  Different passenger demand rates in transit stations underscore the
importance of adopting operational strategies to provide a demand-responsive
service. Aiming at improving passengers' travel time, the present study
introduces an advanced data-driven optimization approach to determine the
optimal stop-skip pattern in urban rail transit lines. In detail, first, using
the time-series smart card data for an entire month, we employ a Long
Short-Term Memory (LSTM) deep learning model to predict the station-level
demand rates for the peak hour. This prediction is based on four preceding
hours and is especially important knowing that the true demand rates of the
peak hour are posterior information that can be obtained only after the peak
hour operation is finished. Moreover, utilizing a real-time prediction instead
of assuming fixed demand rates, allows us to account for unexpected real-time
changes which can be detrimental to the subsequent analyses. Then, we integrate
the output of the LSTM model as an input to an optimization model with the
objective of minimizing patrons' total travel time. Considering the exponential
nature of the problem, we propose an Ant Colony Optimization technique to solve
the problem in a desirable amount of time. Finally, the performance of the
proposed models and the solution algorithm is assessed using real case data.
The results suggest that the proposed approach can enhance the performance of
the service by improving both passengers' in-vehicle time as well as
passengers' waiting time.
