Bayesian Hierarchical Modeling Monthly Crash Counts on Freeway Segments with Temporal Correlation

Abstract

As the basis of traffic safety management, crash prediction models have long been a prominent focus in the field of freeway safety research. Studies usually take years or seasons as the observed time units, which may result in heterogeneity in crash frequency. To eliminate that heterogeneity, this study analyzes monthly crash counts and develops Bayesian hierarchical models with random effects, lag-1 autoregression (AR-1), and both (REAR-1) to accommodate the multilevel structure and temporal correlation in crash data. The candidate models are estimated and evaluated in the freeware WinBUGS using a crash dataset obtained from the Kaiyang Freeway in Guangdong Province, China. Significant temporal effects are found in the three models, and Deviance Information Criteria (DIC) results show that taking temporal correlation into account considerably improves the model fit compared with the Poisson model. The hierarchical models also avoid any misidentification of the factors with significant safety effects, because their variances are greater than in the Poisson model. The DIC value of the AR-1 model is substantially lower than that of the random effect model and equivalent to that of the REAR-1 model, which indicates the superiority of the lag-1 autoregressive structure in accounting for the temporal effects in crash frequency.

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

“…The coefficient (mean=0.30) indicates that a 0.01 increase in freeway grade may contribute to a 35% increase in injury crashes. The result conforms to engineering intuition and aligns with many previous studies which argue that a steep grade also has an adverse impact on stopping sight distances, thereby increasing crash risk [3,34]. Moreover, some previous studies have demonstrated that vertical grade would increase crash injury severities [36,37].…”

“…According to two criteria-the homogeneity in horizontal and vertical alignments as well as the 150 m minimum length-the freeway is split into 154 segments. A more detailed description on the segmentation method can be found in Zeng et al [3].…”

“…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].…”

“…There may exist substantial differences in driving behavior, maneuver ability, and crash aggressivity and worthiness among these vehicle types, which would lead to considerable variability in crash risk and injury severity [11][12][13]. A number of previous studies have demonstrated that traffic composition has statistically significant effects on crash occurrence [3,10,14]. Therefore, it would be beneficial for modeling freeway crash frequency by injury severity to incorporate the proportions of each vehicle type in the mixed traffic into the explanatory variables.…”

Highway Design and Safety Consequences: A Case Study of Interstate Highway Vertical Grades

“…The length of vertical grade, which has been found by Wang et al [4] to be influential to safety, was not considered in the Yan et al [29] research. Zeng et al [30] and Zeng et al [31] proved that higher grade increased the crash risk on highway segments. Zou and Yue [32] analyzed causation of road accidents by a Bayesian Network approach.…”