In urban rail transit networks, transfer connections between lines during off‐peak hours significantly impact passenger experience. Our study introduces a novel approach to optimize these connections based on perceived waiting time. First, we established a functional relationship between actual transfer waiting time and transfer perceived waiting time (TWT–TPWT) across various scenarios, based on passenger arrival sequences at transfer platforms. We then developed an optimization model that minimizes total TPWT network wide by weighting transfer passenger volumes in each direction, while incorporating constraints related to train arrival/departure time at transfer stations. Using the Shenzhen Metro network’s off‐peak period as a case study, we implemented an improved simulated annealing algorithm to solve the model. Results demonstrate that our TWT–TPWT approach substantially enhances transfer connection efficiency: “just in time” connecting trains increased by 19, long‐waiting connections decreased by 16, and both total and average TPWTs decreased by 28.38%. Furthermore, we conducted sensitivity analyses on key model and algorithmic factors, including station dwelling time, train departure intervals, transfer walking time, and the algorithm’s reheating mechanism, providing insights for practical implementation.
Huang et al. (Thu,) studied this question.