This study devotes to investigating the possibility of inferring demographics of passengers using smart
card data (SCD) and household survey. We first represent SCD as a two-dimension image to capture
travel patterns. Then, we propose to use a convolutional neural network for automatic feature extraction
and demographic prediction, including age group, gender, income level and car ownership. The
household survey data is used to train the deep learning model. Finally, a case study using on London’s
Oyster Card and survey is presented and results show it is a promising opportunity for demographic
study based on people’s mobility behaviour
