0. 2026

Grassed Tramway Tracks and Sustainable Urban Mobility: Integrating Nature-based Solutions in City Transport Infrastructure

2026-07-03T04:54:21Z

Grassed Tramway Tracks and Sustainable Urban Mobility: Integrating Nature-based Solutions in City Transport Infrastructure Radácsi, László; Czédli, Herta; Major, Zoltán; Szigeti, Cecília Urban mobility is crucial to sustainable city development, bridging environmental preservation, economic advancement, and social equity. This paper investigates the potential of grassed tramway tracks (or green tracks) in contributing to sustainable urban mobility and aligning with the United Nations Sustainable Development Goals (SDGs). The study highlights the transformative potential of green tracks by analyzing ecological impact, carbon sequestration, and a comparative evaluation of ecological footprints. The findings emphasize their role in mitigating climate change, enhancing urban biodiversity, and promoting resilient infrastructure, positioning them as a critical solution for achieving sustainable urban development.

Multi-Modal Evaluation of Adaptive Interface Design and Railway Grade Crossing Infrastructure, in Simulated Driving Environments

2026-07-03T04:54:04Z

Multi-Modal Evaluation of Adaptive Interface Design and Railway Grade Crossing Infrastructure, in Simulated Driving Environments Nagy, Viktor; Kovács, Gábor; Babić, Darko; Desnica, Eleonora This study examines the usability and cognitive workload associated with two interface concepts ‒ Context-Driven Adaptive Dashboard System and a Bring-Your-Own- Device (BYOD) graphical interface ‒ tested within a high-fidelity driving simulator across urban and rural routes. Nineteen participants completed realistic driving scenarios, during which physiological, behavioral, and subjective data were collected. Usability was assessed using the System Usability Scale, while mental workload was measured with the NASA Task Load Index. The results show that the context-aware interface achieved a 20.9% higher usability score compared to a Bring-Your-Own-Device interface (71.7 vs. 59.3, p = 0.0105). However, workload levels did not differ significantly across the interfaces. The experiment also analyzed driver behavior at both secured and unsecured railway grade crossings using eye-tracking technology. Eye-tracking analysis revealed unsecured crossings elicited 30.2% more fixations, a 13.8% increase in fixation frequency, and a 26.4% decrease in average fixation duration (p < 0.01), reflecting elevated visual search activity and uncertainty. While statistical comparisons of driver risk behavior at crossings yielded limited significance, observed trends consistently pointed to safer actions at secured crossings. These findings underscore the importance of adaptive interface design and intelligent infrastructure in reducing driver distraction and enhancing safety in both everyday and critical driving situations.

Autonomous Train Operation Obstacle Detection Based on Night Vision System

2026-07-03T04:54:03Z

Autonomous Train Operation Obstacle Detection Based on Night Vision System Ćirić, Ivan; Pavlović, Milan; Ivačko, Nikola; Tomov, Pancho; Nikolić, Vlastimir This paper presents the development of an obstacle detection system for autonomous train operation (ATO) using a night vision system. The focus is on the development of the ATO for freight transport that operates in low light and night conditions. An experimental setup featuring an Intensified Charge-Coupled Device (ICCD) camera was employed to acquire images under low-light conditions. A novel computer vision algorithm was developed to ensure robust and reliable obstacle detection. The methodology begins with the detection of rail tracks, which are subsequently used to define a Region of Interest (ROI). Within the ROI, the rail tracks are analyzed for interruptions indicative of obstacles. Obstacle detection is achieved through image segmentation techniques, while the distance between the setup and the detected obstacles is estimated using a homography-based approach. The proposed algorithm was evaluated on a comprehensive dataset comprising images from three representative scenarios. Experimental results demonstrate the system's effectiveness in reliably detecting obstacles under nighttime conditions and accurately estimating distances.

Evaluation of Transport Chain Performance using the IMF SWARA - Fuzzy ROV Model

2026-07-03T04:53:40Z

Evaluation of Transport Chain Performance using the IMF SWARA - Fuzzy ROV Model Song, Meijing; Blagojević, Aleksandar; Kasalica, Sandra; Stević, Željko; Marinković, Dragan; Prentkovskis, Olegas In modern logistics, transport chains are a strategic success factor for companies, influenced by the demand for fast delivery of goods, digitalization and sustainability. Optimizing these processes requires complex decision making, considering costs, speed, reliability and environmental impact. This paper defines an integrated IMF SWARA and Fuzzy ROV model based on the fuzzy Bonferroni aggregation operator for the analysis of transport chains under conditions of uncertainty. Testing on a case study of the Hygiene Pro Team company shows that the combination of road, sea and rail transport (A3 and A4) yields the best results, while the river alternative (A7) has the lowest ranking. The paper confirms the effectiveness of the fuzzy MCDM approach in balancing economic, operational and sustainability aspects, highlighting the need for further research by incorporating AI and expanded criteria.