An improved seasonal rolling grey forecasting model using a cycle truncation accumulated generating operation for traffic flow

Xiao, Xinping; Yang, Jun; Mao, Shuhua; Wen, Jianghui

doi:10.1016/j.apm.2017.07.010

Cited by 119 publications

(52 citation statements)

References 22 publications

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“…en, this idea was further applied to the short-term traffic flow prediction problem [30,31]. According to the grey system theory, the effect of new information is greater than that of old information.…”

Section: Introductionmentioning

confidence: 99%

A Novel Weighted Fractional GM(1,1) Model and Its Applications

et al. 2020

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Based on the ideas of the new information priority principle and the fractional-accumulation generating operator, in this paper we propose a novel weighted fractional GM(1,1) (WFGM(1,1)) prediction model. In the new model, the original sequence is first transformed by using the weighted fractional-accumulation generating operator, which involves two parameters. With special choices of these parameters, the proposed WFGM(1,1) model reduces to the classical GM(1,1) model and the fractional GM(1,1) (FGM(1,1)) model, as well as the new information priority GM(1,1) (NIPGM(1,1)) model studied recently. Stability property of the WFGM(1,1) model is studied in detail. In practice, the quantum particle swarm optimization algorithm is adopted to choose the quasi-optimal parameters for the new model so as to get the best fitting accuracy. Finally, four numerical examples from different practical applications are present. Numerical results show that the new proposed prediction model is very efficient and has both the best fitting accuracy and the best prediction accuracy compared with the GM(1,1) and the FGM(1,1) as well as the NIPGM(1,1) prediction models.

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Section: Introductionmentioning

confidence: 99%

A Novel Weighted Fractional GM(1,1) Model and Its Applications

et al. 2020

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“…Since the grey prediction model was proposed, it has been widely concerned by scholars. The existing studies mainly focus on the theoretical development from the aspects of background value optimization [3,4], parameter optimization [5][6][7], model expansion [8][9][10], and the application of natural gas [11,12], electric power [13][14][15][16], environment [17][18][19][20], economy [21,22], and transportation [23,24].…”

Section: Introductionmentioning

confidence: 99%

Fractional‐Order Accumulative Linear Time‐Varying Parameters Discrete Grey Forecasting Model

Gao

Zhan

Liu

2019

Mathematical Problems in Engineering

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Traditional discrete grey forecasting model can effectively predict the development trend of the stabilizing system. However, when the system has disturbance information, the prediction result will have larger error, and there will appear significant downward trend in the stability of the model. In the presence of disturbance information, this paper presents a fractional-order linear time-varying parameters discrete grey forecasting model to deal with the system that contains both linear trend and nonlinear trend. The modeling process of the model and calculation method are given. The perturbation bounds of the new model are analyzed by using the least-squares method of perturbation theory. And it is compared with that of the first-order linear time-varying parameters discrete grey forecasting model. Finally, two real cases are given to verify the effectiveness and practicality of the proposed method.

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“…Once the data volume is insufficient, the prediction accuracy will be depressed. Xiao et al [11] proposed an improved binding cycle truncation accumulated generating operation seasonal grey rolling forecasting model based on the properties of similar seasonality within intraday and weekly trends. The model weakens the random disturbance and highlights the intrinsic grey exponent rule after accumulating the sequence, so that the model has better performance under different traffic flow conditions.…”

Section: Introductionmentioning

confidence: 99%

A New Synergistic Forecasting Method for Short-Term Traffic Flow with Event-Triggered Strong Fluctuation

Huang

Deng

2018

Journal of Control Science and Engineering

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Directing against the shortcoming of low accuracy in short-term traffic flow prediction caused by strong traffic flow fluctuation, a novel method for short-term traffic forecasting based on the combination of improved grey Verhulst prediction algorithm and first-order difference exponential smoothing is proposed. Firstly, we constructed an improved grey Verhulst prediction model by introducing the Markov chain to its traditional version. Then, based on an introduced dynamic weighting factor, the improved grey Verhulst prediction method, and the first-order difference exponential smoothing technique, the new method for short-term traffic forecasting is completed in an efficient way. Finally, experiment and analysis are carried out in the light of actual data gathered from strong fluctuation environment to verify the effectiveness and rationality of our proposed scheme.

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An improved seasonal rolling grey forecasting model using a cycle truncation accumulated generating operation for traffic flow

Cited by 119 publications

References 22 publications

A Novel Weighted Fractional GM(1,1) Model and Its Applications

A Novel Weighted Fractional GM(1,1) Model and Its Applications

Fractional‐Order Accumulative Linear Time‐Varying Parameters Discrete Grey Forecasting Model

A New Synergistic Forecasting Method for Short-Term Traffic Flow with Event-Triggered Strong Fluctuation

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