When feeling safe becomes risky: A VR-EEG-computer vision framework for analyzing cyclist safety in dynamic traffic environment

The mismatch between cyclists' perceived safety and actual crash risk in mixed-traffic environments is a critical yet underexplored issue in road safety research. While prior studies have focused on static environmental factors, they often overlook the real-time influence of dynamic visual stimuli on risk perception. To address this gap, this study developed a multisource-integrated virtual reality (VR) experimental platform that synchronously captured millisecond-level electroencephalography (EEG) signals from 72 participants, built environment (BE) features, and time-to-collision (TTC) data from VISSIM microsimulation. A Long Short-Term Memory (LSTM) model was used to examine how mismatches emerge between perceived safety and crash risk. Results reveal a 'perceptual relief period' after being overtaken, where cyclists exhibit higher perceived safety despite persistent threats from following vehicles, creating a potentially hazardous temporal window. This mismatch effect is further amplified in environments characterized by high spatial enclosure, complex visual textures, dense vegetation, and low visible vehicle density. These findings suggest that BE features intended to enhance aesthetic appeal or reduce stress may inadvertently impair cyclists' ability to perceive risk in high-conflict areas. This study offers empirical support for an integrated human-vehicle-environment safety framework and calls for interdisciplinary collaboration between neuroscience and transport engineering in the design of safer mobility systems.