters Tobit Model provides a much better fit than its fixed-parameters counterpart, according to the deviance information criteria and Bayesian R2, while it reveals a higher correlation between crash rates at different severity levels. The parameter estimates show that a few risk factors (bus stop, lane changing opportunity and lane width) have heterogeneous effects on crash-injury-severity rates. For the other factors, the variances of their random parameters are insignificant at the 95% credibility level, then the random parameters are set to be fixed across observations. Nevertheless, most of these fixed coefficients are estimated with higher precisions (i.e., smaller variances) in the random-parameters Model. Thus, the random-parameters Tobit Model, which provides a more comprehensive understanding of the factors’ effects on crash rates by injury severity, is superior to the multivariate Tobit Model and should be considered a good alternative for traffic safety analysis.
S C Wong - One of the best experts on this subject based on the ideXlab platform.
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a multivariate random parameters Tobit Model for analyzing highway crash rates by injury severity
Accident Analysis & Prevention, 2017Co-Authors: Qiang Zeng, Helai Huang, S C WongAbstract:In this study, a multivariate random-parameters Tobit Model is proposed for the analysis of crash rates by injury severity. In the Model, both correlation across injury severity and unobserved heterogeneity across road-segment observations are accommodated. The proposed Model is compared with a multivariate (fixed-parameters) Tobit Model in the Bayesian context, by using a crash dataset collected from the Traffic Information System of Hong Kong. The dataset contains crash, road geometric and traffic information on 224 directional road segments for a five-year period (2002–2006). The multivariate random-parameters Tobit Model provides a much better fit than its fixed-parameters counterpart, according to the deviance information criteria and Bayesian R2, while it reveals a higher correlation between crash rates at different severity levels. The parameter estimates show that a few risk factors (bus stop, lane changing opportunity and lane width) have heterogeneous effects on crash-injury-severity rates. For the other factors, the variances of their random parameters are insignificant at the 95% credibility level, then the random parameters are set to be fixed across observations. Nevertheless, most of these fixed coefficients are estimated with higher precisions (i.e., smaller variances) in the random-parameters Model. Thus, the random-parameters Tobit Model, which provides a more comprehensive understanding of the factors’ effects on crash rates by injury severity, is superior to the multivariate Tobit Model and should be considered a good alternative for traffic safety analysis.
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a two stage bivariate logistic Tobit Model for the safety analysis of signalized intersections
Analytic Methods in Accident Research, 2014Co-Authors: S C Wong, Xuecai Xu, Keechoo ChoiAbstract:Abstract Crash frequency and crash severity Models have explored the factors that influence intersection safety. However, most of these Models address the frequency and severity independently, and miss the correlations between crash frequency Models at different crash severity levels. We develop a two-stage bivariate logistic-Tobit Model of the crash severity and crash risk at different severity levels. The first stage uses a binary logistic Model to determine the overall crash severity level. The second stage develops a bivariate Tobit Model to simultaneously evaluate the risk of a crash resulting in a slight injury and the risk of a crash resulting in a kill or serious injury (KSI). The Model uses 420 observations from 262 signalized intersections in the Hong Kong metropolitan area, integrated with information on the traffic flow, geometric road design, road environment, traffic control and any crashes that occurred during 2002 and 2003. The results obtained from the first-stage binary logistic Model indicate that the overall crash severity level is significantly influenced by the annual average daily traffic and number of pedestrian crossings. The results obtained from the second-stage bivariate Tobit Model indicate that the factor that significantly influences the numbers of both slight injury and KSI crashes is the proportion of commercial vehicles. The existence of four or more approaches, the reciprocal of the average turning radius and the presence of a turning pocket increase the likelihood of slight injury crashes. The average lane width and cycle time affect the likelihood of KSI crashes. A comparison with existing approaches suggests that the bivariate logistic-Tobit Model provides a good statistical fit and offers an effective alternative method for evaluating the safety performance at signalized intersections.
Anders Karlstrom - One of the best experts on this subject based on the ideXlab platform.
