A new synergistic forecasting method for short-term traffic flow with event-triggered strong fluctuation. (English) Zbl 1403.93126
Summary: 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.
MSC:
| 93C65 | Discrete event control/observation systems |
| 93E03 | Stochastic systems in control theory (general) |
| 60J10 | Markov chains (discrete-time Markov processes on discrete state spaces) |
| 90B20 | Traffic problems in operations research |
| 93-04 | Software, source code, etc. for problems pertaining to systems and control theory |
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