Predicting Model of Traffic Volume Based on Grey-Markov

Zhang, Yinpeng

doi:10.5539/mas.v4n3p46

Cited by 13 publications

(16 citation statements)

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“…Specifically in transportation, the applications of the theory mainly have been on volume, traffic accident, and pavement design. Among them, GM(1,1) is combined with a Markov transition matrix by (Zhang (2010)) to forecast annual average daily traffic data. A comparison study by ) presents performance of GM(1,1) against back propagation neural network (NN) and radial basis function NN.…”

Section: Introductionmentioning

confidence: 99%

Short-term freeway traffic parameter prediction: Application of grey system theory models

Bezuglov

Comert

2016

Expert Systems with Applications

175

106

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Section: Introductionmentioning

confidence: 99%

Short-term freeway traffic parameter prediction: Application of grey system theory models

Bezuglov

Comert

2016

Expert Systems with Applications

175

106

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“…Based on the above analysis, we know that the model GM (1,1) and the Markov model could be integrated with each other to forecast by their advantages. That is: model GM (1,1) can be used to forecast the change trend of data sequences, while the Markov chain model can be used to decide the vibration regulation of their development [7] [8].…”

Section: Introductionmentioning

confidence: 99%

Forecast of civil aviation freight volume using unbiased grey-fuzzy-Markov chain method

Chen

Tse

2013

2013 6th International Conference on Information Management, Innovation Management and Industrial Engineering

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This paper presented an unbiased grey-Markov chain method to forecast civil aviation freight volume. It combines the unbiased grey system theory and fuzzy classification. When forecasting freight volumes, uncertainty factors often cause deviation in estimations derived from traditional grey model. Therefor fuzzy classification is a good tool to integrate with unbiased grey analysis to reduce the residual resulted from these uncertain factors. This method can take advantage of the prediction power of conventional Grey-Markov forecasting method and simultaneously eliminate grey bias and improve anti-jamming performance. Finally, this method is applied in forecasting the Chinese civil aviation fright volume. Our experimental result shows that the model not only discovers the trend of the freight volumes but also overcomes the random fluctuation of data affecting precision.

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“…Markov state transition matrices play the remedial role in overcoming the limitation of the grey forecasting model and are discussed in recent works. These literatures in [46][47][48] indicated that Markov state matrices have clearly increased the accuracy of grey forecasting models.…”

Section: Grey Markov Model (Mgm)mentioning

confidence: 99%

Predicting the Trend of Taiwan’s Electronic Paper Industry by an Effective Combined Grey Model

Huang¹,

Wang²,

Dang³

et al. 2015

Sustainability

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Electronic paper (e-paper) is a major sector of Taiwan's Optoelectronic industry. It has paid much attention on the development of flexible displays. Even though the market is booming, the future is still unclear for business development. No research has yet forecasted the future market size of the e-paper industry. In addition, proposing an appropriate forecasting model to understand the trend of this industry plays a crucial role for market players and government's authorities in formulating correct strategies. Therefore, in this paper, the future market size of Taiwan's e-paper industry is predicted by an effective combined grey model. Two combinations of DGM(2,1) and Verhulst model with Fourier series and Markov chain, namely FM-Verhulst and FMDGM(2,1), were presented. Based on the annual data of Taiwan's e-paper industry, the results show that the forecasting performances of two FM-Verhulst and FMDGM(2,1) models are highly accurate compared with other grey models. Precision is 96.36% and 97.77%, respectively. However, for long-term prediction, the FMDGM(2,1) model obtains the best performance in all proposed grey models. With obtained forecasting results in Taiwan's e-paper industry by the FMDGM(2,1) model, it can be pointed out that the future market size of Taiwan's e-paper would slowly increase in the next few years.

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Predicting Model of Traffic Volume Based on Grey-Markov

Cited by 13 publications

References 0 publications

Short-term freeway traffic parameter prediction: Application of grey system theory models

Short-term freeway traffic parameter prediction: Application of grey system theory models

Forecast of civil aviation freight volume using unbiased grey-fuzzy-Markov chain method

Predicting the Trend of Taiwan’s Electronic Paper Industry by an Effective Combined Grey Model

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