Sahan Siriwardena | Searcharxiv

arXiv2026

Virtual Reality-Simulated Interaction Between Micro-Mobility Vehicles and Pedestrians: A Biomechanical Analysis of Human Gait and Movement Responses

Pedestrian walking is a fundamental activity of daily living and a key component of first and last-mile urban mobility. The rapid adoption of e-scooters has increased pedestrian-vehicle interactions on shared sidewalks and crossings, raising collision risks. However, most previous studies have relied on trajectory-based observations, providing limited insight into biomechanical gait responses. This study investigated pedestrian gait adaptations during simulated e-scooter interactions using immersive virtual reality (VR) and markerless pose estimation. Twelve healthy male university students (21-23 years) completed four VR walking scenarios: normal walking, e-scooter encounters at 10-25 km/h, crossing encounters, and near-crash encounters. Sagittal-plane videos were analyzed using the OpenPose 25-point model. Step length, gait cycle time, walking velocity, stance and swing phases, and lower-limb joint trajectories were extracted using Kinovea and custom JSON-based analysis tools. Statistical analyses included ANOVA, MANOVA, and non-parametric tests Crossing and near-crash scenarios significantly reduced step length (p<0.001), from 226.5 cm during normal walking to 204.7 cm during near-crash simulations. Although gait velocity and timing were not significantly affected, participants consistently exhibited shorter stance phases, longer swing phases, and restricted knee motion during stressful encounters, indicating reflexive gait adaptations to perceived collision risk. These findings demonstrate that immersive VR combined with markerless pose estimation effectively quantifies pedestrian biomechanical responses to micro-mobility interactions. Gait adaptations identified in this study may serve as sensitive indicators of collision risk and support the development of proactive pedestrian safety measures and intelligent micro-mobility control systems.

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arXiv2026

Development of Multivariate Attention LSTM Model For Dynamic Line Rating Forecasting

As global fossil fuel reserves diminish, there's a growing impetus for nations to transition towards renewable energy sources. Sri Lanka, for instance, aims to generate 70% of its electricity from renewable sources by 2030. Achieving this target requires optimal use of the existing power transmission infrastructure, as expanding the grid is both time-consuming and expensive. Traditionally, Static Line Ratings (SLRs) are used to define line capacity, often resulting in underutilization. Dynamic Line Rating (DLR), which estimates line capacity in real time based on weather conditions, offers a more efficient solution. However, DLR prediction is highly sensitive to environmental variability and forecasting complexity. This study proposes a novel multivariate Long Short-Term Memory (LSTM) model enhanced with an attention mechanism for improved DLR forecasting. Unlike traditional models that treat weather variables independently, the proposed approach captures nonlinear interdependencies among key environmental features such as ambient temperature, cable temperature, wind speed, humidity, and solar irradiance. The attention mechanism dynamically prioritizes the most relevant inputs during forecasting, leading to improved performance. Experimental evaluation on real-world DLR data demonstrates that the proposed model achieves a prediction accuracy of 95.84%, surpassing the conventional LSTM model's 94.62%. This improvement highlights the model's superior ability to deliver accurate and robust DLR forecasts. The findings confirm that incorporating multivariate features with attention enhances forecasting precision, supporting more efficient transmission line utilization and higher renewable energy integration.

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