Estimating truck accident rate and involvements using linear and Poisson regression models

Joshua, S C; Garber, Nicholas J

doi:10.1080/03081069008717439

Cited by 197 publications

(59 citation statements)

References 3 publications

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“…Extensive work by researchers like Joshua and Garber (1990), Miaou et al (1992), Miaou and Lum (1993a,b) established that accident phenomenon is best expressed by count models such as Poisson regression process. The Poisson process with parameter μ is given as…”

Section: Methodology and Analytical Approachmentioning

confidence: 99%

On the nature of over-dispersion in motor vehicle crash prediction models

Mitra

Washington

2007

Accident Analysis & Prevention

206

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Statistical modeling of traffic crashes has been of interest to researchers for decades. Over the most recent decade many crash models have accounted for extra-variation in crash counts-variation over and above that accounted for by the Poisson density. The extra-variation -or dispersion -is theorized to capture unaccounted for variation in crashes across sites. The majority of studies have assumed fixed dispersion parameters in over-dispersed crash models-tantamount to assuming that unaccounted for variation is proportional to the expected crash count. Miaou and Lord [Miaou, S.P., Lord, D., 2003. Modeling traffic crash-flow relationships for intersections: dispersion parameter, functional form, and Bayes versus empirical Bayes methods. Transport. Res. Rec. 1840, 31-40] challenged the fixed dispersion parameter assumption, and examined various dispersion parameter relationships when modeling urban signalized intersection accidents in Toronto. They suggested that further work is needed to determine the appropriateness of the findings for rural as well as other intersection types, to corroborate their findings, and to explore alternative dispersion functions.This study builds upon the work of Miaou and Lord, with exploration of additional dispersion functions, the use of an independent data set, and presents an opportunity to corroborate their findings. Data from Georgia are used in this study. A Bayesian modeling approach with noninformative priors is adopted, using sampling-based estimation via Markov Chain Monte Carlo (MCMC) and the Gibbs sampler. A total of eight model specifications were developed; four of them employed traffic flows as explanatory factors in mean structure while the remainder of them included geometric factors in addition to major and minor road traffic flows. The models were compared and contrasted using the significance of coefficients, standard deviance, chi-square goodness-of-fit, and deviance information criteria (DIC) statistics. The findings indicate that the modeling of the dispersion parameter, which essentially explains the extra-variance structure, depends greatly on how the mean structure is modeled. In the presence of a well-defined mean function, the extra-variance structure generally becomes insignificant, i.e. the variance structure is a simple function of the mean. It appears that extra-variation is a function of covariates when the mean structure (expected crash count) is poorly specified and suffers from omitted variables. In contrast, when sufficient explanatory variables are used to model the mean (expected crash count), extra-Poisson variation is not significantly related to these variables. If these results are generalizable, they suggest that model specification may be improved by testing extra-variation functions for significance. They also suggest that known influences of expected crash counts are likely to be different than factors that might help to explain unaccounted for variation in crashes across sites.

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Section: Methodology and Analytical Approachmentioning

confidence: 99%

On the nature of over-dispersion in motor vehicle crash prediction models

Mitra

Washington

2007

Accident Analysis & Prevention

206

View full text Add to dashboard Cite

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“…In traffic accident studies, multiple linear regression models have been frequently used (Jovanis and Chang, 1986;Joshua and Garber, 1990;Miaou and Lum, 1993a). Accident data consists of counts and the use of regression models that assume a normal distribution can result in undesirable statistical properties, such as the possibility of negative accident counts as has been suggested by Zeeger et al (1990), Miaou and Lum (1993a), and Jovanis and Chang (1986).…”

Section: Background On Statistical Models For Accident Analysesmentioning

confidence: 99%

Improvements in medical care and technology and reductions in traffic-related fatalities in Great Britain

