ENGINEERING AND PSYCHOLOGICAL ANALYSIS OF DRIVER COGNITIVE PROCESSES AT RAILWAY LEVEL CROSSINGS

Kurhan, Mykola; Ivanov, Rodion · 2026 · Crossref

DOI: 10.18664/1994-7852.216.2026.362512

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Summary

This paper presents an engineering-psychological analysis of driver cognitive load at railway level crossings, addressing the persistent safety issue where over 85% of accidents are attributed to human error, such as misperception and decision-making under time constraints. The study aims to establish a quantitative relationship between the spatial arrangement of traffic control devices (warning signs, light signals, barriers) and the dynamics of driver cognitive load, specifically identifying approach segments where the probability of decision errors is highest. The methodology combines systems analysis, cognitive ergonomics, and mathematical modeling. The authors developed a spatial-cognitive model that treats the level crossing as an information environment, mapping driver perception stages against distance to the hazardous zone. The study utilized a comparative case study of five level crossings on the Kyiv–Zhmerinka electrified line in Ukraine, varying in track topology from single-track to multi-track configurations (including a 2+2 split-block layout). Cognitive load was assessed using a 1–10 scale derived from verbal ratings of signal density and decision complexity, with numerical computation used to model load dynamics across specific distance intervals (e.g., 286–182 m, 182–89 m, 50–20 m). Key findings indicate that cognitive load follows a non-linear trajectory: it remains low to moderate during initial approach, increases as drivers refine situation awareness, and peaks in the immediate pre-crossing zone (50–20 m) where stop/go decisions are made under time pressure. This peak is associated with the "attention tunneling" effect, a narrowing of focus toward dominant cues that improves responsiveness to primary warnings but reduces monitoring of secondary safety factors like adjacent traffic or pavement condition. Comparative analysis revealed that multi-track crossings significantly increase cognitive complexity; specifically, a four-track configuration split into two blocks (2+2) creates two successive stress peaks, making it cognitively more demanding than a single-block four-track layout. The study identifies the 50–20 m segment as the critical zone for error risk. The significance of this work lies in providing evidence-based recommendations for optimizing signal placement to reduce information overload in critical segments and establishing clear visual hierarchies. By shifting information provision to longer approach distances, the model suggests a way to lower peak cognitive stress and reduce the likelihood of erroneous decisions. The proposed spatial-cognitive model serves as a foundation for the modernization of level crossings and can be integrated into Intelligent Transport Systems (ITS) using computer vision and AI to adaptively manage warning signals in real-time, thereby enhancing road safety in critical infrastructure.

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• rail grade crossings