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.
METHODS: This was a primary school-based cross-sectional study using multistage cluster sampling, conducted at Bau district in Sarawak, Malaysia in 40 primary schools. A questionnaire was used to collect information of usage pattern in insufficient lighting, timing and position. The physical and behavioural complaints were traced. Data analysis was performed using SPSS version 22. A p-value < 0.05 with 95% CI was considered as statistically significant.
RESULTS: About 52.8% of the 569 students used digital devices in a bright room, 69.8% in the day time and 54.4% in sitting position. The physical complaints were headache (32.9%), neck, shoulder and back pain (32.9%) followed by by eye strain (31.8%). Regarding behavioural problems, 25.7% of the students had loss of interest in study and outdoor activities (20.7%), skipped meals (19.0%) and arguments/disagreements with parents (17.9%). After logistic regression analysis, the lying position (OR=1.71, 95% CI: 1.096, 2.688) and darkroom lighting (OR=2.323 95% CI: 1.138, 4.744) appeared to be potential predictors of the complaint.
CONCLUSION: One-quarter of the students studied experienced physical complaints, and one-fifth had behavioural problems associated with the use of electronic devices. Lying position and darkroom lighting are the potential predictors of complaints. Therefore, we suggest that the children should use electronic devices in the sitting position with adequate room lighting.