Predicting passengers in public transportation using smart card data

Abstract

Transit prediction has long been a hot research problem, which is central to the public transport agencies and operators, as evidence to support scheduling and urban planning. There are several previous work aiming at transit prediction, but they are all from the macro perspective. In this paper, we study the prediction of individuals in the context of public transport. Existing research on the prediction of individual behaviour are mostly found in information retrieval and recommender systems, leaving it untouched in the area of public transport. We propose a NLP based back-propagation neural network for the prediction job in this paper. Specifically, we adopt the concept of “bag of words” to build user profile, and use the result of clustering as input of back-propagation neural network to generate predictions. To illustrate the effectiveness of our method, we conduct an extensive set of experiments on a dataset from public transport fare collecting system. Our detailed experimental evaluation demonstrates that our method gets good performance on predicting public transport individuals