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jointly Modelling individual s daily activity travel time use and mode share by a nested multivariate Tobit Model system
Transportmetrica, 2017Co-Authors: Yusak O Susilo, Anders KarlstromAbstract:In this study, a nested multivariate Tobit Model is proposed to Model activity and travel time use jointly. This proposed Model can handle: (1) The corner solution problem; (2) time allocation trad ...
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Jointly Modelling individual’s daily activity-travel time use and mode share by a nested multivariate Tobit Model system
Transportmetrica, 2017Co-Authors: Yusak O Susilo, Anders KarlstromAbstract:In this study, a nested multivariate Tobit Model is proposed to Model activity and travel time use jointly. This proposed Model can handle: (1) The corner solution problem; (2) time allocation trade-offs among different types of activities; and (3) travel being treated as a derived demand of activity participation. The Model is applied to the Swedish national travel survey (NTS). Evidence of the potential positive utility of travel time added on non-work activity time allocation in the Swedish case is also found. The proposed Model is compared to an MDCEV Model specification. The results show clear differences in marginal effect estimates. In terms of prediction, the nested multivariate Tobit Model shows a slightly worse performance on the hit rate measure than the MDCEV Model combined with a stochastic frontier Model, but shows a slightly better performance on the SMAPE measure.
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jointly Modelling individual s daily activity travel time use and mode share by a nested multivariate Tobit Model system
Transportation research procedia, 2015Co-Authors: Yusak O Susilo, Anders KarlstromAbstract:Understanding mechanisms underlie the individual's daily time allocations is very important to understand the variability of individual's time-space constraints and to forecast his/her daily activity participation. At most of previous studies, activity time allocation was viewed as allocating a continuous quantity (daily time budget) into multiple discrete alternatives (i.e. various activities and trips to engage with). However, few researches considered the influence of travel time that needs to be spent on reaching the activity location. Moreover, travel time itself is influenced by individuals’ mode choice. This can lead to an over- or under-estimation of particular activity time location. In order to explicitly include the individual's travel time and mode choice considerations in activity time allocation Modelling, in this study, a nested multivariate Tobit Model is proposed. This proposed Model can handle: 1. Corner solution problem (i.e. the present of substantial amount of zero observations); 2. Time allocation trade-offs among different types of activities (which tends to be ignored in previous studies); 3. Travel is treated as a derived demand of activity participation (i.e. travel time and mode share are automatically censored, and are not estimated, if corresponding activity duration is censored). The Model is applied on a combined dataset of Swedish national travel survey (NTS) and SMHI (Swedish Meteorological and Hydrological Institute) weather record. Individuals’ work and non-work activity durations, travel time and mode shares are jointly Modelled as dependent variables. The influences of time-location characteristics, individual and household socio demographics and weather characteristics on each dependent variable are examined. The estimation results show a strong work and non-work activity time trade-offs due to the individual's time-space constraints. Evidences on a potential positive utility of travel time added on non-work activity time allocation in the Swedish case, are also found. Meanwhile, the results also show a consistent mode choice preference for a given individual. The estimated nested multivariate Tobit Model provides a superior prediction, in terms of the deviation of the predicted value against the actual value conditional on the correct prediction regarding censored and non-censored, compared to mutually independent Tobit Models. However, the nested multivariate Tobit Model does not necessarily have a better prediction for Model components regarding non-work related activities.
Peter C Austin - One of the best experts on this subject based on the ideXlab platform.
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bayesian extensions of the Tobit Model for analyzing measures of health status
Medical Decision Making, 2002Co-Authors: Peter C AustinAbstract:Self-reported health status is often measured using utility indices that provide a score intended to summarize an individual’s health. Measurements of health status can be subject to a ceiling effect. Frequently, researchers want to examine relationships between determinants of health and measures of health status. In this article, Bayesian extensions of the classical Tobit Model are used to study the relationship between health status and predictors of health. The author examined Models where the conditional distribution of health status was either normal or lognormal, and allowed for both homoscedasticity and heteroscedasticity. Bayes factors were then used to compare the evidence for a given Model against that for a competing Model. The author found very strong evidence that the distribution of the Health Utilities Index, conditional on age, gender, income adequacy, and number of chronic conditions, was normal with nonuniform variance, compared to the competing Models.
