The transition to sustainable mobility is a recognized socio-economic and environmental challenge, particularly among young adults. In addressing the gap in the literature on young adults’ travel behaviors, especially in Cameroon, this paper investigates the transport mode choices, influencing factors, and barriers to sustainable mobility of students at the National Advanced School of Public Works, Yaoundé (NASPW). Data were collected through online questionnaires with 360 valid responses. Findings revealed that most students used multiple modes of transport for commuting, with moto-taxis being the most common. Accessibility, vehicle speed, and flexibility appeared as the most important reasons for the preferred transport modes, while driver’s license possession, safety perceptions, speed, and proximity were significant predictors for mode choice. Demographic factors were found to influence transport preferences, with distinct clusters prioritizing different aspects. Barriers to public transport were primarily long waiting times and congestion, while active mobility was hindered by distance, infrastructure, and weather. The usage of public transportation was encouraged by its affordability and reduced travel time, whilst active options were preferred due to cost savings and health benefits. To promote sustainable mobility for campus travel, it is crucial to encourage active modes, develop mass transport systems, and raise awareness through symposia and conferences among students and staff.

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.

In Yaoundé, Cameroon, where walking dominates transport modes, pedestrian safety remains an issue as pedestrians account for a fair share of road traffic casualties, partly due to the lack of walking policies and pedestrian facilities safety data, hindering targeted intervention. This study used a pedestrian safety index (PSI) and the Global Walkability Index (GWI) to investigate 12 road segments frequented by diverse pedestrian groups. Indexes were graded from E—lowest to A—highest and analyzed using description and rank correlation. Main safety issues included lack of adequate and accessible sidewalks, bollards, pedestrian crossings, signage, shade, and street lighting. Only one segment (R7) achieved grade C, while the remainder scored D or E, indicating poor pedestrian safety conditions and an unpleasant walking experience. The correlation coefficient (0.69) between the PSI and GWI at a 99% significance level validated the safety assessment, providing confidence in the results. A seven-year (2024–2030) safety strategy is proposed to improve all roads to grade B. This strategy contains several interventions, including engineering improvement, which have been proven effective. This study offers evidence for city officials to improve pedestrian safety and informs walking policies and the implementation of upcoming projects. Future research should quantify the recommendations’ benefits and validate indexes with crash or conflict data.