Traveler Segmentation using Smart Card Data with Deep Learning on Noisy Labels
Automated Fare Collection (AFC) system is widely used in today's urban transportation facilities across the globe. While Smart Card data collected by AFC systems contain rich information for understanding the city travelers' transportation behaviors, the anonymous nature of such information hinders further applications with the data which will require clustering the anonymous smart card holders into meaningful customer groups, such as for transportation policy planning and implementation of customized services or targeted incentives. As manual labelling of the travellers is impractical, heuristic rule based or frequentist methods can be used but they will lead to noisy labels. In this paper, we propose to tackle the problem using a three stage procedure involving two sample testing, heuristic labelling, and deep learning with label denoising to mine the temporal patterns of travelers to classify them into several meaningful customer segments of interest. We experimentally validated the robustness of our label denoising approach for deep learning, and showed that the segment sizes predicted using our method on a nine month Smart Card dataset are in agreement with the reported population gures.