ORCID Profile
0000-0002-9274-8811
Current Organisations
Universiti Teknologi MARA
,
Queensland University of Technology
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Publisher: Elsevier BV
Date: 12-2022
Publisher: No publisher found
Date: 2018
DOI: 10.1080/15389588.2018.1482537
Abstract: Traffic crashes along mountainous highways may lead to injuries and fatalities more often than along highways on plain topography however, research focusing on the injury outcome of such crashes is relatively scant. The objective of this study was to investigate the factors affecting the likelihood that traffic crashes along rural mountainous highways result in injuries. 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 st and proximity measures in AutoCAD-Geolocation. 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. 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.
Publisher: Elsevier BV
Date: 05-2017
DOI: 10.1016/J.AAP.2017.03.002
Abstract: Mountainous highways generally associate with complex driving environment because of constrained road geometries, limited cross-section elements, inappropriate roadside features, and adverse weather conditions. As a result, single-vehicle (SV) crashes are overrepresented along mountainous roads, particularly in developing countries, but little attention is known about the roadway geometric, traffic and weather factors contributing to these SV crashes. As such, the main objective of the present study is to investigate SV crashes using detailed data obtained from a rigorous site survey and existing databases. The final dataset included a total of 56 variables representing road geometries including horizontal and vertical alignment, traffic characteristics, real-time weather condition, cross-sectional elements, roadside features, and spatial characteristics. To account for structured heterogeneities resulting from multiple observations within a site and other unobserved heterogeneities, the study applied a random parameters negative binomial model. Results suggest that rainfall during the crash is positively associated with SV crashes, but real-time visibility is negatively associated. The presence of a road shoulder, particularly a bitumen shoulder or wider shoulders, along mountainous highways is associated with less SV crashes. While speeding along downgrade slopes increases the likelihood of SV crashes, proper delineation decreases the likelihood. Findings of this study have significant implications for designing safer highways in mountainous areas, particularly in the context of a developing country.
Publisher: American Physiological Society
Date: 06-2001
DOI: 10.1152/JAPPL.2001.90.6.2420
Abstract: Heat exposure early in ovine pregnancy results in placental insufficiency and intrauterine growth restriction (PI-IUGR). We hypothesized that heat exposure in this model disrupts placental structure and reduces placental endothelial nitric oxide synthase (eNOS) protein expression. We measured eNOS protein content and performed immunohistochemistry for eNOS in placentas from thermoneutral (TN) and hyperthermic (HT) animals killed at midgestation (90 days). Placental histomorphometry was compared between groups. Compared with the TN controls, the HT group showed reduced delivery weights (457 ± 49 vs. 631 ± 21 g P 0.05) and a trend for reduced placentome weights (288 ± 61 vs. 554 ± 122 g P = 0.09). Cotyledon eNOS protein content was reduced by 50% in the HT group ( P 0.03). eNOS localized similarly to the vascular endothelium and binucleated cells (BNCs) within the trophoblast of both experimental groups. HT cotyledons showed a reduction in the ratio of fetal to maternal stromal tissue (1.36 ± 0.36 vs. 3.59 ± 1.2 P≤ 0.03). We conclude that eNOS protein expression is reduced in this model of PI-IUGR and that eNOS localizes to both vascular endothelium and the BNC. We speculate that disruption of normal vascular development and BNC eNOS production and function leads to abnormal placental vascular tone and blood flow in this model of PI-IUGR.
Publisher: Elsevier BV
Date: 10-2018
DOI: 10.1016/J.AAP.2018.07.006
Abstract: Road safety in rural mountainous areas is a major concern as mountainous highways represent a complex road traffic environment due to complex topology and extreme weather conditions and are associated with more severe crashes compared to crashes along roads in flatter areas. The use of crash modelling to identify crash contributing factors along rural mountainous highways suffers from limitations in data availability, particularly in developing countries like Malaysia, and related challenges due to the presence of excess zero observations. To address these challenges, the objective of this study was to develop a safety performance function for multi-vehicle crashes along rural mountainous highways in Malaysia. To overcome the data limitations, an in-depth field survey, in addition to utilization of secondary data sources, was carried out to collect relevant information including roadway geometric factors, traffic characteristics, real-time weather conditions, cross-sectional elements, roadside features, and spatial characteristics. To address heterogeneity resulting from excess zeros, three specialized modelling techniques for excess zeros including Random Parameters Negative Binomial (RPNB), Random Parameters Negative Binomial - Lindley (RPNB-L) and Random Parameters Negative Binomial - Generalized Exponential (RPNB-GE) were employed. Results showed that the RPNB-L model outperformed the other two models in terms of prediction ability and model fit. It was found that heavy rainfall at the time of crash and the presence of minor junctions along mountainous highways increase the likelihood of multi-vehicle crashes, while the presence of horizontal curves along a steep gradient, the presence of a passing lane and presence of road delineation decrease the likelihood of multi-vehicle crashes. Findings of this study have significant implications for road safety along rural mountainous highways, particularly in the context of developing countries.
Publisher: Elsevier BV
Date: 10-2020
No related grants have been discovered for Rusdi Rusli.