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the use of the Tobit Model for analyzing measures of health status
Quality of Life Research, 2000Co-Authors: Peter C Austin, Michael Escobar, Jacek A KopecAbstract:Self-reported health status is often measured using psychometric or utility indices that provide a score intended to summarize an individual's health. Measurements of health status can be subject to a ceiling effect. Frequently, researchers want to examine relationships between determinants of health and measures of health status. Regression methods that ignore the presence of a ceiling effect, or of censoring in the health status measurements can produce biased coefficient estimates. The Tobit regression Model is a frequently used tool for Modeling censored variables in econometrics research. The authors carried out a Monte-Carlo simulation study to contrast the performance of the Tobit Model for censored data with that of ordinary least squares (OLS) regression. It was demonstrated that in the presence of a ceiling effect, if the conditional distribution of the measure of health status had uniform variance, then the coefficient estimates from the Tobit Model have superior performance compared with estimates from OLS regression. However, if the conditional distribution had non-uniform variance, then the Tobit Model performed at least as poorly as the OLS Model.
Helai Huang - One of the best experts on this subject based on the ideXlab platform.
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a bayesian spatial random parameters Tobit Model for analyzing crash rates on roadway segments
Accident Analysis & Prevention, 2017Co-Authors: Qiang Zeng, Helai Huang, Mohamed AbdelatyAbstract:Abstract This study develops a Bayesian spatial random parameters Tobit Model to analyze crash rates on road segments, in which both spatial correlation between adjacent sites and unobserved heterogeneity across observations are accounted for. The crash-rate data for a three-year period on road segments within a road network in Florida, are collected to compare the performance of the proposed Model with that of a (fixed parameters) Tobit Model and a spatial (fixed parameters) Tobit Model in the Bayesian context. Significant spatial effect is found in both spatial Models and the results of Deviance Information Criteria (DIC) show that the inclusion of spatial correlation in the Tobit regression considerably improves Model fit, which indicates the reasonableness of considering cross-segment spatial correlation. The spatial random parameters Tobit regression has lower DIC value than does the spatial Tobit regression, suggesting that accommodating the unobserved heterogeneity is able to further improve Model fit when the spatial correlation has been considered. Moreover, the random parameters Tobit Model provides a more comprehensive understanding of the effect of speed limit on crash rates than does its fixed parameters counterpart, which suggests that it could be considered as a good alternative for crash rate analysis.
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a multivariate random parameters Tobit Model for analyzing highway crash rates by injury severity
Accident Analysis & Prevention, 2017Co-Authors: Qiang Zeng, Helai Huang, S C WongAbstract:In this study, a multivariate random-parameters Tobit Model is proposed for the analysis of crash rates by injury severity. In the Model, both correlation across injury severity and unobserved heterogeneity across road-segment observations are accommodated. The proposed Model is compared with a multivariate (fixed-parameters) Tobit Model in the Bayesian context, by using a crash dataset collected from the Traffic Information System of Hong Kong. The dataset contains crash, road geometric and traffic information on 224 directional road segments for a five-year period (2002–2006). The multivariate random-parameters Tobit Model provides a much better fit than its fixed-parameters counterpart, according to the deviance information criteria and Bayesian R2, while it reveals a higher correlation between crash rates at different severity levels. The parameter estimates show that a few risk factors (bus stop, lane changing opportunity and lane width) have heterogeneous effects on crash-injury-severity rates. For the other factors, the variances of their random parameters are insignificant at the 95% credibility level, then the random parameters are set to be fixed across observations. Nevertheless, most of these fixed coefficients are estimated with higher precisions (i.e., smaller variances) in the random-parameters Model. Thus, the random-parameters Tobit Model, which provides a more comprehensive understanding of the factors’ effects on crash rates by injury severity, is superior to the multivariate Tobit Model and should be considered a good alternative for traffic safety analysis.
