Road traffic crashes claim approximately 1.19 million lives annually worldwide, with low- and middle-income countries (LMICs) bearing a disproportionately high share of this burden. Intersections in these contexts are particularly hazardous due to mixed, non-lane-based traffic and infrastructural constraints. This study analysed 1242 police-reported intersection crashes (2021–2025) from Douala and Yaoundé, Cameroon, using binary probit and logistic regression models to identify infrastructural and environmental determinants of crash severity. Results from both models were consistent, indicating that late-night and early-morning crashes (00:00–05:59) significantly increased the probability of severe outcomes by 13.5% (p-value < 0.05), while single-lane roads raised it by 21.5% (p-value < 0.05; OR = 5.38), and two-lane roads raised the probability by 9.1% (p-value < 0.05; OR = 3.90) compared with multilane sections. Additionally, centre lines were associated with safer outcomes than physical separation (p-value < 0.05; OR = 0.30). Although model fit indices were modest (Nagelkerke R² = 0.118), typical of cross-sectional crash-severity models, the findings underscore the dominant influence of road geometry and lighting-related temporal exposure in shaping intersection crash outcomes. These insights provide a basis for targeted interventions such as road widening, improved night-time illumination, and simplified midblock designs to enhance safety in Cameroon and similar LMIC urban settings.

Real-time crash risk assessment is a key component of proactive road safety management, enabling the identification of hazardous conditions within short temporal intervals before crashes occur. Traditional crash-based models are unsuitable for such applications due to the rarity, reporting delay, and stochastic nature of crash data. Traffic conflicts, capturing near-miss interactions between road users, provide a practical alternative for real-time safety analysis. Over the past decade, numerous modelling approaches have been developed to translate conflict information into crash risk estimates; however, the literature remains fragmented and lacks a unified analytical synthesis. This review presents a state-of-the-art, model-centric analysis of conflict-based approaches, classifying them into five paradigms: statistical/regression-based, Bayesian, extreme value theory (EVT), machine learning (ML), and hybrid models. Beyond classification, the study conducts a structured cross-paradigm comparison across key dimensions, including conflict representation, data characteristics, temporal modelling, uncertainty treatment, validation strategies, computational complexity, and operational readiness. The paradigms are further interpreted through the complementary lenses of conflict frequency and severity. The review identifies key research gaps, including fragmented conflict definitions, challenges in modelling rare and extreme events, incomplete treatment of uncertainty and spatiotemporal dynamics, and limitations in validation, transferability, and deployment. Emerging research directions include standardized and adaptive conflict indicators, EVT–machine learning integration, integrated uncertainty-aware frameworks, advanced spatiotemporal modelling, transferable models, and scalable real-time implementation. By combining structured evidence mapping and cross-paradigm synthesis, this study supports model selection, development, and deployment for dynamic crash risk assessment.

Urban mobility in Yaoundé is increasingly constrained by rapid demographic growth, unbalanced spatial development, and the absence of structured public transport. Daily commutes between peripheral neighbourhoods and the city centre are characterised by long travel times, heavy congestion, and strong reliance on informal modes. This study addresses the central question of which factors explain long commuting times in Yaoundé, and how they can be mitigated to improve daily mobility conditions. The analysis, based on a survey of 214 respondents (205 valid cases), complemented by spatial analysis, used a binary logit model to estimate the factors affecting the duration of commutes. The results show that over 18% of commuters report trips exceeding one hour, mostly using shared taxis and private cars. There is a significant use of motorcycle taxis to meander in traffic and reduce travel times often without regard for safety. Distance from the city centre, presence of traffic congestion and absence of reliable public transport are the most significant determinants of long commutes, whereas motorcycle taxis reduce the travel duration. Spatial analysis highlights recurrent bottlenecks where large incoming flows converge without adequate infrastructure or effective traffic management. Policy recommendations for efficient pendular mobility in Yaoundé include structuring public transport corridors with options, improved city planning, and strengthening governance through a metropolitan mobility authority, and regulating informal modes. These findings underline the urgency of integrated and inclusive strategies to foster sustainable commuting in Yaoundé.