  Accurate short-term passenger flow prediction in urban rail transit stations
has great benefits for reasonably allocating resources, easing congestion, and
reducing operational risks. However, compared with data-rich stations, the
passenger flow prediction in newly-operated stations is limited by passenger
flow data volume, which would reduce the prediction accuracy and increase the
difficulty for station management and operation. Hence, how accurately
predicting passenger flow in newly-operated stations with limited data is an
urgent problem to be solved. Existing passenger flow prediction approaches
generally depend on sufficient data, which might be unsuitable for
newly-operated stations. Therefore, we propose a meta-learning method named
Meta Long Short-Term Memory Network (Meta-LSTM) to predict the passenger flow
in newly-operated stations. The Meta-LSTM is to construct a framework that
increases the generalization ability of long short-term memory network (LSTM)
to various passenger flow characteristics by learning passenger flow
characteristics from multiple data-rich stations and then applying the learned
parameter to data-scarce stations by parameter initialization. The Meta-LSTM is
applied to the subway network of Nanning, Hangzhou, and Beijing, China. The
experiments on three real-world subway networks demonstrate the effectiveness
of our proposed Meta-LSTM over several competitive baseline models. Results
also show that our proposed Meta-LSTM has a good generalization ability to
various passenger flow characteristics, which can provide a reference for
passenger flow prediction in the stations with limited data.
