Estimation of Passenger Route Choice Pattern Using Smart Card Data for
  Complex Metro Systems
  Nowadays, metro systems play an important role in meeting the urban
transportation demand in large cities. The understanding of passenger route
choice is critical for public transit management. The wide deployment of
Automated Fare Collection(AFC) systems opens up a new opportunity. However,
only each trip's tap-in and tap-out timestamp and stations can be directly
obtained from AFC system records; the train and route chosen by a passenger are
unknown, which are necessary to solve our problem. While existing methods work
well in some specific situations, they don't work for complicated situations.
In this paper, we propose a solution that needs no additional equipment or
human involvement than the AFC systems. We develop a probabilistic model that
can estimate from empirical analysis how the passenger flows are dispatched to
different routes and trains. We validate our approach using a large scale data
set collected from the Shenzhen metro system. The measured results provide us
with useful inputs when building the passenger path choice model.
