Short-term metro passenger flow forecasting using ensemble-chaos support vector regression
mode - subway/metro, place - asia, ridership - forecasting, operations - scheduling, organisation - workforce planning
Short-term passenger flow forecasting, chaos, support vector regression, ensemble learning
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.
Permission to publish the abstract has been given by Taylor&Francis, copyright remains with them.
Shi, Z., Zhang, N., Schonfeld, P.M., & Zhang, J. (2020). Short-term metro passenger flow forecasting using ensemble-chaos support vector regression. Transportmetrica A: Transport Science, Vol. 16(2), pp. 194-212.