Reducing road traffic accidents (RTA) and their socioeconomic costs is an increasingly important priority in many countries. In recent years, many authors have proposed various approaches to analyse historical RTA data using Geographic Information System (GIS) tools, so that locations with high instances or risk of RTA – i.e., RTA hotspots or blackspots – could be identified and ranked. This could then enable limited road engineering resources to be prioritised for remediating more critical RTA hotspots and therefore reducing RTA rates. This paper presents the development of a new validation method to evaluate the application of four different hotspot analysis methods in ESRI ArcGIS 10.2 to identify and rank RTA hotspots using historical RTA data on a section of a road in Brunei Darussalam as a case study. A road safety audit was undertaken for six locations along the study road section to identify the risk levels at these locations. These risk levels were then compared with risk levels at the same six locations computed by four hotspot analysis methods: Network KDE by SANET, KDE+, Getis-Ord Gi*, and a recently proposed risk-based method – STAA – that accounts for RTA frequency, severity and socioeconomic costs. It was found that SANET-KDE overestimated the risk levels of road sections that are free of RTA. The results from KDE + showed the most deviations compared to the identified risk levels by the road safety audit. Getis-Ord Gi* was not able to identify RTA hotspots along the study road section of Jalan Tutong. However, the STAA method provided the most consistent results in comparison with the identified risk levels by the road safety audit. STAA, by having a better validity compared to other methods, provides a promising way to prioritise RTA hotspots for remediation, and thus reducing the associated socioeconomic costs. In addition, these findings demonstrated that the novel method of validation via risk levels determined by road safety audit was able to provide an independent measure of the validity of the hotspot analysis (HSA) methods.

MATEC Web of Conferences, open access proceedings in Materials science, Engineering and Chemistry

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.

The aim of this paper is to investigate the impact of alternate road lighting (i.e. switching off every other road light at selected roadways) on road traffic accident (RTA) hotspots using Spatial Traffic Accident Analysis (STAA) – a GIS hotspot analysis method that takes into account RTA frequency and socio-economic impact. STAA was used to identify and rank into four hierarchical risk levels day-time and night-time RTA hotspots before and after the implementation of alternate road lighting along a study road. Using the day-time RTA hotspots as a comparison group and night-time RTA hotspots as treatment group, the changes to the length of RTA hotspots of the four hierarchical risk levels were evaluated. There was an overall increase in the lengths of RTA hotspots with moderate and serious risk levels, while there was an overall reduction in the lengths of RTA hotspots with minor and significant RTA risk levels. Some of the limitations of the current study were identified and further research is recommended to validate the current findings.

This paper introduces a novel Lithium-Ion Battery (LIB) State-of-Charge (SOC) estimation approach that integrates Akima–Savitzky–Golay curve reconstruction with a Bayesian-optimized, adaptive Extended Kalman Filter (EKF). The method addresses crucial SOC estimation challenges by means of three foundational advancements: (i) a refined open-circuit voltage (OCV)-SOC curve reconstruction grounded in Akima interpolation coupled with Savitzky–Golay filtering, (ii) an adaptive EKF weighting strategy, and (iii) systematic hyperparameter value optimization executed through Bayesian optimization. Comprehensive performance validation utilizes an extensive dataset collected from LG HG2 18650 cells across temperatures of −20 °C to 40 °C, incorporating multiple standard driving cycles—namely HPPC, UDDS, HWFET, LA92, and US06 cycles. The proposed method achieves an improved estimation accuracy with an average Root Mean Square Error (RMSE) of 2.65% over the different operating conditions and temperature variations. Notably, the method markedly enhances SOC estimation reliability in the critical mid-SOC range (20–80%), while preserving the computational overhead necessary for real-time integration into Battery Management Systems (BMSs). The adaptive weighting successfully compensates for the present physical limitations, thereby delivering a resilient SOC estimation tailored for Electric Vehicle (EV) battery applications.

A vehicle average speed monitoring system (ASMoS) design is proposed for speed management in Brunei Darussalam. This paper explains the principle of a RFID (Radio Frequency Identification)-based speed monitoring system, including all hardware and software considerations. Off-the-shelf industry standard RFID development kit is integrated with a Raspberry-Pi board as the Central Controller Unit (CCU). We adopt a two-point interrogation detection scheme for the calculation of speed over a known distance. Ultra high frequency (UHF) RFID transponders have been used for the proposed design as the key vehicle identifier. The CCU is responsible for establishing a Secure Sockets Layer (SSL) connection with a remote administration server for data storage. The aim of ASMoS is to assist road safety enforcement authorities to monitor and record speeding-related road traffic offences.