[EN] With the wide application of the smart card technology in public transit
system, traveller’s daily travel behaviours can be possibly obtained. This
study devotes to investigating the pattern of individual mobility patterns and
its relationship with social-demographics. We first extract travel features
from the raw smart card data, including spatial, temporal and travel mode
features, which capture the travel variability of travellers. Then, travel
features are fed to various supervised machine learning models to predict
individual’s demographic attributes, such as age group, gender, income level
and car ownership. Finally, a case study based on London’s Oyster Card
data is presented and results show it is a promisingZhang, Y.; Cheng, T. (2018). Inferring Social-Demographics of Travellers based on Smart Card Data. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 55-62. https://doi.org/10.4995/CARMA2018.2018.8310OCS556