Congestion in near capacity metro operations: optimum boardings and alightings at bottleneck stations.

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.