Multivariate random-parameters zero-inflated negative binomial regression model: An application to estimate crash frequencies at intersections

Dong, Chunjiao; Clarke, David B.; Yan, Xuedong; Khattak, Asad J.; Huang, Baoshan

doi:10.1016/j.aap.2014.04.018

Cited by 151 publications

(87 citation statements)

References 31 publications

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“…Regression models (such as logit and probit) have been widely employed [18][19][20][21]. In regression models, binary or multiple levels of severity are typically set as dependent variables and the risk factors affecting severity as independent variables.…”

Section: Decision Tree Methodsmentioning

confidence: 99%

Analyzing Traffic Crash Severity in Work Zones under Different Light Conditions

Wei

Shu

Huang

et al. 2017

Journal of Advanced Transportation

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Previous studies have investigated various factors that contribute to the severity of work zone crashes. However, little has been done on the specific effects of light conditions. Using the data from the Enhanced Tennessee Roadway Information Management System (E-TRIMS), crashes that occurred in the Tennessee work zones during 2003-2015 are categorized into three light conditions: daylight, dark-lighted, and dark-not-lighted. One commonly used decision tree method-Classification and Regression Trees (CART)-is adopted to investigate the factors contributing to crash severity in highway work zones under these light conditions. The outcomes from the three decision trees with differing light conditions show significant differences in the ranking and importance of the factors considered in the study, thereby indicating the necessity of examining traffic crashes according to light conditions. By separately considering the crash characteristics under different light conditions, some new findings are obtained from this study. The study shows that an increase in the number of lanes increases the crash severity level in work zones during the day while decreasing the severity at night. Similarly, drugs and alcohol are found to increase the severity level significantly under the dark-not-lighted condition, while they have a limited influence under daylight and dark-lighted conditions.

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Section: Decision Tree Methodsmentioning

confidence: 99%

Analyzing Traffic Crash Severity in Work Zones under Different Light Conditions

Wei

Shu

Huang

et al. 2017

Journal of Advanced Transportation

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“…Use the GFMNB-model, -means algorithm, or hierarchical clustering algorithm to cluster the data into groups (2) Separate the data into groups based on the results of clustering (3)…”

Section: Modeling Resultsmentioning

confidence: 99%

“…Network screening to identify sites (i.e., roadway segments or intersections) with promise for safety treatments is an important task in road safety management [1][2][3][4][5][6][7]. The identification of sites with promise, also known as crash hotspots or hazardous locations, is the first task in the overall safety management process [8].…”

Section: Introductionmentioning

confidence: 99%

Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification

Zou

Zhong

Ash

et al. 2017

Journal of Advanced Transportation

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Hotspot identification (HSID) is a critical part of network-wide safety evaluations. Typical methods for ranking sites are often rooted in using the Empirical Bayes (EB) method to estimate safety from both observed crash records and predicted crash frequency based on similar sites. The performance of the EB method is highly related to the selection of a reference group of sites (i.e., roadway segments or intersections) similar to the target site from which safety performance functions (SPF) used to predict crash frequency will be developed. As crash data often contain underlying heterogeneity that, in essence, can make them appear to be generated from distinct subpopulations, methods are needed to select similar sites in a principled manner. To overcome this possible heterogeneity problem, EB-based HSID methods that use common clustering methodologies (e.g., mixture models, -means, and hierarchical clustering) to select "similar" sites for building SPFs are developed. Performance of the clustering-based EB methods is then compared using real crash data. Here, HSID results, when computed on Texas undivided rural highway cash data, suggest that all three clustering-based EB analysis methods are preferred over the conventional statistical methods. Thus, properly classifying the road segments for heterogeneous crash data can further improve HSID accuracy.

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“…Although the aim of this research is not to assess which is the best methodological approach to study the influence of these variables, suggestions of possible models are made nonetheless, seeing as during the course of the investigation, improvement in outcomes became evident when using multivariate models such as random-effect Negative Binomial, truncated Poisson, truncated Negative Binomial, zero-inflated Poisson, zero-inflated Negative Binomial, all of which yielded good estimation results from the relevant parameters. However, these models have their limitations, especially their inability to handle excess of zero records [13].…”

Section: Introductionmentioning

confidence: 99%

“…The results showed that light vehicles play the most important role in the occurrence of accidents on urban roads for both types of accidents. In the state of Tennessee (U.S.), Dong et al [13] established a relationship between collision frequency, road conditions, traffic conditions, and geometric-design features for 603 intersections. The available accident data corresponded to 7,840 events that occurred between 2001 and 2005.…”

Section: Introductionmentioning

confidence: 99%

Effects of the Attributes Associated with Roadway Geometry, Traffic Volumes and Speeds on the Incidence of Accidents in a Mid-Size City

Barbosa

Espinel-Bayona

Palacio-Sánchez

2015

IyU

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Currently traffic accidents are the second cause of death, after homicides, in Colombia. Hence, government institutions require decision-making tools to identify and tackle the causes of traffic accidents. This research aims to determine the influence of factors related to roadway geometry and conditions, and traffic volumes and speeds on the frequency of accidents on the urban road network in the city of Ocaña (north of Santander). This study will take a methodological modeling approach, which will include measuring variables in the field, creating an accidents database, and conducting a subsequent analysis of the data by calibrating a model based on Poisson and Negative Binomial regressions. The results showed that variables such as road width, number of intersections, pavement type, traffic volumes (broken down into motorcycles, light, and heavy vehicles), and average driving speed (50th percentile) relate to accident rates.

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Multivariate random-parameters zero-inflated negative binomial regression model: An application to estimate crash frequencies at intersections

Cited by 151 publications

References 31 publications

Analyzing Traffic Crash Severity in Work Zones under Different Light Conditions

Analyzing Traffic Crash Severity in Work Zones under Different Light Conditions

Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification

Effects of the Attributes Associated with Roadway Geometry, Traffic Volumes and Speeds on the Incidence of Accidents in a Mid-Size City

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