Mining temporal patterns of transport behaviour for predicting future transport usage

There is huge potential in increasing the value of public transportation by creating novel travel information systems which are centred on the individual transport user. Especially, in dense urban cities where it is hard to oversee complex transport networks that are subject to frequent changes, maintenance and construction works, travellers want to be proactively notified about disruptions and traffic incidents relevant to their future behaviour. In this paper, we show how to mine characteristic patterns of the transport routines of urban bus riders for the design of novel travel information system that have the ability to understand forthcoming travel needs of individual users. We leverage on travel histories collected from automated fare collection system (AFC) to extract features of personal transport usage and study their predictive power to forecast whether people access public transport services on a future day or not