Search Results (123)

Developing countries, including Indonesia, are characterized by Heterogeneous Disordered Traffic (HDT), making traditional technology adoption models for autonomous vehicles (AVs) unsuitable. To bridge this gap, this study surveyed 204 urban early adopters using a rigorous psycho-statistical framework. Utilizing t-tests, ANOVA, and bootstrapped ordinal regression, we validated three behavioral pillars: safety-first prioritization, educated critical adoption, and a direct mode shift to shared AVs. Subsequent market segmentation via a CHAID decision tree yielded empirical AV market penetration rates (MPR) of 20%, 40%, and 70%. MPRs were evaluated in a calibrated PTV VISSIM microsimulation of the A.P. Pettarani Arterial Road in Makassar, Indonesia. A tree-based ensemble framework (XGBoost, CatBoost, and Random Forest) with SHAP diagnostics and a Double Machine Learning causal protocol assessed infrastructure trade-offs. With an R² of 0.99 and MAPE of 9.9%, XGBoost strongly predicted intersection delays and road section speeds. Causal analysis shows that putting dedicated lanes in place too soon, when the penetration rate is low (≤20% MPR), makes traffic worse. At an intermediate 40% MPR, dedicated lanes reduce intersection bottlenecks. Mixed non-dedicated traffic wins at 70% widespread saturation. The AV fleet acts as macroscopic pacemakers, smoothing traffic shockwaves and reducing intra-lane oversaturation to maximize global network efficiency without spatial segregation. Full article

The aim of this article is to demonstrate the significance of Iceland’s Ring Road as a key route providing access to geotourism attractions and to discuss its role in shaping visitor traffic in the context of tourist feedback and the principles of sustainable tourism development. The study is based on an analysis of 223 online reviews concerning the Ring Road, supplemented by the author’s own field observations from travelling around Iceland. Opinions relating to natural and anthropogenic assets, tourist infrastructure, transport accessibility, travel safety, visitor concentration and the interpretation of geological heritage were analysed. The results indicate that the Ring Road is highly rated by tourists, primarily because of the exceptional natural assets located along and near the route. At the same time, the analysis revealed challenges related to traffic concentration at the most recognisable attractions, the uneven quality of tourist infrastructure, high costs, travel safety and the limited use of the educational potential of geosites. The findings show that online reviews, when combined with field observations, can serve as a useful source of knowledge about the practical conditions for the sustainable use of geotourism attractions in popular natural destinations. Full article

This paper presents the design, implementation, and assessment approach of the Blended Intensive Program (BIP) “Towards Enhanced Pedestrian Safety”, developed under the Erasmus+ framework at the University of Beira Interior in collaboration with four partner universities from Slovenia, Poland, and Croatia. The study applies the EPIC framework (Embedded, Pluralistic, Internationalized, and Connected) to the design and evaluation of a short-term, hybrid, and international learning format in Civil Engineering, addressing a gap in the literature regarding the limited availability of structured and empirically grounded approaches for the design and assessment of Blended Intensive Programs in engineering education. The research presents an applied case study design in higher education supported by a mixed-methods approach, combining quantitative and qualitative data from a cohort of 25 participants across different academic levels. Data collection includes performance-based assessment (tests and project evaluation), structured surveys, and qualitative feedback. The program combined digital technologies, including GIS, machine learning, and video image analysis, with problem-based and collaborative learning in a hybrid format comprising 7 h online and 35 h in person. The BIP fostered both technical competences, such as road safety diagnosis and data-driven modelling, and transversal skills, including teamwork, communication, leadership, and intercultural competence. Results from student assessment and satisfaction surveys indicate high levels of engagement, motivation, and learning achievement. The findings provide empirical support for the applicability of the EPIC framework in the design and implementation of BIPs, demonstrating its adaptability to short-term, hybrid, and international learning contexts. The study highlights the pedagogical value of integrating international collaboration, digital tools, and active learning strategies, while aligning with SDG 3 (Good Health and Well-being) and SDG 11 (Sustainable Cities and Communities). Full article

Advanced Driver Assistance Systems (ADAS) have significant potential to improve road safety. However, drivers’ perceptions and acceptance of these systems in Thailand have not been explored. This study investigated Thai drivers’ perceptions towards ADAS and examined factors associated with trust and intention to use. A cross-sectional survey was conducted with 849 licenced drivers. The questionnaire measured perceived usefulness, perceived ease of use, trust, barriers and concerns, expectations and preferences, and intention to use ADAS. Data were analyzed using Mann–Whitney U tests, Spearman’s rank correlations, and multiple linear regression. Results indicated that Thai drivers reported positive perceptions of ADAS regarding perceived usefulness, expectations, preferences, and intention to use. Trust was most strongly associated with constructs such as perceived usefulness, perceived ease of use, and intention to use. Multiple regression identified perceived usefulness, trust, and expectations and preferences as significant positive predictors of intention to use ADAS, whereas barriers and concerns were negatively associated with intention to use. Perceived ease of use was not a significant predictor. These findings highlight the importance of perceived usefulness, trust, and user expectations in shaping intention to use ADAS and support the need for new policies regarding driver education and awareness initiatives in Thailand. Full article

