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

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.

Spatial Variation and Temporal Instability in the Growth/Climate Relationship of White Birch (Betula platyphylla Suk) in the Changbai Mountain, China

Tree growth in mountain ecosystems is affected by complex environments, and its relationship with climatic and environmental factors varying with elevation. In order to examine the spatial variation and temporal stability of the growth/climate relationship of Betula platyphylla (BP), the dendrochronological method was used to analyze the radial growth/climate relationship between 1946 and 2016 of the BP trees along an altitudinal gradient in the Changbai Mountain of northeast China. Our results showed that the mean sensitivity of BP was higher than that of other species studied in Changbai Mountain. The growth/climate relationship of BP trees varies with altitude, and this conclusion has reached a consensus from the study of tree growth response to climate change. More specifically, at low altitudes (550–995 m a.s.l.), the radial growth of BP is mainly affected by spring precipitation and temperature in May and October of the current year. However, at high-altitude areas (1210–1425 m a.s.l.), it is mainly affected by the temperature in September of the previous year and May of the current year. Furthermore, the growth/climate relationship of BP trees showed temporal instability. After 1970, the rise in temperature inhibited the growth of BP at low altitudes and promoted the growth of BP trees at high altitudes. In the context of continued warming in the future, the white birch stands in Changbai Mountain will move to higher altitudes.