Urban traffic noise has become an increasingly significant environmental and public health issue, with many cities—particularly those experiencing rapid urban growth, such as Kuwait—recording levels that often exceed recommended limits. In this study, we present a detailed, data-driven approach for assessing and predicting equivalent continuous noise levels (LAeq) in residential neighborhoods. The analysis draws on measurements taken at 12 carefully chosen sites covering different road types and urban settings, resulting in 21,720 matched observations. A range of predictors was considered, including road classification, traffic composition, meteorological variables, spatial context, and time of day. Four predictive models—Linear Regression, Support Vector Machine (SVM), Gaussian Process Regression, and Bagged Trees—were evaluated through 5-fold cross-validation. Among these, the Bagged Trees model achieved the strongest performance (R² = 0.91, RMSE = 2.13 dB(A)). To better understand how the model made its predictions, we used SHAP (SHapley Additive Explanations) analysis, which showed that road classification, location, heavy vehicle volume, and time of day had the greatest influence on noise levels. The results identify the main determinants of traffic noise in Kuwait’s urban areas and emphasize the role of targeted design and planning in its mitigation.

One of the important factors of road safety is drivers’ behavior. It is very useful to examine the factors of driver behavior to reduce the human factors to improve road safety. The objective of this study is to employ Manchester driver behavior questionnaire (DBQ) to measure various types of driver behavior in Kuwait. Data collection was performed by using an online questionnaire. The factor analysis was used to examine the data which compute into three factors structure. However, these three factors could not be classified into errors, lapses and violations categories as other studied due to social desirability bias. The future studies should examine this issue further.

This study examines public perceptions of electric vehicle (EV) adoption in Kuwait, a high-income petroleum-dependent country characterized by highly subsidized electricity, low fuel prices, limited charging infrastructure, and extreme climatic conditions. Using a structured survey of 1753 licensed drivers, the study evaluates how economic incentives, practical constraints, environmental perceptions, technological confidence, and safety concerns shape expectations of future EV diffusion. Descriptive statistics, principal component analysis, and ordinal logistic regression were used to examine the factors associated with respondents’ expectation of widespread EV adoption in Kuwait over the next ten years. The regression results show that low-tariff/delayed-bill perception was the strongest positive predictor of expected EV adoption, indicating that Kuwait’s low-cost electricity environment may strengthen expectations of EV diffusion. However, the findings also demonstrate that electricity tariffs alone do not explain public expectations. EV performance perception, environmental benefit perception, workplace charging, battery warranty, prior passenger experience in an EV, and higher weekly fuel expenditure were also positively associated with stronger expectations of EV adoption. In contrast, perceived complexity was negatively associated with expected adoption, highlighting the importance of consumer familiarity and ease of use. Safety-related perceptions, particularly concerns regarding EV fire-extinguishing difficulty and lower perceived safety compared with conventional vehicles, were also significant, suggesting that fire safety remains a salient issue in the Kuwaiti context. The findings contribute to the literature on sustainable transportation adoption in petroleum-based economies and extreme climates by showing that EV diffusion depends on a combination of economic, infrastructural, technological, environmental, and safety-related factors. Policy efforts in Kuwait should therefore combine charging-infrastructure development, workplace charging expansion, consumer education, battery-warranty assurance, and EV-specific safety and emergency-response measures.

The 1991 Gulf War contaminated more than 49 km² of Kuwaiti desert with hydrocarbon spills, a persistent threat to soil resources, infrastructure and the United Nations Sustainable Development Goals embedded in Kuwait Vision 2035. Managing these legacy lands calls for predictive tools that capture spatial variability while remaining computationally tractable and statistically defensible at the small sample sizes typical of post-conflict monitoring. This study develops a multi-resolution physics-informed neural network that combines wavelet-based parameter encoding, scale-dependent regularisation and a progressive upsampling training protocol. The framework is evaluated on nine trial-pit observations at a single depth of 30 cm in the Al-Ahmadi field, where the contaminated pits show a mean internal friction angle of 26.8° compared with 36.0° at co-located control pits sampled at the same time. Generalisation is assessed by leave-one-out cross-validation across the nine locations. The framework attains a friction-angle root-mean-square error of 1.29°. Under the same data and compute budget, ordinary kriging and a standard physics-informed neural network remain statistically competitive. This outcome indicates that the physics residual acts as a mass-conservation-consistent smoothness regulariser rather than a site-calibrated transport predictor. A multi-objective remediation workflow produces a cost-versus-residual-risk Pareto front for a scenario-specific 1–2 km² case, presented as an illustrative decision-support envelope pending external pilot calibration. A projected pathway from these outcomes to six Sustainable Development Goals and two pillars of Kuwait Vision 2035 is also discussed; quantitative attribution at this sample size is beyond scope. The small-sample, single-depth and single-locality limitations that bound the admissible inference are stated explicitly.

The Volume 7 published for IJCI Journal.