Safety Assessment of Adjacent Roads Sections via Maximum Entropy Driver's Perception Field

Kulbashna, Nadiia; Lynnyk, Iryna; Galkin, Andrii; Prasolenko, Oleksii; Dulfan, Sergii · 2020 · OpenAlex-citations

DOI: 10.26552/com.c.2020.4.182-190

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Summary

This study addresses the challenge of ensuring traffic safety on adjacent road sections by accounting for the human factor, specifically the driver’s adaptation to changing road environments. Existing methods often rely on isolated geometric or speed criteria, failing to provide a systematic assessment of the complex interaction between the driver, vehicle, and road environment. The authors propose a new method for assessing the consistency of adjacent road sections based on the maximum entropy of the driver’s perception field. This criterion allows for a comprehensive evaluation of multiple road environment factors influencing the driver, aiming to prevent undesirable transient phenomena and fatigue caused by drastic changes in traffic conditions. The research employed a combination of theoretical modeling and field experiments. Theoretical foundations were established using systems theory and information theory to define the driver’s behavior program in terms of maximum entropy. Experimental studies were conducted on three suburban roads totaling 45 kilometers, involving 26 drivers with 3–7 years of experience. Data collected included actual vehicle speeds, travel times, and the number of road environment objects within the driver’s perception field, which was calculated using an empirical formula based on speed. Traffic volume and composition were estimated using the moving observer method. Additionally, historical accident statistics over five years were used to calculate the accident rate coefficient for each road section. The results demonstrated a significant correlation between the maximum entropy of the driver’s perception field and traffic safety. Analysis revealed a quadratic relationship between the accident rate and the maximum entropy value, with a high correlation index ($r' = 0.851$). Furthermore, the study established a dependence between the accident rate and the ratio of maximum entropy values of adjacent sections ($H_m(n)/H_m(n+1)$). The findings indicate that adjacent sections are considered "aligned" or safe when the maximum entropy values vary between 46% and 84%. Variations below 46% are also consistent, while changes exceeding 84% correlate with dangerous or very dangerous conditions, leading to increased accident rates. Statistical validation using the Student’s t-test confirmed the reliability of these correlations. The significance of this work lies in providing a practical, human-centered tool for road infrastructure design and operation. By establishing specific limits for maximum entropy changes, the method allows engineers to assess the safety of transitions between adjacent road sections. This approach enables the identification of unsafe configurations where drivers face excessive informational load or adaptation difficulties. Consequently, the proposed criteria can guide the reconstruction or design of road environments to ensure consistent and safe driving conditions, reducing the risk of accidents caused by abrupt changes in the road environment.

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• traffic density

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• Empirical Findings: crash risk outcomes