  Problems of dynamic origin-destination (OD) estimation using smart card data
can be modelled using entropy maximisation and solved for large networks using
solution techniques such as Lagrangian relaxation. In this paper, we give an
overview of the research literature about OD estimation. We show how entropy
maximisation can be used to model this problem in case station-entry data from
smart cards is the only available information, i.e. number of entries is known
but not exits nor the flow between the stations. The large entropy maximisation
program is solved using Lagrangian relaxation. The model is tested on a case
study from the commuter train service in Stockholm with smart card entry-data
from a working day in 2015. The results show that, given the entry-data, the
entropy maximisation-based model and methods allows to find an OD-estimate that
is as accurate as the reported estimates from manual observations. The
estimates are further improved when using relevant assumptions and additional
available data.
