Highway Passenger Short-term Prediction Based On EEMD

Road transport is the transport commonly used in urban traffic. With its fast、direct access、comfortable、 high resilience、flexible、adaptable、less demanding for road conditions etc., more and more favored by the majority of passengers. Analyzing the characteristics and development trend of road passenger traffic, and accurately predicting traffic are the premise of successful revenue management. Through comprehensive research and analysis, get the most reasonable prediction scheme. The basic characteristics of the data provided by the road traffic and the current research on short-term forecasting at home and abroad provide a clear idea for the selection of short-term traffic forecasting methods; According to the characteristics that short-term forecasting in traffic affected by many factors and that data has the nature of nonlinearity and nonstationarity, Ensemble Empirical Mode Decomposition is applied to the study of short-term traffic. The original time series are decomposed into the sum of multiple intrinsic mode functions and trends. Study its internal information from the periodicity and volatility of time series on highway traffic. Reveal the cyclical variation between each mode and the volatility characteristics of different time periods. Establish the combination forecasting model through comprehensive analysis and model on the data of low and high frequency. This method is used to make the short-term prediction on road traffic. The results obtained show that this method can achieve good predictive results and has a high value, which illustrates this prediction method is reliable and effective. The results have a reference value for the development of future highway route and emergency program for passenger peak. The results also ensure the reasonableness of highway investment and avoid wasting resources. It is of great practical significance to adjust the transport structure to meet the needs of passenger traffic on different time periods and to improve the quality of road transport services.