Mining Smart Card Data for Travellers’ Mini Activities
In the context of public transport modelling and simulation, we address the problem of mismatch between simulated transit trips and observed ones. We point to the weakness of the current travel demand modelling process; the trips it generates are over-optimistic and do not reflect the real passenger
choices. We introduce the notion of mini activities the travellers do during the trips; they can explain the deviation of simulated trips from the observed trips. We propose to mine the smart card data and identify characteristics help detect the mini activities.
We develop a technique to integrate them in the generated trips and learn such an integration from two available sources, the trip history and trip planner recommendations. For an input travel demand, we build a Markov chain over the trip collection and apply the Monte Carlo Markov Chain algorithm to integrate mini activities in such a way that the selected characteristics converge to the desired distributions. We test our method on the trip dataset collected in Nancy, France. Results of a series of evaluations demonstrate a very important mismatch reduction.