Tehnički vjesnik, Vol. 30 No. 4, 2023.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20230226000383
Analysis and Forecast of Railway Freight Volume based on Prophet-Deep AR Model
Fangcan Zhao
; School of Traffic and Transportation Beijing Jiaotong University, Beijing 100044, China
Baotian Dong
; School of Traffic and Transportation Beijing Jiaotong University, Beijing 100044, China
Yuanyun Sun
; China Railway Information Technology Center, Beijing 100844, China
Shiyao Huang
; School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
Sažetak
The research on railway freight volume forecast is of great significance to the allocation of railway freight transport resources, the formulation of transport plans and the organization of railway freight transport. This study, by fully mining the railway freight ticket data information, put forward the precise forecast model of railway freight volume under different types of freight. Firstly, the railway freight ticket data are cleaned, regulated and integrated, and the time series of the daily number of railway freight trains are constructed, get the trend, periodicity and holiday data of railway traffic data, and set the parameters of Chinese holidays and rest days according to the demand characteristics of different categories. Secondly, the forecasting result of the Prophet is taken as a cooperative parameter. DeepAR is used to forecast, and a combined model of the Prophet-DeepAR is constructed. Finally, the combined model was validated with Shanghai Railway Bureau data from January 1, 2015 to December 31, 2018 for the food and tobacco category, and with Prophet-DeepAR, LSTM, Wavelet, BILSTM, and Prophet-LSTM, prophet-wavelet and Prophet-Bilstm are used to compare the prediction results. The results show that the Prophet-DeepAR model can extract the multi-dimensional periodicity of freight traffic and mine the trend information of freight traffic, and get the prediction result with high precision. It has better accuracy than other models.
Ključne riječi
deepAR; deep learning; probabilistic interval prediction; prophet; railway freight volume
Hrčak ID:
305457
URI
Datum izdavanja:
28.6.2023.
Posjeta: 1.134 *