Tricycles contribute to road crashes globally, especially in Asia where it is a popular means of transport. Safety assessment of tricycles has discovered that associated fatality rate and severity of injuries from tricycle crashes, which has risen steadily, are due to their flawed designs especially the absence of seatbelts and paddings of hard surfaces. Tricycle related crashes were sourced weekly from the regional office of the National Road Safety Authority from January 2012 to June 2022 and modelled with Autoregressive Integrated Moving Average Model. The results showed ARIMA (1,0,3) errors were retained as the best model for estimating road fatalities caused by tricycle crashes. The exogenous factors: tricycle crashes, time of day, season of the year, and road design were tested and found to be statistically significant relative to its contribution to the fatalities. The time series components were deemed to be relevant contributing factors to fatalities for the study period. Further, crash data forecasted to October 2022 largely exhibited a sinusoidal behaviour with spikes at various weeks. It was recommended that tricycle drivers undergo mandatory refresher courses on safe driving techniques and general road safety regulations as a policy intervention. In addition, the licensing regime for tricycles should be strengthened, and government should create separate lanes along some major routes within the metropolitan, municipal, and district assemblies to eliminate or reduce the negative incidents.

Globally, the rise in car ownership and usage has intensified parking challenges, particularly within central business districts (CBDs) of many developed cities. Scarce parking infrastructure and escalating land values have further exacerbated these issues, leading to heightened competition among business owners, residents, shoppers, and clients for the limited available paid and free on-street parking spaces. Against this backdrop, the present study sought to model the determinants of motorists’ parking challenges using a recursive structural equation model (RSEM), drawing on empirical evidence from Greater Kumasi, Ghana. Primary data were collected through a structured survey involving 1000 drivers within the designated catchment area, employing cluster and systematic sampling techniques to ensure representativeness. The findings reveal that four out of five structural paths of the constructs exerted significant influences on the structural model components. Both time-related indices and parking costs demonstrated direct and indirect effects on parking challenges, with vehicle type serving as a mediating variable. Furthermore, most of the measurement models significantly impacted the latent factors, either positively or negatively, highlighting the complex interrelationships between parking behavior and underlying determinants. Overall, this study makes several contributions: it provides localized empirical evidence from a developing-country context, offers theoretical refinements to existing models, demonstrates methodological rigor through the application of RSEM, and proposes practical policy insights to address urban parking challenges in rapidly growing African cities such as Kumasi.

The rapid rise in the use of Liquefied Petroleum Gas (LPG) as an alternative vehicle fuel in Ghana presents both opportunities and risks within the national energy transition agenda. This study investigates LPG safety as well as environmental and regulatory implications using a multi-method quantitative approach that combines structured survey data, exploratory multivariate analysis (MCA), and machine learning classification (Random Forest) to uncover emerging associations and patterns in LPG safety practices. Primary data were obtained from 384 respondents, including vehicle operators, auto-technicians, regulatory officials, and LPG station attendants across five major transport zones: Kejetia, Asafo, Ahodwo, Bantama, and Suame Magazine. The MCA identified four distinct behavioural and safety profiles—At-Risk, Proactive Safety, Compliant and Equipped, and Formal and Reported—reflecting diverse compliance and risk patterns across socio-occupational groups. The Random Forest classifier achieved a predictive accuracy of 96.5% based on cross-validated performance. Sensitivity and specificity values were high, indicating reliable discrimination among incident types. To reduce the risk of overfitting, k-fold cross-validation and monitored error convergence were performed across increasing numbers of trees. While the model shows strong predictive capability, we present these results cautiously and emphasize observed associations and emerging patterns rather than definitive predictive conclusions. The findings reveal that while economic motivations underpin LPG adoption, weak institutional enforcement and widespread informal installations heighten safety vulnerabilities. Comparisons with sub-Saharan and Asian contexts underscore the need for a structured regulatory framework, mandatory certification of installers, and periodic vehicle inspections. The study contributes to the broader discourse on informal energy transitions in developing economies by demonstrating how technical and behavioural determinants interact within weak regulatory systems. Policy recommendations emphasize the integration of data-driven risk assessment tools into regulatory oversight to enhance vehicular LPG safety and sustainability.

The mining industry in Ghana, despite its economic significance, continues to grapple with occupational health and safety (OHS) issues, which include but are not limited to high accident rates and a largely reactive approach to safety. In this study, current practices in OHS management are assessed and a model is developed that reflects the interdependencies and associations between workplace hazards, accidents, health effects and preventative actions in a probabilistic fashion. A quantitative analytical design was employed and a sample of 298 workers, safety officers and supervisors from mines were surveyed. The data were analysed using Principal Component Analysis (PCA) to derive latent OHS factors, K-modes clustering and Hierarchical Clustering to classify worker safety profiles, and a Bayesian Network model was employed to investigate probabilistic dependencies. Three major OHS dimensions emerged from PCA: perceived adequacy of safety measures, formal training exposure, and safety resources. Three distinct worker profiles were identified, suggesting that the provision of physical safety equipment does not necessarily reflect perceived operational safety. Moreover, the Bayesian Network model indicated a conditional dependency between workers’ reported health issues and formal accident reporting, and that high hazard environments more than double the probability of an accident occurring (from 0.216 to 0.453). The results indicate that the industry is now operating in an incident-based manner. In order to mitigate the likelihood of accidents, management needs to move towards anticipatory safety management systems, which involve proactive health monitoring, equipment maintenance, and implementation of safety policies in practice.

Ghana’s automotive sector has significant opportunities for the transition from Internal Combustion (IC) to electric vehicles. Current training remains largely IC-engine focused. TVET programmes, apprenticeship training, and automotive education curricula must align with emerging EV technologies and evolving workforce needs. This study aimed to assess automotive graduates’ preparedness for electric vehicle (EV) repair and maintenance in Kumasi, Ghana, identify critical competency gaps, and determine priorities for strengthening workforce readiness. A cross-sectional survey was conducted among automotive engineering graduates, technicians, industry professionals, and academic staff in Kumasi, Ghana, using a multi-stage stratified sampling approach. A total of 753 eligible respondents provided usable responses to the structured online questionnaire. Data were analysed using descriptive and inferential statistical techniques, and all statistical analyses were performed in R version 4.3.2 and IBM SPSS Statistics version 20. The study revealed a substantial competency gap in EV knowledge, technical skills, and practical competencies, with 51.1% of respondents lacking knowledge in EV fault diagnosis. The findings underscore the need to prioritize periodic practical EV repair and maintenance training, industry internships and apprenticeships, and stronger collaboration between industry and training institutions to enhance graduate competencies and workforce readiness for the transition to electric mobility in Ghana.