I’m just on my way back from beautiful Sydney, where I presented a paper called “Mining Public Transport Usage for Personalised Intelligent Transport Systems” (by me, Jon Froehlich, and Licia Capra) at the IEEE 2010 International Conference on Data Mining. The abstract of the paper reads as follows:
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 invariably 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 each other and thus provide useful travel updates.
This roughly translates to:
Public transport in a large city like London can be chaotic; the information services that were built to support it do not take into consideration who you are when they spit out updates. At the same time, most Londoners now use Oyster cards, that record detailed traces of each person’s movements around the city. The research question we address in the paper is: can Oyster card records be leveraged to build personalised travel info services? Much like the way Amazon says “recommended especially for you” – can we do similar things with travel data? Short answer: yes. Long answer: read the paper. Medium answer: look at slides below.