Road safety is closely related to geometric design consistency, which is usually assessed by examining operating speed. Most consistency models only consider passenger car speeds, even though the interaction between passenger cars and heavy vehicles plays a pivotal role in road safety. This is due to the fact that there are too few models to estimate heavy vehicle speeds. This study aims to develop speed prediction models for heavy vehicles on horizontal curves of two-lane rural roads. To do this, continuous speed profiles were collected by using Global Positioning System (GPS) tracking devices on 11 road sections. Truck speeds were analyzed on 105 horizontal curves. The results showed that the radius of the horizontal curve and the grade at the point of curvature have a significant influence on heavy vehicle speeds. In this regard, vertical alignment only has a significant effect on truck speeds along upgrades. In addition, different trends were identified for loaded and unloaded trucks, so different speed models were calibrated for each of them. As a result, heavy vehicle speeds were adversely affected by grades greater than 3%. This phenomenon was larger for loaded trucks than for unloaded ones. Finally, the calibrated 85th and 15th percentile speed models were compared with those developed previously. As a conclusion, the use of the proposed models in this study was recommended on Spanish two-lane rural roads due mainly to the different characteristics of heavy vehicles around the world.

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 are mainly caused by three concurrent factors: infrastructure, vehicle, and human factors. Regarding infrastructure, in recent decades, a series of management tools and procedures called Road Infrastructure Safety Management (RISM) have been proposed. The aim of RISM procedures is to support road authorities in the prevention and mitigation of future road traffic crashes. One of these procedures is the In-built Road Safety Assessment (IRSA) methodology. The peculiarity of an IRSA methodology is the underpinning method used to assign a score to a road section with the aim of identifying those road sections in a network with safety-related infrastructure deficiencies. The objective of this paper is to provide an overall literature review of existing methodologies used worldwide for network-wide road safety assessment for rural road. The review was conducted following the guidelines provided by Joanna Briggs Institute (JBI) and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) checklist 2020. The characteristics of these methodologies were compared with respect to the following research questions: What are the general characteristics of IRSA methodologies? What risk method/index is applied? Which data collection method/technique is used? What types of road parameters are considered for the assessment? What is the level of expertise needed to implement the methodology? Where and how are the results validated? As a result, 14 IRSA methodologies were identified. Also, the review showed that similar road parameters were used including: operating speed, road surface, low curve radius, poor sight distance (horizontal and vertical curves), lane width, undivided road (median type), shoulder width, sight obstructions (landscape, obstacles and vegetation), absence of traffic signs and road markings, traffic flow (AADT), intersection quality and density of intersections/lateral accesses. Despite these similarities, some differences were observed in risk formulation, safety quantities of parameters, level of expertise required, and validation of studies. Researchers may use these findings to develop future road safety assessment methodologies, while road practitioners can make use of this in identifying suitable network-wide assessment methods for safety assessments of road infrastructures. Finally, a series of recommendations for future research work on IRSA methodologies is suggested.

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.

Despite advancements in road safety, Powered Two-Wheelers (PTWs) remain a vulnerable group with disproportionately high crash rates. This paper presents an in-depth analysis of PTW crashes in six European countries, with a case study of Loss of Control in Curves (LoCC), to address the gap between crash causation and prevention. By examining crash causation factors and their linkage to prevention strategies, the study illustrates various approaches for connecting causes and countermeasures. These approaches, which are applicable to different crash scenarios, include looking forward in the crash causation chains, looking backward, looking at only the last cause (critical events), or the first cause, or following a systemic approach. The research introduces a set of guidelines following the safe system approach, aiming to enhance the understanding of crash prevention among policymakers. The systemic approach to countermeasures, bridges the shortcomings of traditional crash causation studies that may exhibit bias or a narrow focus on “root causes”. The proposed approach emphasizes the need for a comprehensive view of crash scenarios (i.e., considering the entire crash causation chain or multiple causation chains) and ensuring that preventive measures address the full spectrum of the system. It also takes in to account external factors such as cost, benefits, and politics, leading to improved road safety outcomes. The study findings are significant for researchers, since it is a step forward in in-depth crash causation studies, as well as road practitioners and policymakers, in providing a strategic framework for more effective and efficient road safety interventions.

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.