With reliable and accurate predictions of short-term passenger flow, metro agencies can assign proper trains and crews, optimize schedules, and operate efficiently. This paper presents a hybrid model, which is intended for forecasting short-term passenger flow. The forecasting model aggregates SVR-based sub-models developed with subsets extracted by combining bootstrap sampling with dimensions subsampling. The sub-models consider not only the data from the reconstructed chaos attractor but also the periodic historical data. Passenger flow data collected from four different metro stations in Nanjing are used for model implementation and performance evaluation. Results confirm that the proposed EICSVR model was able to significantly improve predictive performance and generalization. The excellent accuracy and stability obtained in the empirical study indicate that the proposed model has good development potential for forecasting short-term passenger flow.
Short-term metro passenger flow forecasting using ensemble-chaos support vector regression
Transportmetrica A: Transport Science ; 16 , 2 ; 194-212
07.02.2020
19 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Short-to-medium Term Passenger Flow Forecasting for Metro Stations using a Hybrid Model
Springer Verlag | 2017
|Short-to-medium Term Passenger Flow Forecasting for Metro Stations using a Hybrid Model
Online Contents | 2018
|Forecasting of Short-Term Metro Ridership with Support Vector Machine Online Model
DOAJ | 2018
|