Driving in snow presents major safety challenges due to reduced visibility and slippery road conditions. Simulation-based tools may help improve hazard awareness; however, their effectiveness depends on how realistically they represent real-world driving experiences. This study examines the perceived realism and learning outcomes of a browser-based snow-driving simulation. A total of 87 licensed drivers with prior snow-driving experience interacted with a first-person browser-based simulation and subsequently completed a structured questionnaire. Composite indices were developed to measure Real-World Risk Perception (RWRP), Simulation Realism (SRI), Learning and Reflection (LEARN), and Awareness and Behavioral Reconsideration (AWARE). Quantitative analyses included reliability testing, descriptive statistics, correlation analysis, and multiple regression, complemented by qualitative thematic analysis. Results showed that perceived simulation realism was significantly associated with self-reported learning and awareness outcomes, whereas prior real-world risk perception was only weakly associated with post-simulation responses. Behavioral consistency between reported real-world and simulated driving behaviors was limited, suggesting that increased cognitive awareness does not necessarily correspond to behavioral equivalence. Qualitative findings identified limitations in vehicle dynamics, environmental complexity, traffic interactions, and emotional realism. Overall, the findings suggest that perceived realism plays a central role in shaping learning and awareness outcomes in browser-based driving simulations. The study highlights the educational potential of accessible web-based simulation environments while also emphasizing limitations in behavioral realism and transfer. Full article

Traditional cognitive practices in the Engineering Management Major (EMM) are often constrained by safety risks, high costs, and geographical limitations. This study proposes a novel Virtual Cognitive Practice (VCP) mode that integrates ubiquitous learning (U-learning) with web-based panoramic maps to overcome these challenges. We developed a VCP system leveraging panoramic data of roads, bridges, and tunnels from commercial web mapping platforms to provide high-fidelity, interactive observation environments. To evaluate its effectiveness, 147 undergraduate students participated in a virtual practice course and subsequently completed a structured questionnaire. The results demonstrate that the accuracy on objective knowledge tests exceeded 80%, alongside a high mean score of 4.27/5 for visualization satisfaction. Statistical analysis using Chi-square tests indicates that students with prior on-site experience are significantly more confident in the VCP mode’s potential as a pedagogical alternative. This research bridges the technical gap in EMM practical education by providing a flexible, ubiquitous learning ecosystem. Full article

The construction industry remains among the most hazardous, with workers in horizontal transportation infrastructure facing additional risks from dynamic work zones, live traffic exposure, and variable environmental conditions. Immersive technologies such as Virtual Reality (VR) and Augmented Reality (AR) offer new approaches to accident analysis and prevention, yet their applications toward improving occupational safety in transportation construction have not been comprehensively reviewed. This paper presents a systematic review of 54 studies published between 2016 and 2025 collected from two online databases (Transportation Research International Documentation and Web of Science). This review synthesizes how immersive technologies contribute to occupational risk assessment, safety training, and real-time hazard monitoring in the construction of roads, bridges, tunnels, and work zones. Each study is classified across two dimensions: the immersive medium (VR, AR, etc.) and the operational context within the construction lifecycle (onsite tools, offsite monitoring and planning, simulation-based analysis, and workforce education). This dual classification is the first to systematically map immersive technology applications for occupational safety, specifically within horizontal transportation infrastructure. The findings of this review demonstrate the unique use cases of each immersive medium, revealing that VR is primarily used for controlled experimentation and full-immersion remote analysis, whereas AR and handheld devices are preferred for field-deployed applications. Despite these promising capabilities, widespread adoption remains limited by hardware constraints, challenging field conditions, and organizational resistance. This suggests that future work should focus on safety systems tested in real-world settings and rigorously evaluated by domain experts to enable their integration into standard workplace risk management practices. Full article

