This paper proposes the use of a random parameter negative binomial (NB) model for the analysis of crash counts. With the use of a 9-year, continuous panel of histories of total crash frequencies on Interstate highways in Washington State for 1999 to 2007, a random parameter NB model was estimated to account for parameter correlations, panel effects that contributed to intrasegment temporal variations, and between-site effects. Interstate geometric variables, such as lighting type proportions by length, shoulder width proportions, lane cross-section proportions, and curvature variables, were used in the model specification. Curvature variables included the number of horizontal curves in a segment, the number of vertical curves in a segment, the shortest horizontal curve in a segment length, the largest degree of curvature in a segment, the smallest vertical curve gradient, and the largest vertical curve gradient in a segment. Segments were analyzed at the interchange and the noninterchange levels. A total of 1,153 directional segments of the seven Washington State Inter-states were analyzed. The analysis yielded a statistical model of crash frequency on the basis of 10,377 observations. Several curvature effects were found to be random, which meant that they varied from segment to segment. Although, for example, the numbers of horizontal and vertical curves in a segment were fixed-parameter effects, the largest degree of curvature, as well as the smallest and largest vertical curve gradient variables, were random parameters. The logarithm of average daily travel and the median and point lighting proportions were also found to be random parameters. These results suggested that segment-specific insights into crash frequency occurrence could be improved for appropriate design policy and prioritization.
It is well known that a roundabout is an efficient and safe intersection. However, the safety is generally influenced by the given various conditions. This study analyzed the effects of the geometric and traffic flow conditions on traffic accident frequency at roundabouts, constructed in Korea since 2010. Many previous studies have investigated the efficiency and safety effects of roundabout installation. However, not many studies have analyzed the specific influences of individual geometric elements and traffic flow conditions of roundabouts. Accordingly, this study analyzed the effects of various influencing variables on traffic accident frequency based on a random parameter count model using traffic accident data in 199 roundabouts. Using random parameters that can take into account unobserved heterogeneity, this study tried to make up for the weakness of the fixed parameters model, which constrains estimated parameters to be fixed across all observations. A total of eight variables were determined to be the main influencing factors on traffic accident frequency including the number and width of entry lanes, the presence of pedestrian crossings, the width of the circulatory lanes, the presence of central islands, the radius and number of entry lanes, and traffic volume influence accident frequency. Based on the study results, safer roundabout design and more efficient roundabout operation are expected.
The objective of this study is finding the relationship between interstate highway accident frequencies and geometrics using Random Parameter Negative Binomial model. Even though it is impossible to take account of the same design criteria to the all segments or corridors on the road in reality, previous research estimated the fixed value of coefficients without considering each segment's characteristic. The drawback of the traditional negative binomial is not to explain the integrated variations in terms of time and the distinct characters specific segment has. This results in underestimation of the standard error which inflates the t-value and finally, affects the modeling estimation. Therefore, this study tries to find the relationship of accident frequencies with the heterogeneous geometrics using 9-years and 7-interstate highway data in Washington State area. 16-types of geometrics are used to derive the model which is compared with the traditional negative binomial Model to understand which Model is more suitable. In addition, by calculating marginal effect and elasticity, heterogeneous variables' effect to the accidents are estimated. Hopefully, this study will help to estiblish the future policy of geometrics.
PURPOSES :The objective was to develop the advanced method which could not explain each observation s specific characteristic in the present negative binomial method that results in under-estimation of the standard error(t-value inflation) and affects the confidence of whole derived results.
METHODS :This study dealt with traffic accidents occurring within interchange segment on highway main line with RPNB(Random Parameter Negative Binomial) method that enables to take account of heterogeneity.
RESULTS :As a result, AADT and lighting installation type on the road were revealed to have random parameter and in terms of other geometric variables, all were derived as fixed parameter(same effect on every segment). Also, marginal effects were adapted to analyze the relative effects on traffic accidents.
CONCLUSIONS :This study proves that RPNB method which considers each observation's specific characteristics is better fitted to the accident data with geometrics. Thus, it is recommended that RPNB model or other methods which could consider the heterogeneity needs to be adapted in accident analysis.
Keywords accident model, random parameter negative binomial model, heterogeneity, interchange type, marginal effect
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