Noland¹,

Quddus²

2004

Accident Analysis & Prevention

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractGreat Britain has one of the lowest levels of traffic-related fatalities in the industrialized world with a current total of about 3500 fatalities per year. Large reductions have occurred over the last 20-30 years and the government has targets of achieving another 40% reduction by 2010. This paper analyzes some of the factors that have been statistically significant in helping to achieve those reductions with a focus on improvements in medical care and technology. Using a cross-sectional time-series of regional data a fixed effects negative binomial model is estimated which includes three proxies of medical care and technology changes. These are the average length of inpatient stay in the hospital, the per-capita level of National Health Service staff, and number of people per-capita waiting for hospital treatment.All are statistically significant with the expected sign showing that improvements in medical technology have reduced total fatalities with less of an impact from changes in medical care.Other variables are also found to be significant, including the percent of elderly people in the population, per-capita expenditure on alcohol, motorway capacity, and average vehicle age.The latter shows a surprisingly unexpected effect, with more older vehicles in a region leading to fewer fatalities. Models evaluating effects on serious and slight injuries are also estimated and serve to confirm the expected effects of medical care and technology.

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“…Joshua and Garber (1990) These models indicated that the slope change rate, AADT, truck percentage and speed differences between trucks and non-trucks influenced the crash occurrence. However, these models did not consider any exposure (AADT or segment length) for the crash occurrence, which leads to the conclusion that the crash frequency would be zero if any of the variables' values were equal to zero (e.g., crashes would occur on a roadway section without slope changes).…”

Section: Arterialsmentioning

confidence: 99%

Development of Safety Performance Functions for SafetyAnalyst Applications in Florida

Lu¹

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During the time of my doctoral study at Florida International University, he helped me become a professional, responsible, and confident person. Dr. Gan's influence on my life has spanned many levels, at times as a professor, at other times, like a caring father.Dr. Gan taught me invaluable wisdom and problem-solving skills, both of which have been of great benefit to me during my studies, and will certainly continue to do so as I enter the engineering profession. He even took time out of his busy schedule to show me how to write professionally, from emails to technical papers. With Dr. Gan's help, I received many university, state, and international honors and awards. He has shown me that nothing is impossible if I simply believe and try my best. I am deeply honored to have been one of Dr. Gan's students. SafetyAnalyst implements the empirical Bayes (EB) method, which requires the use of Safety Performance Functions (SPFs). The system is equipped with a set of national default SPFs, and the software calibrates the default SPFs to represent the agency's safety performance. However, it is recommended that agencies generate agency-specific SPFs whenever possible. Many investigators support the view that the agency-specific SPFs represent the agency data better than the national default SPFs calibrated to agency data. Furthermore, it is believed that the crash trends in Florida are different from the states whose data were used to develop the national default SPFs.In this dissertation, Florida-specific SPFs were developed using the 2008 Roadway Characteristics Inventory (RCI) data and crash and traffic data from [2007][2008][2009][2010] for both total and fatal and injury (FI) crashes. The data were randomly divided into two sets, one for calibration (70% of the data) and another for validation (30% of the data). The negative binomial (NB) model was used to develop the Florida-specific SPFs vii for each of the subtypes of roadway segments, intersections and ramps, using the calibration data. Statistical goodness-of-fit tests were performed on the calibrated models, which were then validated using the validation data set. The results were compared in order to assess the transferability of the Florida-specific SPF models.The default SafetyAnalyst SPFs were calibrated to Florida data by adjusting the national default SPFs with local calibration factors.The performance of the Florida-specific SPFs and SafetyAnalyst default SPFs calibrated to Florida data were then compared using a number of methods, including visual plots and statistical goodness-of-fit tests. The plots of SPFs against the observed crash data were used to compare the prediction performance of the two models. Three goodness-of-fit tests,represented by the mean absolute deviance (MAD), the mean square prediction error (MSPE), and Freeman-Tukey R 2 (R 2 FT ), were also used for comparison in order to identify the better-fitting model. The results showed that Florida-specific SPFs yielded better prediction performance than the national default SPFs calibr...

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Estimating truck accident rate and involvements using linear and Poisson regression models

Cited by 197 publications

References 3 publications

On the nature of over-dispersion in motor vehicle crash prediction models

On the nature of over-dispersion in motor vehicle crash prediction models

Improvements in medical care and technology and reductions in traffic-related fatalities in Great Britain

Development of Safety Performance Functions for SafetyAnalyst Applications in Florida

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