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

Abstract

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.

A Short-Term Traffic Flow Reliability Prediction Method considering Traffic Safety

“…To evaluate the effectiveness of the DE-BPNN model and some conventional models, the four most commonly used performance indicators were selected for regression problems: the mean absolute error (MAE), the MSE, the mean absolute percentage error Mathematical Problems in Engineering (MAPE), and the mean square percent error (MSPE). All indicators are defined as follows: y t and y t represent the detection value and the model prediction value of traffic flow, respectively [21,22].…”