In the pursuit of sustainable mobility and the decarbonization of transport systems, public authorities are increasingly scrutinizing the impact of travel speed on emissions within both low-speed and high-speed environments. This study critically examines the evidence concerning emission impacts associated with speed limit changes in different traffic environments by conducting a systematic review of the literature in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 25 studies that met the eligibility criteria were assessed. The results reveal mixed evidence for reducing emissions through speed limit reductions in low-speed areas. However, emerging evidence suggests that reduced urban speeds may abate emissions through enhanced traffic flow and a shift in modal preferences away from personal vehicle use. Additionally, in urban areas, minor observed emission reduction per vehicle can add up to large overall reductions due to the high number of vehicles. In high-speed contexts, the evidence is much clearer, showing that reduced speed limits correlate with significant reductions in NOx, CO₂, and particulate matter emissions. The extent of these reductions is highly variable and contingent upon the specific speed limits or limit reductions, the local context, the vehicle type, and the baseline types and levels of pollutants. Notably, there is a lack of research on the effects of speed on emissions, especially in low- and middle-income countries (LMICs), highlighting a critical area for future investigation. The findings of this study underscore the potential environmental benefits of speed management policies and advocate for the promotion of smoother and less aggressive driving behavior to mitigate emissions and enhance sustainable mobility in both low-speed and high-speed settings.

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.

Speeding is a significant global issue, with disparities in speed and safety outcomes between low- and middle-income countries (LMICs) and high-income countries (HICs). This study aims to address speed research gaps in LMICs by examining the prevalence of self-reported speeding and factors influencing drivers’ speeding behavior using the Theory of Planned Behavior (TPB). An online survey involving 387 anonymous drivers was conducted in Yaoundé. Results showed that all drivers reported exceeding speed limits, with 81% exceeding them by 5 km/h, 12% by 10 km/h, and 7% by more than 10 km/h in urban areas. On highways, 54% reported exceeding the limit by more than 10 km/h. Age, driver’s license, and gender showed significant associations with speed limit non-compliance. This study’s results suggest that the TPB can be useful in assessing speeding behavior, as the inclusion of TPB variables led to a more than 50% proportionate increase in speeding behavior variance. Structural equation modeling revealed attitude towards speeding as the strongest predictor of speeding intention, along with certain demographics indirectly influencing speeding behavior through speeding intention. Perceived behavioral control and speeding intention directly influenced speeding behavior. These findings emphasize the potential of interventions targeting attitude, intentions, and perceived behavioral control to modify speeding behavior and improve road safety.

The transition to sustainable mobility is a recognized socio-economic and environmental challenge, particularly among young adults in low- and middle-income countries (LMICs). This paper addresses the lack of comprehensive research on mobility patterns for LMICs by examining the travel patterns of students, staff, and lecturers at the National Advanced School of Public Works, Yaoundé (NASPW) to understand transport mode choices and barriers to the use of public and active transport modes. Data was collected through online questionnaires from 425 participants. Findings revealed that most students (27.5%) used multiple modes of transport, with moto-taxis being the most common (21%). Lecturers primarily used private cars (50%), while staff relied on taxis or multiple modes (33%). Accessibility, vehicle speed, and flexibility appeared as the most important reasons for the preferred modes of transport. Barriers included long waiting times and traffic congestion for public transport, and distance and inadequate infrastructure for active mobility. The usage of public transportation was encouraged by its affordability and reduced travel time, whilst active options were preferred due to their 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 symposiums and conferences among students and staff.

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.