Modeling Crash Types: New Insights into the Effects of Covariates on Crashes at Rural Intersections

Kim, Do-Gyeong; Washington, Simon; Oh, Jaewon

doi:10.1061/(asce)0733-947x(2006)132:4(282)

Cited by 85 publications

(30 citation statements)

References 12 publications

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“…The results from this analysis are compared with a likelihoodbased estimation done by Kim et al (2006) using the same dataset but following a classical estimation approach and is given in Table 2. The results from the four models where traffic vol ume and geometric design factors are considered in the mean function are given in Table 3.…”

Section: Resultsmentioning

confidence: 99%

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

Mitra

Washington

2007

Accident Analysis & Prevention

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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: Resultsmentioning

confidence: 99%

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

Mitra

Washington

2007

Accident Analysis & Prevention

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206

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“…The NB model allows for capturing the extra variation by adding a randomly distributed error term, which is based on the Gamma distribution, as the name implies. Since the introduction of the NB model, it has been widely applied by transportation safety analysts (Miaou, 1994, Milton and Mannering, 1998, Kim et al, 2006, Lord and Bonneson, 2007. For example, the Safety Performance Functions (SPFs) listed in Highway Safety Manual (HSM) were all developed based on NB model (AASHTO, 2010).…”

Section: Introductionmentioning

confidence: 99%

The Poisson inverse Gaussian (PIG) generalized linear regression model for analyzing motor vehicle crash data

Zha

Lord

Zou

2014

Journal of Transportation Safety & Security

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This paper documents the application of the Poisson Inverse Gaussian (PIG) regression model for modeling motor vehicle crash data. The PIG distribution, which mixes the Poisson distribution and Inverse Gaussian distribution, has the potential for modeling highly dispersed count data due to the flexibility of Inverse Gaussian distribution. The objectives of this paper were to evaluate the application of PIG regression model for analyzing motor vehicle crash data and compare the results with Negative binomial (NB) model, especially when varying dispersion parameter is introduced. To accomplish the objectives, both NB and PIG models were developed with fixed and varying dispersion parameters and compared using two datasets. The Texas undivided rural highway segments dataset includes five years of crash data, while the divided highway segments Washington dataset includes four years. The results of this study show that PIG models perform better than the NB models in terms of goodness-of-fit (GOF) statistics. Moreover, PIG models can perform similarly well in capturing the variance of crash to the NB models. Lastly, PIG models demonstrate almost the same prediction performance compared to NB models. Considering the simple form of PIG model and its easiness of applications, PIG model could be used as a potential alternative to the NB model for analyzing crash data. Zha et al.2

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“…The simplest way of locating is grading and categorizing them in descending trend according to accident frequencies and recording accidents used in this country. Although this method has clear advantages, efficiency of identifying real traffic hot spots has many capacities for development and promotion (Brown et al, 1992;Heydecker and Wu, 2001;Kim et al, 2006;Norden et al, 1956). In this traditional method, a filed data and historical records for attribution are essentially needed.…”

Section: Introductionmentioning

confidence: 99%

Predicting and identifying traffic hot spots applying neuro-fuzzy systems in intercity roads

Hosseinlou

Sohrabi

2009

Int. J. Environ. Sci. Technol.

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Providing safety in roads for the purpose of protecting human assets and preventing social and economic losses resulted from road accidents is a significant issue. Identifying the traffic hot spots of the roads provides the possibility of promoting the road safety which is also related to investigate frequency and intensity of occurred accidents. Accidents are multidimensional and complicated events. Identifying the accident factors is based on applying a comprehensive and integrated system for making decisions. Therefore, applying common mathematical and statistical methods in this field can be resulted in some problems. Hence, the new research methods with abilities to infer meaning from complicated and ambiguous data seem useful. Therefore, along with identifying the traffic hot spots, adaptive Neuro-Fuzzy inference system is used to predict traffic hot spots on rural roads. In this process, a fuzzy inference system from Sugeno type is trained applying hybrid optimization routine (back propagation algorithm in combination with a least square type of method) and accident data of Karaj-Chalus road in Tehran Province. Then the system was tested by a complete set of data. Finally, the stated system could predict 96.85 % of accident frequencies in the studied blocks. Furthermore, the amount of effective false negative in all cases included only 0.82 % of predictions, which indicated a good approximation of predictions and model credibility.

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Modeling Crash Types: New Insights into the Effects of Covariates on Crashes at Rural Intersections

Cited by 85 publications

References 12 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

The Poisson inverse Gaussian (PIG) generalized linear regression model for analyzing motor vehicle crash data

Predicting and identifying traffic hot spots applying neuro-fuzzy systems in intercity roads

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