Background: Present intensifying forest fire incidents are becoming a major concern as they pose threats to the environment, economy and human’s safety. This paper aims to identify forest fire hotspots using advanced hotspot analysis in the Geographical Information Systems (GIS), and suggest appropriate preventive measures.Objective: Brunei Darussalam is one of the Southeast Asian countries that is significantly affected by the increase in forest fire incidents in this region. Brunei Muara is the worst-hit district by forest fire in Brunei Darussalam, and hence it was selected as a case study for this research.Results and Conclusion: This research identified and prioritized forest fire hotspots, highlighted the shortage of fire stations within the identified hotspots and suggested the suitable locations for new fire stations in Brunei Muara district.

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.

There is a growing global interest in preserving transportation infrastructure. This necessitates routine evaluation and timely maintenance of road networks. The effectiveness of pavement management systems (PMSs) heavily relies on accurate pavement deterioration models. However, there are limited comparative studies on modeling approaches for rural roads in arid climatic conditions using the same datasets for training and testing. This study compares three approaches for developing a pavement condition index (PCI) model as a function of pavement age: classical regression, machine learning, and deep learning. The PCI is a pavement management index widely adopted by many road agencies. A dataset on pavement age and distress was collected over a twenty-year period to develop reliable predictive models. The results demonstrate that the regression model, machine learning model, and the deep learning model achieved a coefficient of determination ($R^2$) of 0.973, 0.975, and 0.978, respectively. While these values are technically equal, the average bias for the deep learning model (1.14) was significantly lower than that of the other two models, signaling its superiority. Additionally, the trend predicted by the deep learning model showed more distinct phases of PCI deterioration with age than the machine learning model. The latter exhibited a wider range of PCI deterioration rates over time compared to the regression model. The deep learning model outperforms a recently developed regression model for a similar region. These findings highlight the potential of using deep learning to estimate pavement surface conditions accurately and its efficacy in capturing the PCI-age relationship.

Municipalities and transportation departments worldwide are striving to keep road pavement conditions acceptable, thus enhancing pavement sustainability. Although the pavement condition index (PCI) is widely used to assess distress conditions, traditional visual surveys used for PCI estimation can be laborious, expensive, and time-consuming. The international roughness index (IRI) can be measured more economically and conveniently than PCI; however, it does not directly indicate the surface condition of the pavement. In this study, a PCI–IRI correlation is proposed for urban roads located in the New Beni-Suef region, Egypt. For this purpose, a total of 44 km of urban roads was divided into homogenous sections. A visual distress survey was conducted to measure PCI considering typical distress patterns. The IRI values for the same sections were measured using an ultrasonic distance sensor mounted on an automobile. An exponential model was proposed to capture the relationship between IRI and PCI. With a coefficient of determination of 0.82, the exponential model seems to outperform reported IRI-PCI correlations. Model validation, along with a comparison to the existing models, supports its applicability to a wide range of roads. The proposed model provides a cost-effective means for accurately predicting PCI based on IRI, which is particularly useful for pavement maintenance management programs on limited budgets.

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.