How smart is your smartcard?: measuring travel behaviours, perceptions, and incentives
Proceedings of the 13th international conference on Ubiquitous computing
Avoiding the crowds: understanding Tube station congestion patterns from trip data
Proceedings of the ACM SIGKDD International Workshop on Urban Computing
The hidden image of the city: sensing community well-being from urban mobility
Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
| Hi-index | 0.00 |
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.