Deep learning-based algorithm for environmental satisfaction assessment at metro stations
To comprehensively assess the passenger satisfaction of the environment in Beijing metro stations,firstly,the data collection of the environment images in metro stations with privacy-protected processing is carried out,and through questionnaire surveys and random forest algorithms,the key indexes and their weights affecting the satisfaction of the environment in metro stations are determined,and the assessment data of the subjects are utilized to establish the environmental satisfaction assessment dataset which contains the satisfaction scores.Next,a deep convolutional neural network model is constructed by learning the relationship between environmental images and satisfaction scores.To enhance the model generalization,migration learning method is used.The model assessment accuracy reaches 96%on the test set of metro line 14,and the migration learning model achieves 94%accuracy on the metro line 7 which is not pretrained.The experimental results show that the model can accurately reflect passengers'experience of the metro environment in terms of accessibility,safety,comfort and pleasantness,providing a powerful reference for the optimization of metro services.
NETL
NSTL
万方数据
