Abstract
Crowds management during peak commuting hours is a key challenge facing metro systems worldwide, which results in serious safety concerns and unfair public transit service for commuters on different origin-destination (o-d) pairs. In “Online Passenger Flow Control in Metro Lines,” the authors investigate the impact of online decision making on the value of passenger flow control solution methodologies. The authors formulate the problem as a stochastic dynamic program with a fairness (fill rate) constraint and exploit Blackwell's approachability theorem and Fenchel duality to characterize the attainable service level of each o-d pair. They use these insights to develop online policies that can enable more passengers boarding a train (efficiency) as well as ensure equitable service level (fairness) provided to each o-d pair. Numerical experiments on a set of transit data from Beijing show that this approach performs well compared with existing benchmarks in the literature.
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