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Abstract. Road T raffic Accidents (RT A) are known to be one of the main causes of fatalities worldwide. One usef ul approach to improve road safety is through the identification of RT A hotspots along a road, so they can be prioritised and treated. T his paper introduces an approach based on Geographical Information System (GI S) to identify and prioritise RT A hotspots along a road network using historical RT A data. One particular urban road in Brunei with a historically high rate of RT As, Jalan Gadong, was selected as a case study. Five years of historical RT A data were acquired from the relevant authorities and input into a GIS database. GI S analysis was then used to identify the spatial extension of the RT A hotspots. The RT A hotspots were ranked according to three different schemes: frequency, severity and socio-economic impact of RT As. A composite ranking scheme was also developed to combine these schemes; this enabled the prioritisation and development of intervention and maintenance programmes of the identified RT A hotspots. A visualisation method of the RT A spatial distribution within each identified RT A hotspot was also developed to determine the most risky road stretches within each hotspot, which is important for treatment prioritisation when limited resources are available.

An increase in sediment load resulting from extreme weather event can affect the capacity of existing water infrastructure, for example, decreasing reservoir capacities, creating obstacles and reducing the navigation depth, or eroding bridge piers by scouring actions. A number of studies have been carried out on factors affecting sediment yield and transport but only a few studies being done on the combination of both rainfall and flow on the sediment load. Therefore, the aim of this study is to identify the impact on sediment load comprising of well-graded silica sand due to combined effect of flow and rainfall. This research has two objectives; firstly, to study the relationship between flow, rainfall, and sediment load and secondly to devise an experiment to investigate how combination of flow and rainfall could affect sediment load with the help of Advanced Environmental Hydrology System. Thirty-six sets of experiments were conducted on a 2 m long, 0.2 m wide and 0.15 m deep channel, moulded in the Armfield S12 MKII on a 1% constant slope with six different readings of rainfall ranging from 6 to 72 mm/hr and by varying the flow ranging from 0.5 to 3.0 L/min to observe the different trends and changes to sediment load when rainfall and flow varies. This experimental study demonstrates a combination of both rainfall and flow resulted in a stronger linear correlation with sediment load.

In order to establish objective criteria for road traffic accident (RTA) hotspots, this paper examines the application of three different hotspot analysis methods to both identify and rank the RTA hotspots. The three methods selected are the network Kernel Density Estimation (KDE+) method, the Getis-Ord GI* method, and a recently proposed risk-based method that accounts for RTA frequency, severity and socioeconomic costs -STAA method. The study road, Jalan Tutong, is a major dual-carriageway connecting major residential and commercial areas from the west of Brunei-Muara district and beyond to the capital, Bandar Seri Begawan. The RTA data consists of cases reported to the police during a 5-year period from 2012 to 2016. The RTA data were digitised and prepared, before being imported into ESRI ArcGIS 10.2 software for analysis using each of these methods. The outcomes, particularly the location, extent and priority of the RTA hotspots, are subsequently compared to results from road safety audits, in order to determine the relative merits and drawbacks of each method. The findings from the comparative study would be useful to recommend the most suitable method to identify and rank the RTA hotspots for the study road.