Road traffic safety is central to socially resilient and sustainable cities, yet many middle-income countries lack harmonized subnational data on exposure, infrastructure, and enforcement. This study examines whether routinely available demographic composition can serve as a practical structural baseline for provincial traffic accident rates and as a diagnostic layer for richer safety models. Using official province–year data from Türkiye (2008–2019 and 2022–2024; n = 1215), demographic shares by sex, education, and age were treated as compositional inputs and transformed using isometric log-ratio (ILR) methods, with GDP per person included as a scalar covariate. A Tabular Residual Network (ResNet) was trained on the historical panel and evaluated on a post-period calibration/evaluation window (2022–2024), which was used for checkpoint selection and seed screening rather than as an independent held-out test set. Among the evaluated specifications, the ResNet seed-ensemble achieved the strongest performance on the 2022–2024 calibration/evaluation period (R² = 0.5717), outperforming the best single-seed model (R² = 0.5539), a province-specific last-value-carried-forward temporal heuristic based on 2019 values (R² = 0.4779), tree-based tabular benchmarks (Random Forest: R² = 0.1328; XGBoost: R² = 0.0706), and pooled statistical reference models (linear: R² = 0.1375; negative binomial: R² = 0.0686; Poisson: R² = −0.0634). Year-wise diagnostics indicated gradual temporal drift, suggesting that periodic recalibration or the inclusion of additional policy-relevant covariates is needed to preserve calibration. Overall, ILR-based compositional geodemography provides a scalable and interpretable baseline for traffic safety monitoring and prioritization in data-constrained settings. Full article

In this paper, we present a curated dataset derived from continuous multi-object tracking observations over a two-year period from a signalized urban intersection in Salzburg, Austria. The dataset includes time-resolved trajectories of multimodal road users, post-processed object attributes, movement relations, and Post-Encroachment Time values computed for a fixed set of eight predefined multimodal traffic conflict scenarios. Moreover, traffic signal data are included and can be used as contextual information. A temporal six-month subset is published via Zenodo including usage examples written in python. The full dataset can be provided on request. Potential applications include traffic safety analysis, behavioral modeling, method development for interaction detection, and educational use in data-driven traffic research. Full article

This study identifies and explains road safety behavior patterns in Colombia using explainable artificial intelligence (XAI). Based on 9232 records and 38 variables from the Territorial Survey of Road Safety Behavior, the CRISP-DM methodology was applied, including data cleaning, normalization, encoding, and feature selection. XGBoost, Random Forest, Bagging, and AdaBoost models were evaluated, incorporating three domain-specific indices: Distraction Index (DI), Risky Road Interaction Index (RRI), and Normative Compliance Index (NCI). AdaBoost achieved the best overall balance (Precision = 0.78; Recall = 0.75; F1-score = 0.77), simultaneously reducing false positives and false negatives. SHAP analysis revealed that environmental and infrastructure factors (lighting, traffic signals, intersections, congestion, perceived crime) explain more variance than self-reported behaviors (mobile phone use, alcohol consumption, speeding). The complementary indices indicated above-average distraction levels, high exposure to risky interactions, and low compliance in specific segments. These findings enable the prioritization of targeted interventions (improvements in lighting and crossings, focused enforcement, and educational campaigns) and support operation with thresholds adjusted to error costs, providing traceable decision support for public road safety policies. Overall, the proposed approach integrates prediction and explainability to enable actionable decisions and continuous monitoring aimed at reducing traffic accidents. Full article

Accurate perception and interpretation of the road environment are essential for safe driving. Vertical traffic signs play a key role in communicating warnings, regulations, and guidance to road users, thereby supporting safe and efficient traffic flow. However, their effectiveness depends not only on proper design and placement but also on how accurately and promptly they are perceived by drivers, which may be influenced by factors such as attention, cognitive workload, physical and mental condition, and fatigue. This study evaluates the contribution of selected vertical traffic signs to driving safety along a designated roadway section in Şanlıurfa, Türkiye. Face-to-face surveys were conducted with 480 active road users. Drivers’ knowledge, compliance behavior, safe route preferences, perceived visibility, and the effects of missing or inadequate signage were analyzed. The results indicate that driving exposure, education level, and experience significantly influence knowledge and perception of traffic signs, while compliance shows limited variation. These findings suggest that knowledge alone does not necessarily translate into behavioral compliance and underscore the importance of considering both driver-related factors and infrastructure characteristics in traffic safety strategies. The study provides practical insights for improving the visibility, placement, and overall effectiveness of vertical traffic signs in rapidly developing urban environments. Full article

Pedestrian safety remains a major concern in rapidly urbanizing cities of developing countries, where road traffic crashes constantly involve vulnerable road users. In Lahore, Pakistan, pedestrian facilities such as signalized crossings often underperform due to limited awareness, inadequate design, poor maintenance, and weak enforcement. This study evaluates pedestrian awareness, perception, and compliance with pedestrian signals along the Mall Road Corridor, a busy urban arterial serving diverse socio-economic groups. Data were collected through a self-administered questionnaire survey, yielding 600 valid responses. Descriptive statistics, Pearson correlation analysis, ordinal logistic regression, and factor analysis were employed to examine the influence of socio-demographic characteristics and perceived infrastructural attributes on pedestrian behavior. Results indicate that gender, age, education, employment status, and income significantly affect compliance with pedestrian signals. Factor analysis identified seven latent constructs related to compliance behavior, safety perception, signal placement, traffic conditions, perceived importance, and user satisfaction. Only 43% of respondents demonstrated full awareness of pedestrian signals, and 54% reported regular or occasional use. The findings highlight that in this perception-based study, both infrastructural quality and perceived safety strongly shape pedestrian compliance, underscoring the need for targeted design improvements and enforcement measures to enhance pedestrian safety in developing urban contexts. Full article

