Meta-learning Based Short-Term Passenger Flow Prediction for Newly-Operated Urban Rail Transit Stations. (arXiv:2210.07098v1 [cs.LG])

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 …

arxiv flow meta meta-learning prediction rail transit