@conference {14316,
	title = {Mining Public Transport Usage for Personalised Intelligent Transport Systems},
	booktitle = {2010 IEEE 10th International Conference on Data Mining (ICDM)},
	year = {2010},
	month = {2010},
	pages = {887 - 892},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Traveller information, route planning, and service updates have become essential components of public transport systems: they help people navigate built environments by providing access to information regarding delays and service disruptions. However, one aspect that these systems lack is a way of tailoring the information they offer in order to provide personalised trip time estimates and relevant notifications to each traveller. Mining each user{\textquoteright}s travel history, collected by automated ticketing systems, has the potential to address this gap. In this work, we analyse one such dataset of travel history on the London underground. We then propose and evaluate methods to (a) predict personalised trip times for the system users and (b) rank stations based on future mobility patterns, in order to identify the subset of stations that are of greatest interest to the user and thus provide useful travel updates.},
	keywords = {automated ticketing systems, data mining, Intelligent Transport Systems, London underground, mobility patterns, personalised intelligent transport systems, personalised trip times, Personalization, public administration, public information systems, public transport systems, rapid transit systems, route planning, service disruptions, service updates, traffic information systems, travel history, traveller information},
	isbn = {978-1-4244-9131-5},
	doi = {10.1109/ICDM.2010.46},
	author = {Lathia,N. and Jon Froehlich and Capra,L.}
}
