Modeling Risk Exposure: Fuzzy and Fuzzy Intuitionistic Approaches to Pedestrian and Vehicle Interaction

Benkhadda, Oumaima; Mandar, Meriem · 2025 · Crossref

DOI: 10.47839/ijc.24.1.3887

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Summary

This paper addresses the challenge of modeling risk exposure in pedestrian-vehicle interactions, a critical issue in road safety where pedestrians remain highly vulnerable. The authors argue that traditional risk assessment methods often overlook human behavioral factors and rely on precise values that fail to capture the inherent uncertainty of traffic dynamics. To overcome this, the study compares fuzzy logic and intuitionistic fuzzy logic approaches, which handle uncertainty and subjective human cognition more naturally than classical methods. The goal is to develop more accurate risk assessment models that can inform tailored safety measures. The methodology involves developing simulation models for both pedestrian and vehicle dynamics. The pedestrian model utilizes Ant Colony Optimization (ACO), a bio-inspired algorithm that simulates individual choices based on environmental factors like obstacles and crowd density, enriched with fuzzy logic to represent the imprecise nature of pedestrian decision-making. The vehicle model employs the Nagel-Schreckenberg cellular automaton, which simulates traffic flow, acceleration, braking, and random deceleration on a single-lane road. Risk modeling is conducted using two formulations: an "old" formula based on standard fuzzy sets and a "new" formula incorporating intuitionistic fuzzy sets. The intuitionistic approach introduces degrees of membership, non-membership, and uncertainty to represent the indecision and misjudgment of both drivers and pedestrians regarding crossing times and safety distances. The results are derived from simulations that generate comparative graphs analyzing risk exposure under both fuzzy and intuitionistic frameworks. The study establishes a risk assessment matrix that categorizes risk levels (Low, Moderate, High, Very High) based on the correctness of driver and pedestrian assessments. The comparative analysis highlights that the intuitionistic fuzzy approach provides a finer understanding of reality by accounting for the subjective aspects of risk, such as the degree of uncertainty in assessing traffic conditions. The simulation results demonstrate a significant improvement in the accuracy of risk assessments when using intuitionistic fuzzy logic compared to standard fuzzy logic, particularly in capturing the nuances of human indecision and the variability of accident conditions. The significance of this work lies in its contribution to more robust accident risk modeling. By integrating intuitionistic fuzzy logic, the proposed method offers a more comprehensive representation of the complexity and indeterminacy surrounding pedestrian-vehicle conflicts. This enhanced accuracy allows for better prediction of threats and supports the development of adaptive risk management strategies. The findings suggest that these advanced modeling techniques can help urban planners and traffic engineers design safer infrastructures and policies by providing a more realistic assessment of how human behavior and uncertainty influence road safety outcomes.

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• pedestrian behavior perception