Model of Relationship between Interstate Crash Occurrence and Geometrics

Abstract

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.

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“…The average marginal effect for the NB model implies that the number of accidents increased by 0.421. Our findings are conform to previous results (Abdel-Aty & Radwan, 2000; Agbelie, 2016a; Anastasopoulos, 2016;Kumara & Chin, 2003;Venkataraman et al, 2013;Venkataraman. et al, 2011).…”

Multi-level Bayesian safety analysis with unprocessed Automatic Vehicle Identification data for an urban expressway

“…Urban road geometric characteristics contributing to crash occurrence were identified. Venkataraman et al (2011Venkataraman et al ( , 2013 analyzed 1153 directional segments of the seven Washington State interstates with nine-year's crash data. In their study, several geometric variables such as curvature effects, roadway lighting, left and right side shoulder width were studied.…”

Analysis of Traffic Accident Occurrence at Hazardous Road Locations: A Case Study in Tunisia

“…Overall, the previous findings are consistent. The factors, that most commonly found to increase frequency of accidents, are Section length (Abdel-Aty & Radwan, 2000;Anastasopoulos, 2016;Anastasopoulos & Mannering, 2009;Caliendo, De Guglielmo, & Guida, 2013;Caliendo., De Guglielmo, & Guida, 2016;Carson & Mannering, 2001;Malyshkina, Mannering, & Tarko, 2009;Naznin, Currie, Logan, & Sarvi, 2016;Qin, Ivan, & Ravishanker, 2004;Singh, Sachdeva, & Pal, 2016;Venkataraman, Ulfarsson, & Shankar, 2013;Wang, Quddus, & Ison, 2011) number of lanes (Caliendo et al, 2013;Chang, 2005;Gomes, 2013;Venkataraman., Ulfarsson, Shankar, Oh, & Park, 2011) and the presence of work zone (Chen. & Tarko, 2014;Eustace, Aylo, & Mergia, 2015;Roshandeh, Agbelie, & Lee, 2016).…”