Ren J.-H.,University of Chinese Academy of Sciences |
Ren M.,Economic and Planning Research Institute of the Ministry of Railway
Journal of Railway Engineering Society | Year: 2013
Research purposes: The initial traffic volume composition of the Wuhan-Guangzhou Passenger Dedicated Line is analyzed and its operational efficiency is evaluated for the purpose of knowing the initial traffic volume and its composition of passenger dedicated line to urge the operator doing the better works of the transportation organization, transportation coordination and marketing. Research conclusions: The result shows the initial traffic volume composition of the Wuhan-Guangzhou Passenger Dedicated Line possesses the characteristics that the induced traffic volume is bigger than the lost traffic volume in the north areas to Yueyang while the lost traffic volume is bigger than the induced traffic volume in south areas to Yueyang. Though the occupancy rate of train is from 36.37% to 80.01% in the different areas, but the completed local railway passenger traffic volume and the utilization rate are low. It is proposed that in this case, the government should regard the high-speed railway as the public welfare undertaking and give the appropriate subsidy to the railway to assist the operator reducing the price of train ticket for enhancing the occupancy rate and giving the play to the high-speed railway, so that the ordinary people can afford and enjoy the efficient travel services of the high-speed railway.
Zhu Z.-H.,Economic and Planning Research Institute of the Ministry of Railway |
Weng Z.-S.,Economic and Planning Research Institute of the Ministry of Railway
Tiedao Xuebao/Journal of the China Railway Society | Year: 2011
Applying the phase space reconstruction method of the chaos theory, 12 groups of time series associated with rail traffic volumes were analyzed in respect of the chaotic statistics data of the embedding delay time, embedding dimension, correlation dimension and the largest Lyapunov exponent. In accordance, the chaotic characteristics of the 12 groups of time series were identified. The analytical results show as follows: The railway passenger and freight traffic volumes and turnovers do not possess chaotic characteristic, their four groups of corresponding time series are not chaostic sequences; the increments and growth rates of the passenger and freight traffic volumes and turnovers possess significant chaotic characteristic, their eight groups of corresponding time series are chaostic sequences. On the basis of such chaotic judgment, the railway passenger and freight traffic volumes and turnovers are forecasted and analyzed with the largest Lyapunov exponent forecasting model.