The influence of road surface condition on road safety / Nurulhikmah Binti Haji Husaini
Material type:
TextPublication details: Bandar Seri Begawan : Universiti Teknologi Brunei, ©2019. Description: 119 pages : coloured illustrations, charts, tables ; 30 cmSubject(s): -- Project Report Universiti Teknologi Brunei | Thesis Writing | Project Report, Academic | Project Report Universiti Teknologi Brunei | Roads -- Safety measures | Surface -- Safety measures | Traffic engineering -- Safety measures | Traffic safetyOther classification: RTDS 344 | UTB 120 REPORT THESIS & DISSERTATION, RTDS 344
| Item type | Current library | Call number | Copy number | Status | Notes | Date due | Barcode |
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Reports, Thesis & Dissertation Students
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Universiti Teknologi Brunei Library - at level 2 | UTB 120 REPORT THESIS & DISSERTATION, RTDS 344 (Browse shelf(Opens below)) | 1 | Not for loan | Reg. No._UTB [RTDS 344] | 850388 |
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Submitted in fulfillment of the requirements for the degree of Master of Science.
Abstract
Road surface condition is seen as one of the most important factors that affect the safety of a land transport mode. Although various researchers used different measures of pavement surface condition, only a few focused on how road roughness, particularly quantified by International Roughness Index (IRI), may contribute to Road Traffic Accidents (RTA) occurrences. This thesis presents an analysis of the influence of IRI and other various road properties statistically and spatially on the crash risk on a high-traffic volume and high-speed highway section in Brunei Darussalam. A Negative Binomial regression (NBREG) method has been used to model the RTA occurrences and with that, an RTA prediction model was developed. Kernel Density Estimation Plus (KDE+) method was selected to identify high number of RTA with high crash risk along the concerned road. The outcome of the analysis has shown that the IRI values have a weak correlation with the observed number of all types of RTA given that other explanatory variables are included into the regression model. Limitations of the statistical regression acquired are discussed and recommendations to refine the selected variables and analysis methods are provided.
Reports, Thesis & Dissertation Students
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