Road transport across Asia is undergoing rapid motorisation and exemplifies growing road safety challenges, with rising accident rates closely linked to driver behaviour. Recent reports indicate that Vietnamese drivers often perceive risk as manageable and enforcement as inconsistent, contributing to habitual violations such as speeding, signal ignoring, and risky manoeuvres, particularly when traffic is light. Evidence shows that riders, especially young adults, feel confident controlling their vehicles and frequently disregard safety warnings. This study investigates traffic safety awareness among motorcyclists and car drivers in Hanoi, based on a questionnaire survey of 393 respondents. Principal Component Analysis (PCA) was used to group 11 attitudinal statements into key components influencing road safety perceptions, identifying five: non-compliance with traffic regulations (Component 1), aggressive driving behaviour (Component 2), traffic signal issues (Component 3), road quality and infrastructure (Component 4), and preventive measures (Component 5). Multiple Correspondence Analysis (MCA) and two-step cluster analysis (TCA) were then applied to determine user clusters by socio-demographic characteristics, producing three groups: young adults in employment riding motorcycles (Cluster 1), young adults in education riding motorcycles (Cluster 2), and mature adults in employment driving cars (Cluster 3). Finally, Multinomial Logistic Regression (MLR) was applied to assess variations in road safety perceptions across the different groups (clusters). Mature adults driving cars (Cluster 3) identified the first four components as significant, with Components 1 and 2 showing negative associations and Components 3 and 4 positive associations. Full article

Pedestrian safety remains a pressing challenge in low- and middle-income countries (LMICs), where global predictive models often misrepresent local realities. This study tests the hypothesis that global predictive models, such as the International Road Assessment Programme (iRAP), overestimate countermeasure effectiveness in LMICs because key contextual factors are omitted. The two-phase research combined a PRISMA-based systematic literature review (SLR) with a quantitative iRAP performance gap analysis of the countermeasures implemented in the candidate studies of the SLR. The review systematically evaluated the effectiveness of pedestrian safety countermeasures, with an emphasis on their application in LMIC contexts. Following PRISMA 2020 guidelines, 14 longitudinal before–after studies were selected from 1911 records and screened with EPPI-Reviewer 4 software. The analysis identified 33 contextual factors shaping countermeasure performance across both high- and low-income settings; of these, 23 were specific to LMICs, and 13 are not accounted for in the iRAP model. The findings show that iRAP systematically overestimates countermeasure effectiveness in LMICs due to weak enforcement, poor maintenance, and informal road use. Transverse rumble strips were the only intervention consistently effective across diverse LMIC settings. A novel performance gap analysis of five LMIC case studies revealed an average discrepancy of 30.9% (SD = 29.7%) between predicted and observed outcomes. A risk of bias assessment showed that most LMIC studies were of moderate to serious risk, reflecting systemic data limitations and a frequent reliance on proxy outcomes. These findings highlight the urgent need for recalibrated, context-sensitive frameworks that incorporate enforcement, maintenance, and socio-economic variables. Policy implications include prioritising affordable and scalable countermeasures, pairing infrastructure with enforcement and education, and strengthening crash data systems to support more realistic, evidence-based road safety planning. Full article

Education and post-licencing training programmes for novice drivers are widely implemented to improve road safety, yet their effectiveness remains debated. This study evaluates short-term attitudinal changes relating to participation in a mandatory post-licencing training programme for novice drivers in Slovenia. A within-subject pre–post survey methodology was used to evaluate self-reported driving attitudes across six safety-related domains among 225 novice drivers at a Slovenian driving training centre in 2024. Paired t-tests revealed minor yet statistically significant improvement following the programme in perceived support for the additional driver training, lowered overconfidence, heightened care in speeding and intersection behaviour, and enhanced attitudes towards vehicle operation and utilization of safety equipment. Attitudes regarding attention and adherence to traffic regulations showed negligible shifts, indicating a strong baseline attitude towards safe driving. The findings indicate a modest but fairly consistent short-term change in attitudes after programme participation. Due to the lack of a control group and dependence on self-reported data, the findings should be seen as evaluative rather than causative, necessitating more longitudinal and behavioural research to evaluate long-term and behavioural effects. Full article