During peak hours, metro systems often operate at high service frequencies to
transport large volumes of passengers. However, the punctuality of such
operations can be severely impacted by a vicious circle of passenger congestion
and train delays. In particular, high volumes of passenger boardings and
alightings may lead to increased dwell times at stations, that may eventually
cause queuing of trains in upstream. Such stations act as active bottlenecks in
the metro network and congestion may propagate from these bottlenecks to the
entire network. Thus, understanding the mechanism that drives passenger
congestion at these bottleneck stations is crucial to develop informed control
strategies, such as control of inflow of passengers entering these stations. To
this end, we conduct the first station-level econometric analysis to estimate a
causal relationship between boarding-alighting movements and train flow using
data from entry/exit gates and train movement data of the Mass Transit Railway,
Hong Kong. We adopt a Bayesian non-parametric spline-based regression approach
and apply instrumental variables estimation to control for confounding bias
that may occur due to unobserved characteristics of metro operations. Through
the results of the empirical study, we identify bottleneck stations and provide
estimates of optimum passenger movements per train and service frequencies at
the bottleneck stations. These estimates, along with real data on daily demand,
could assist metro operators in devising station-level control strategies.