A Deep-Learning Based Optimization Approach to Address Stop-Skipping Strategy in Urban Rail Transit Lines

TL;DR

This paper presents a data-driven optimization method using deep learning and ant colony algorithms to improve stop-skipping strategies in urban rail transit, reducing passenger travel and waiting times.

Contribution

It introduces a novel integration of LSTM-based demand prediction with an optimization framework for real-time stop-skipping decisions in urban rail transit.

Findings

Improved passenger travel time and waiting time with the proposed approach.

Effective real-time demand prediction using LSTM models.

Enhanced transit service performance demonstrated on real case data.

Abstract

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.