METHOD: This study proposes a combination of decision tree and logistic regression techniques to model crash severity (injury vs. noninjury), because the combined approach allows the specification of nonlinearities and interactions in addition to main effects. Both a scobit model and a random parameters logit model, respectively accounting for an imbalance response variable and unobserved heterogeneities, are tested and compared. The study data set contains a total of 5 years of crash data (2008-2012) on selected mountainous highways in Malaysia. To enrich the data quality, an extensive field survey was conducted to collect detailed information on horizontal alignment, longitudinal grades, cross-section elements, and roadside features. In addition, weather condition data from the meteorology department were merged using the time stamp and proximity measures in AutoCAD-Geolocation.
RESULTS: The random parameters logit model is found to outperform both the standard logit and scobit models, suggesting the importance of accounting for unobserved heterogeneity in crash severity models. Results suggest that proportion of segment lengths with simple curves, presence of horizontal curves along steep gradients, highway segments with unsealed shoulders, and highway segments with cliffs along both sides are positively associated with injury-producing crashes along rural mountainous highways. Interestingly, crashes during rainy conditions are associated with crashes that are less likely to involve injury. It is also found that the likelihood of injury-producing crashes decreases for rear-end collisions but increases for head-on collisions and crashes involving heavy vehicles. A higher order interaction suggests that single-vehicle crashes involving light and medium-sized vehicles are less severe along straight sections compared to road sections with horizontal curves. One the other hand, crash severity is higher when heavy vehicles are involved in crashes as single vehicles traveling along straight segments of rural mountainous highways.
CONCLUSION: In addition to unobserved heterogeneity, it is important to account for higher order interactions to have a better understanding of factors that influence crash severity. A proper understanding of these factors will help develop targeted countermeasures to improve road safety along rural mountainous highways.
RESULTS: The results of the study show that total phenolic content (TPC) in soil and leaves of three species of Macaranga were highest in TPSF followed by freshwater swamp forest and flooded limestone forest, then dry land sites. Highest TPC values were associated with acidity (in TPSF) and waterlogging (in flooded forests). Moreover, phenolic compounds are rapidly leached from fallen senescent leaves, and could be reabsorbed by tree roots and converted into more complex phenolics within the leaves.
CONCLUSIONS: Extreme conditions-waterlogging and acidity-may facilitate uptake and synthesis of protective phenolic compounds which are essential for impeded decomposition of organic matter in TPSF. Conversely, the ongoing drainage and degradation of TPSF, particularly for conversion to oil palm plantations, reverses the conditions necessary for peat accretion and carbon sequestration.