AI-Powered Eye-Tracking Technology Revolutionizes Visual Environment Assessment in Urban Rail Transit Spaces and Establishes Cybersecurity Defense Frameworks

Abstract

Urban rail transit, a vital component of public transportation systems, has been revolutionized through the integration of AI eye-tracking technology with its visual spaces. This study constructs an interaction model linking AI-driven gaze analysis to urban rail environments, systematically evaluating passengers’ visual fixation parameters (e.g., dwell time, saccadic frequency, and focal distribution) to assess the impact of diverse visual designs on sensory perception, emotional states, and comfort levels. The derived metrics are embedded into a holistic visual environment evaluation framework, complemented by a cybersecurity defense mechanism tailored to the AI-transportation interface. Key findings demonstrate that the synergy between AI eye-tracking and transit visual spaces enhances passenger-centric outcomes: Improved Element Recognition; Emotional Resonance; Behavioral Guidance. The proposed framework advances spatial cognition through personalized visual interactions and establishes a cognitive neuroscience foundation for the "human-machine-environment" tripartite collaboration in intelligent transportation systems. Additionally, the cybersecurity architecture safeguards biometric data and AI models against adversarial threats via encrypted federated learning and anomaly detection algorithms.

This study introduces a novel paradigm for intelligent transportation systems by synergistically integrating AI-driven visual optimization techniques with next-generation cybersecurity protocols, thereby establishing a transformative framework that simultaneously enhances operational accessibility, reinforces safety parameters, and elevates passenger experience metrics within smart urban ecosystems.