TY - CONF T1 - Mining Public Transport Usage for Personalised Intelligent Transport Systems T2 - 2010 IEEE 10th International Conference on Data Mining (ICDM) Y1 - 2010 A1 - Lathia,N. A1 - Jon Froehlich A1 - Capra,L. KW - automated ticketing systems KW - data mining KW - Intelligent Transport Systems KW - London underground KW - mobility patterns KW - personalised intelligent transport systems KW - personalised trip times KW - Personalization KW - public administration KW - public information systems KW - public transport systems KW - rapid transit systems KW - route planning KW - service disruptions KW - service updates KW - traffic information systems KW - travel history KW - traveller information AB - 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'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. JA - 2010 IEEE 10th International Conference on Data Mining (ICDM) PB - IEEE SN - 978-1-4244-9131-5 M3 - 10.1109/ICDM.2010.46 ER -