Characteristics of Urban Arterial Crashes Relative to Proximity to Intersections and Injury Severity

Abstract

Intersections of an urban arterial corridor may influence crashes that occur even beyond their physical area. This study examines the effect of the gradual change in the distance of intersection influence on crash characteristics that explain injury severity outcomes of arterial crashes. The approach adopted involves simultaneous estimation of two variables: an ordinal variable representing crash-injury severity and a binary variable representing crash location (intersection versus segment crashes). The dichotomy in crash location is based on the threshold distance of intersection influence. Five sets of bivariate simultaneous models were estimated by using five threshold distances of influence varying from 0 to 200 ft at 50-ft increments. A threshold of 0 ft essentially means that crashes only at the physical area of intersections are treated as intersection crashes. The other four thresholds define crashes 50, 100, 150, and 200 ft from the center of the intersections as intersection crashes. Simultaneous estimation allows accounting for common factors that affect both crash location and injury severity, but are explicitly included in neither model. Effects of these common unknown factors are reflected in the estimated correlation coefficient between the error terms for the two models. The correlation coefficients were found to be significant for influence distances of 150 and 200 ft and insignificant for influence distances 0 through 100 ft. The implications of these results are discussed. Results of the simultaneous estimation also reveal that crashes on the corridor are less severe during afternoon peak traffic conditions and on blacktop surfaces, while segments with a higher speed limit, a wider pavement surface, and a lower-than-median annual average daily traffic are likely to experience more severe crashes. At low-influence distance thresholds (≤50 ft), pavement surface condition (dry pavement) is significant in discriminating intersection crashes from segment crashes, while pavement surface type (blacktop surface) is significant at higher (≥150-ft) thresholds.

Using conditional inference forests to identify the factors affecting crash severity on arterial corridors

“…Hence the increase in speeds and less attention by the drivers could lead to crashes with severe injuries. Das et al (2008) had found similar results. On the same corridor group, older drivers (N55 yrs) also are involved in severe rear-end crashes.…”

“…For example, in Florida, all the crashes occurring within 250 ft. from the center of an intersection are categorized as intersection related crashes, as has been reported by Abdel-Aty and and Wang, Abdel-Aty, and Brady (2006). Recently Das, Pande, Abdel-Aty, and Santos (2008) showed that proximity only is not the best way to assign crashes. Wang, Abdel-Aty, Nevarez, and Santos (2008) used frequency modeling for crashes with fixed as well as varying influence distance and found a different set of significant factors.…”

“…Also, the proposed methodology (conditional inference trees/forests) uses Chi-square test statistic to identify the relationship between a particular parameter and target variable. Each category of the variable should have a sufficient number of observations in the contingency table for the Chi-square to be evaluated as discussed by Das et al (2008). Continuous variables like annual daily traffic (ADT), percentage of trucks, and K-factor (design hour volume as a percentage of ADT) and skid (friction resistance multiplied by a factor of 100) were also divided into categories.…”

Multivariate Poisson-Lognormal Model for Analysis of Crashes on Urban Signalized Intersections Approach

“…For example, the Texas Department of Transportation (2015) defines an intersection crash as the crash in which the first harmful event occurs within the limits of an intersection, and an intersection-related crash is a crash in which the first harmful event occurs on an approach to or exit from an intersection and results from an activity, behavior, or control related to the movement of traffic units through the intersection. In Florida, the physical area of an intersection is considered to be the area within 50 ft of the center of the intersection (Das et al 2008). In Australia, the Transport for New South Wales (2015) defines an intersection crash as "a crash where the first impact occurs at or within 10 m of an intersection."…”

“…Although numerous intersection crash analysis articles are available, few studies focus on the crash characteristics of intersection approach crashes and their influencing factors. Das et al (2008) studied the effect of distance from the center of the intersection on characteristics of urban arterial crashes. They suggested that severity of arterial crashes (intersection crashes and midblock segment crashes) might be modeled independently of crash location if the intersection crash occurred within 100 ft of the center of intersection.…”