Multivariate space-time modeling of crash frequencies by injury severity levels

The Effects of Traffic Composition on Freeway Crash Frequency by Injury Severity: A Bayesian Multivariate Spatial Modeling Approach

“…As in the previous studies [1,2], the daily vehicle kilometers traveled (DVKT), measured as the product of AADT and segment length, are used as the crash exposure variable.…”

“…As a primary component of roadway transportation system, the safety performance of freeways has attracted much attention in the field of traffic safety research in recent years [1][2][3]. To reveal crash occurrence mechanism, rank hotspots for safety improvement, and evaluate the safety benefits of countermeasures, crash prediction models (also called "safety performance functions") are usually developed to link traffic volume, roadway attributes (e.g., length, speed limit, number of lanes, median and shoulder width, vertical grade, and horizontal curvature), and weather conditions (e.g., visibility, temperature, and precipitation) to the crash frequencies on freeway segments [2,4,5].…”

“…Ye et al [9] propose a simultaneous equations model for analyzing frequencies of no-injury, possible injury, and injury and fatal crashes on 275 multilane freeway segments in the State of Washington. Ma et al [1] use multivariate space-temporal approaches to modeling daily no-injury and 2 Journal of Advanced Transportation injury crash counts on the mountainous freeway, I70 in the State of Colorado. In these studies, the roadway geometry and environmental factors are used as the safety predictors.…”

“…Since they are often found significantly correlated, joint modeling approaches, including multivariate regression [16,17], simultaneous equations [8,9], jointprobability approach [18], two-stage multivariate model [19], and multinomial-generalized Poisson models [20][21][22], are widely used for analyzing crash frequency by severity. Among these approaches, multivariate regression is able to provide a fully general covariance structure for capturing the underlying correlation while accommodating some other characteristics of crash data, such as overdispersion and spatiotemporal correlation [1]. For freeway crash frequency modeling, spatial correlation is an important issue to be considered, because the safety performance of adjacent segments may be affected by common omitted factors [23].…”

“…The multivariate conditional autoregressive (CAR) model is one of the most advanced methods for multivariate spatial modeling under the Bayesian framework. It has been successfully applied to analyze crash frequency by severity or transportation mode at the macro level (e.g., canton and census tract) [24][25][26] and the micro level (e.g., roadway segment and intersection) [1,[27][28][29]. With the ability of accounting for heterogeneous and spatial effects and their correlations among crash severities, the multivariate CAR model is expected to improve the accuracy of identifying the contributing factors to freeway crash frequency by severity.…”