Multi-Time-Scale Modeling for Day-Ahead Forecasting of Passenger Travel-Time and Destinations Distribution

It is crucial to forecast passenger flows for optimizing subway operations and improving travel experience for passengers. While traditional methods usually estimate the average travel time of passengers, this study delves into the modeling and prediction travel time distributions from all origin stations to all destinations. We construct a matrix that includes the travel time and destination for passengers originating from each station at a time slot. We propose a traveltime and destinations distribution modeling approach for one-day ahead predictions for each origin station. By quantifying the multi-time-scale similarities of week-to-week, day-to-day, and time-to-time, we unveil the underlying mechanisms of passengers' travel patterns, revealing predictable and repetitive mobility behaviors. We validate the proposed model using real subway passenger data sets and demonstrate its superior performance in predicting the passenger flows and the associated travel time.