Brain activity analysis reveals how environmental factors influence female cyclists' perceived security during virtual reality simulations.
This study investigates the perceptions of safety among female cyclists, particularly within urban settings like Tehran, highlighting significant findings through the use of innovative technologies such as virtual reality (VR) and electroencephalography (EEG). The research employs VR cycling simulations to provide participants with varied environmental experiences, allowing for the gathering of real-time brainwave data alongside subjective security perceptions.
Active transportation, such as cycling, is increasingly recognized for its role in improving public health and reducing environmental impacts. Despite the benefits, female cycling rates are often markedly lower than those of males, particularly in low- and middle-income countries. The willingness of women to use bicycles frequently hinges on their perception of security; when feeling unsafe, women are disproportionately more likely to modify their cycling habits or avoid bicycling altogether.
This study addresses this pressing issue by examining how different environmental and social factors contribute to feelings of security among female cyclists. Researchers engaged 52 female participants to navigate through diverse cycling scenarios within Tehran’s VR simulator. Each scenario was crafted to simulate aspects such as busy urban environments with obstacles, poorly lit areas, and the presence of surveillance measures including police officers.
Utilizing data from EEG sensors, researchers captured brainwave signals which reflected real-time emotional responses during the simulated rides. The study aimed to identify subtle indicators of stress or alertness linked to participants' perceived security. The analysis employed the Gaussian mixture approach to cluster these brainwave patterns, leading to insights on how specific environmental characteristics influenced cyclists’ feelings of safety.
The findings indicated significant connections between perceived security and various factors present during the simulations. For example, the presence of urban obstacles like kiosks or the degradation of cycling paths led to heightened feelings of insecurity among participants. Informal surveillance, characterized by the presence of other pedestrians or police, was also found to alleviate anxiety, enhancing perceptions of safety.
The predictive modeling conducted revealed the strengths of different machine-learning techniques applied to EEG data classification. Notably, the support vector machine method demonstrated the best performance, yielding an F1 score of 0.74 for identifying influential factors associated with female cyclists' security perceptions. This indicated the viability of applying advanced data analysis methods to transport-related psychological assessment.
"Factors such as the presence of obstacles like kiosks, cycling routes passing through tunnels and underpasses, and levels of incivility drastically influence female cyclists' security perception," researchers noted, emphasizing the urgent need to address these variables through infrastructure improvements.
By highlighting the gaps currently existing within cycling infrastructure and proposing enhancements supported by scientific data, the research has pivotal practical applications for urban planners. Enhancements could include improved lighting, easing of physical barriers, or increased police patrolling geared toward enhancing feelings of safety among female cyclists.
Concluding, this rigorous study not only backs previous assertions concerning the importance of perception of security but also offers fresh, actionable insights from the nuanced lens offered by EEG data analysis. Future research directions could extend these findings across various urban settings and potentially inform broader strategies aimed at increasing cycling participation among women, transforming cycling infrastructure to be more inclusive and safer overall.