Non-linear combined prediction model of medium and long-term civil vehicle population of China

Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology

Volumn 10, Issue 6, 2010, Pages 103-108

  Deng, Wen ^a   Hu, Si Ji ^a  

^a BEIJING JIAOTONG UNIVERSITY   (China)

Author keywords

Grey Verhulst model; Neural network; Non liner prediction; Space time theory; Urban traffic

Indexed keywords

BP NEURAL NETWORKS; COMBINATION FORECASTING; CORRELATION COEFFICIENT; DEVELOPMENT TENDENCY; FORECASTING MODELS; GREY VERHULST MODEL; INTERNATIONAL EXPERIENCES; LINEAR COMBINATIONS; NON-LINEAR; POPULATION PREDICTIONS; PREDICTION MODEL; PRINCIPAL COMPONENTS; SPACE-TIME THEORY; URBAN TRAFFIC; VEHICLE DEVELOPMENT; VERHULST MODEL;

ECONOMIC ANALYSIS; MATHEMATICAL MODELS; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS; VEHICLES;

FORECASTING;

EID: 78651360104     PISSN: 10096744     EISSN: None     Source Type: Journal    
DOI: 10.1016/s1570-6672(09)60078-7     Document Type: Article

References (8)

1. Xia X F, Han L H. Forecast of Chinese automobile population in future [J]. Market Modernization, 2007, 1: 41-42.
2. Shen Z Y. Forecast of vehicle population based on income distribution curve [J]. China Energy, 2006, 28(8): 11-15.
3. James D Hamilton. Time Series Analysis [M]. New Jersey: Princeton University Press, 1994.
4. Zhou Q, Yang D Y. Multi-relative factor forecast of vehicle population by neural network [J]. Journal of Highway and Transportation Research and Development, 2001, 18(6): 126-129.
5. Zhang H W. Using the improved grey model to forecast Chinese automobile possession quantity in 2000 [J]. Journal of Liaoning Province Communications College, 1996, 4(2): 24-20.
6. Jack Johnston, John DiNardo. Econometric methods [J]. Econometric Theory, 2000, 16: 139-142.
7. Bauer S, Keller D, Weber F M, et al. An Efficient Prediction Based on Principal Component Analysis [M]. IF-MBE Proceedings, World Congress on Medical Physics and Biomedical Engineering, September 7-12, 2009, Munich, Germany.
8. Energy Research Institute National Development and Reform Commission. China's Low Carbon Development Pathways by 2050 [R]. Beijing: Science Press, 2009 (1): 46-49.