Izvorni znanstveni članak
https://doi.org/10.7307/ptt.v33i2.3561
Forecasting the All-Weather Short-Term Metro Passenger Flow Based on Seasonal and Nonlinear LSSVM
Xin Huang
; School of Civil Engineering and Transportation, South China University of Technology
Yimin Wang
; School of Civil Engineering and Transportation, South China University of Technology
Peiqun Lin
; School of Civil Engineering and Transportation, South China University of Technology
Heng Yu
; School of Civil Engineering and Transportation, South China University of Technology
Yue Luo
; School of Civil Engineering and Transportation, South China University of Technology
Sažetak
Accurate metro ridership prediction can guide passengers in efficiently selecting their departure time and simultaneously help traffic operators develop a passenger organization strategy. However, short-term passenger flow prediction needs to consider many factors, and the results of the existing models for short-term subway passenger flow forecasting are often unsatisfactory. Along this line, we propose a parallel architecture, called the seasonal and nonlinear least squares support vector machine (SN-LSSVM), to extract the periodicity and nonlinearity characteristics of passenger flow. Various forecasting models, including auto-regressive integrated moving average, long short-term memory network, and support vector machine, are employed for evaluating the performance of the proposed architecture. Moreover, we first applied the method to the Tiyu Xilu station which is the most crowded station in the Guangzhou metro. The results indicate that the proposed model can effectively make all-weather and year-round passenger flow predictions, thus contributing to the management of the station.
Ključne riječi
seasonal and nonlinear least square support vector machine; short-term subway passenger flow prediction; multi-model fusion prediction; time series
Hrčak ID:
270881
URI
Datum izdavanja:
7.4.2021.
Posjeta: 504 *