Cycling is a sustainable mode of transportation offering environmental, social, and economic benefits. However, cycling adoption in Sri Lanka has declined due to urbanization, safety concerns, and socioeconomic factors. This study profiles Sri Lankan cyclists to identify key sociodemographic, behavioral, attitudinal and environmental factors influencing cycling. Data were collected from cyclists through a survey. Two-step cluster analysis was performed to classify cyclists based on cycling patterns, and one-way ANOVA was conducted to compare attitudinal and environmental perception statements across the identified cyclist groups. The results reveal four distinct clusters leading to four cyclists’ profiles: leisure-oriented, student, multipurpose, and utility-oriented cyclists. The findings reveal the importance of attitudinal influences and perceived cycling environment in cycling decisions, reinforcing the need for targeted policies and interventions. By providing a structured approach to cyclist profiling, this research contributes to promoting cycling as a viable transportation mode and supports sustainability and urban mobility goals.
Geographic Information System (GIS)-based hotspot analysis is widely used in road safety studies, but many network-level approaches rely on administrative units, grids, or arbitrary equal-length segments that may miss infrastructure-specific crash concentrations. Addressing this gap, this study applies a geometry-based segmentation framework for spatiotemporal crash hotspot analysis in Kathmandu Valley, Nepal, using 23,300 police-reported crashes from 2019 to 2021 and 12,336 road sections. Global Moran's I, Getis-Ord Gi*, and KDE were applied across severity, collision type, and temporal categories. Moran's I confirmed spatial clustering, Gi* identified hotspot segments at the 99% confidence level, and KDE showed over 85% spatial agreement with Gi* hotspots. Hotspots were concentrated along Ring Road, Araniko Highway, Tribhuvan Highway, and the Bishnumati corridor. Across categories, 49 segments totaling 9.36 km remained persistent hotspots. Reported cause profiles indicated dominant driver carelessness, with high speed and alcohol more prominent in severe, night time, and weekend crashes.
The International Maritime Organization (IMO) aims to achieve net-zero GHG emissions from international shipping by or around 2050. This study developed a system dynamics model to evaluate combined operational, technological, and economic CO2-reduction measures in the maritime industry. The model focuses on tank-to-wake CO2 emissions from fuel combustion; therefore, the results should be interpreted within this defined boundary rather than as a comprehensive well-to-wake GHG assessment. Simulation results show that no single measure is sufficient to meet the modeled CO2-reduction target. The most effective pathway combines slow steaming, large-scale deployment of vessels assumed to have zero tank-to-wake CO2 emissions using alternative fuels, continuous technological efficiency improvements, and economic incentives such as carbon levies and preferential interest rates. The proposed framework enables policymakers, shipowners, shipbuilders, and other maritime stakeholders to compare integrated CO2-reduction pathways and assess trade-offs among emission reduction, fleet transition, and economic sustainability.
Digital ticketing is increasingly central to smart urban mobility, yet its operational benefits depend on passenger adoption and institutional readiness. This study examines the impact of digital ticketing on passenger-flow efficiency and user acceptance at BTS Khu Khot Station, a major suburban terminal in Bangkok. A sequential mixed-methods design was adopted. First, Arena-based discrete-event simulation assessed three digital adoption scenarios involving Rabbit cards and ticket vending machines. Second, passenger survey data (n = 402) and expert responses (n = 12) were analysed using constructs informed by TAM and UTAUT2. Simulation results show that increasing non-manual ticketing adoption from 45% to 80% reduced average system time from 4.55 to 2.86 min and queue time from 2.12 to 1.05 min, while throughput increased from 2.51 to 3.87 entities per minute. The findings highlight digital ticketing as both an operational intervention and a user-centred implementation challenge.
This paper proposes a hybrid approach combining the Gravity Model (GM) with machine learning (ML) techniques to improve predictions of Malaysia's international freight exports and imports. The study evaluates GM and three ML models; Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Machine (SVM), with RF emerging as the most robust performer based on RMSE, MSE, and R-squared metrics. Four hybrid approaches were developed by combining GM and RF predictions with residual modeling. Results demonstrate that hybrid models consistently outperform standalone approaches. Notably, Hybrid Approach 2, which uses GM predictions adjusted by RF-modelled residuals with RF features, delivered superior performance, achieving RMSE values of 0.226 for exports and 0.162 for imports. This confirms the hybrid model's enhanced capability to capture complex trade patterns, offering a valuable forecasting tool for Malaysia's international freight transport planning.
