Abstract
Due to the continuous improvement of productivity, the transportation demand of freight volume is also increasing. It is difficult to organize freight transportation efficiently when the freight volume is quite large. Therefore, predicting the total amount of goods transported is essential in order to ensure efficient and orderly transportation. Aiming at optimizing the forecast of freight volume, this paper predicts the freight volume in Xi’an based on the Gray GM (1, 1) model and Markov forecasting model. Firstly, the Gray GM (1, 1) model is established based on related freight volume data of Xi’an from 2000 to 2008. Then, the corresponding time sequence and expression of restore value of Xi’an freight volume can be attained by determining parameters, so as to obtain the gray forecast values of Xi’an’s freight volume from 2009 to 2013. In combination with the Markov chain process, the random sequence state is divided into three categories. By determining the state transition probability matrix, the probability value of the sequence in each state and the predicted median value corresponding to each state can be obtained. Finally, the revised predicted values of the freight volume based on the Gray–Markov forecasting model in Xi’an from 2009 to 2013 are calculated. It is proved in theory and practice that the Gray–Markov forecasting model has high accuracy and can provide relevant policy bases for the traffic management department of Xi’an.
Highlights
With the rapid development of the economy, China’s transportation develops rapidly and the traffic volume increases quickly, which provides great convenience for the logistic system [1]. e logistic system is one of the most crucial aspects of the regional economy and the freight volume is the largest component of the logistic system [2].us, freight volume can reflect the transportation development level to some extent
Randomness is a crucial factor that can cause an undesirable impact on the predicted results. e reason is that the actual probability of a result can be hard to determine by taking randomness into consideration [6]. roughout the comprehensive transportation development research, there have been abundant research results in the prediction of traffic volume related to transportation. e traditional forecast of freight volume usually includes the combination of qualitative and quantitative methods or the combination of subjective and objective methods [7,8,9]
Conclusion e Gray–Markov forecasting model is based on the shortterm advantages of the Gray prediction model
Summary
With the rapid development of the economy, China’s transportation develops rapidly and the traffic volume increases quickly, which provides great convenience for the logistic system [1]. e logistic system is one of the most crucial aspects of the regional economy and the freight volume is the largest component of the logistic system [2]. Due to the strong randomness, nonlinearity, and some other characteristics of freight volume change, the research of forecasting methods has always been the focus in this field. Among these characteristics, randomness is a crucial factor that can cause an undesirable impact on the predicted results. Li et al established the gray model to forecast the annual passenger departure volume of railway stations [12]. E traditional volume prediction methods include the exponential smoothing model, gray forecasting model, and regression analysis [4]. Is paper introduces the Markov chain into the Gray GM (1, 1) model and establishes the Gray–Markov forecasting model to predict the freight volume in Xi’an
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