Resumen
Public transportation systems are an effective way to reduce traffic congestion, air pollution, and energy consumption. Today, smartcard technology is used to shorten the time spent boarding/exiting buses and other types of public transportation; however, this does not alleviate all traffic congestion problems. Accurate forecasting of passenger flow can prevent serious bus congestion and improve the service quality of the transportation system. To the best of the current authors? knowledge, fewer studies have used smartcard data to forecast bus passenger flow than on other types of public transportation, and few studies have used time-series lag periods as forecast variables. Therefore, this study used smartcard data from the bus system to identify important variables that affect passenger flow. These data were combined with other influential variables to establish an integrated-weight time-series forecast model. For different time data, we applied four intelligent forecast methods and different lag periods to analyze the forecasting ability of different daily data series. To enhance the forecast ability, we used the forecast data from the top three of the 80 combined forecast models and adapted their weights to improve the forecast results. After experiments and comparisons, the results show that the proposed model can improve passenger flow forecasting based on three bus routes with three different series of time data in terms of root-mean-square error (RMSE) and mean absolute percentage error (MAPE). In addition, the lag period was found to significantly affect the forecast results, and our results show that the proposed model is more effective than other individual intelligent forecast models.