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ATS International Journal
Editor in Chief: Prof. Alessandro Calvi
Address: Via Vito Volterra 62,
00146, Rome, Italy.
Mail to: alessandro.calvi@uniroma3.it

The research on the city bus traffic time sequence prediction mode based on empiricaldecomposition mode

Y. Ruo, C. Wei, L. Shan
Pages: 51-60

Abstract:

With the continuous expansion of the city size and the improvement of people’s economic level, the travel is more and more convenient. However, the traffic pressure is bigger. By forecasting the demand of the city bus network, we can formulate the scientific operation plans and allocate the bus resources reasonably. This can meet the daily travel and obtain higher income. In this paper, we propose ARMA predictive model based on the empirical mode decomposition. This model solves the premise that the sequences in the traditional time sequence analysis method must be smooth. Then it can improve the prediction accuracy. Finally, we apply the model to forecast the short-term traffic flow. The last example shows that the model has the pertinence and validity.
Keywords: Empirical Mode Decomposition; city bus traffic prediction; Auto Regression Moving Average

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