Investigating the Temporal Instability in Injury Severity Outcomes of Clear and Adverse Weather Crashes on Rural Mountainous Highways

Abstract

Analysis of driver injury severity based on weather conditions on rural highways is limited in the literature. Such analyses provide insights useful to policymakers in optimizing the allocation of limited resources based on weather conditions. Furthermore, if there is a possibility of factors exhibiting temporal instability, then an aggregate analysis can lead to erroneous allocation of funds. In this study, separate models for clear and adverse weather conditions were developed for each of the years from 2015 to 2019 using crash data from a rural mountainous highway corridor. A random-intercept Bayesian logistic approach was used to analyze the dichotomous injury severity response and capture the between-crash variance. An efficient Markov chain Monte Carlo sampling technique known as the No-U-Turn Hamiltonian Monte Carlo was employed to sample the posterior distributions of parameter estimates. Likelihood ratio tests provided statistical significance of the temporal instability and also the differences in driver injury severities resulting from clear and adverse weather crashes. While most of the variables demonstrated temporal instability, some factors exhibited temporal stability over a short period of time and only during clear weather conditions. Findings from the separate models suggest that there are major differences in both the combination and magnitude of the significant contributing factors. Implementation of confirmatory warning signs, variable message signs, connected vehicle technology, strict enforcements during different times and locations, and driver awareness programs have been recommended as suitable countermeasures. The findings and recommendations could potentially help in guiding the respective agencies in formulating injury severity mitigation policies and strategies.

Analysis Framework to Assess Crash Severity for Large Trucks on Rural Interstate Roads Utilizing the Latent Class and Random Parameter Model

“…Speeding for conditions such as adverse weather has been found to be a contributing factor in raising the resulting severity of a crash ( 60 ) Similar to the previous study, the multi-vehicle truck crash model predicts that speed that is too fast is a significant variable. Among the multi-vehicle crash types, head-on ( O ^ R = 8.519) was found to have the most severe outcome, significantly increasing the odds of fatal/severe injury in the TTC model.…”

“…Crash Characteristics. Crashes between trucks and other vehicles are expected to result in fatality/severe injury (17,24,60). As the number of vehicles in a crash increases, it is expected that the odds of fatal/injury increase by 1.786 times and 1.391 times, found from the total truck crash and multi-vehicle truck crash models, respectively.…”

“…When vehicles collide with a fixed object, such as signs, delineator posts, or fences, as the first harmful event, the resulting severity outcome is likely to be minor injury or no injury ( 24 , 60 , 62 ). However, the results in this study show that truck crashes are likely to result in a fatal/severe injury when colliding with a fixed object as inferred from the total truck crash and single-truck crash models, O ^ R = 3.285 and O ^ R = 4.975, respectively.…”