Traffic parameter short-time prediction method based on empirical mode decomposition and classification combination prediction in abnormal state

Abstract

A traffic parameter short-time prediction method based on empirical mode decomposition (EMD) and classification combination prediction in an abnormal state relates to the technical field of traffic information. The prediction method includes being combined with the data sequence process method of the EMD, solving unstable data sequence of traffic parameters in an abnormal state into a stable intrinsic mode function (IMF) with multi-scale features; constructing a filter bank based on EMD filtering characteristics, reorganizing the IMF into high-frequency filtering, medium-frequency filtering, and low-frequency filtering; according to different characteristics of the IMF of each group, performing predictions by using the grey theory, kalman filtering and auto regressive moving average (ARMA) model respectively; accumulating results of all the groups to generate real-time predicting results of the traffic parameters of next time interval; and according to the real-time predicting results of the traffic parameters and historical data in the abnormal state, and performing multistep prediction so as to obtain a final predicting result of the traffic parameters and a future development tendency. The traffic parameter short-time prediction method based on the EMD and the classification combination prediction in the abnormal state has a better predicting capacity on the traffic parameters in the abnormal state and a future variation tendency.

Description

technical field [0001] The invention relates to the technical field of traffic information, in particular to a short-term traffic parameter prediction method based on empirical mode decomposition and classification combination prediction under abnormal conditions. Background technique [0002] Short-term traffic parameter prediction is one of the core issues in the construction of intelligent transportation systems. It provides the basic conditions for advanced traffic management systems (ATMS) to formulate active traffic control strategies and traffic travel information systems (ATIS) for real-time route guidance. [0003] Traffic parameter prediction refers to the real-time prediction of traffic parameters at the next decision-making time t+Δt and even several moments later at time t. It is generally considered that the prediction time span from t to t+Δt is not more than 15 minutes for short-term traffic parameter prediction. The results of traffic parameter prediction c...

AI Technical Summary

Problems solved by technology

[0005] However, under abnormal event conditions, due to the uncertainty of its cause, the randomness of occurrence time and location, the changes in traffic parameters such as traffic flow and travel time show a discontinuous state after the event, and With the great difference in the severity of the incident, its huge destructiveness also directly leads to the lack of historical data

However, the various methods mentioned above cannot meet the requirements of prediction accuracy, applicability and large amount of historical data at the same time.

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