Short-time traffic-flow combination prediction method

Abstract

The invention discloses a short-time traffic-flow combination prediction method. The method comprises the following steps of step 1, presetting an acquisition period and collecting traffic flow time sequence data of a prediction target point according to the acquisition period; if a prediction day of the prediction target point is not a holiday, collecting traffic flow time sequence data of days in at least adjacent previous three weeks, wherein the days are the same weekday with the prediction day; if the prediction day of the prediction target point is a holiday, collecting traffic flow time sequence data of days in at least adjacent previous three years, wherein the days are the same holiday with the prediction day; step 2, using a set experience modal decomposition method to decompose the traffic flow time sequence data into several intrinsic mode components possessing a same characteristic; step 3, using a BP neural network algorithm to carry out prediction on each intrinsic mode component obtained through decomposition by using the set experience modal decomposition method respectively, superposing prediction results of the intrinsic mode components and acquiring a final prediction result. By using the prediction method in the invention, prediction precision of the short-time traffic flow can be effectively increased.

Description

technical field [0001] The invention relates to a short-term traffic flow combination forecasting method, in particular to a short-term traffic flow combined forecasting method based on ensemble empirical mode decomposition (EEMD) and BP neural network, belonging to the field of intelligent traffic forecasting. Background technique [0002] Short-term traffic flow prediction has always been a hot research field in intelligent transportation systems. Real-time and accurate traffic flow prediction is the premise and key of traffic control and traffic guidance, and it is of great significance to alleviate urban traffic congestion and avoid waste of social resources. . [0003] At present, there are many methods for modeling and predicting short-term traffic flow. Common models include: historical average model, time series model, Kalman filter model, and neural network model. Among them, the historical average model is suitable for Road sections with small traffic flow fluctua...

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Problems solved by technology

[0003] At present, there are many methods for modeling and predicting short-term traffic flow. Common models include: historical average model, time series model, Kalman filter model, and neural network model. Among them, the historical average model is suitable for Road sections with small traffic flow fluctuations, but low prediction accuracy and poor ability to deal with unexpected traffic conditions; for road sections with small traffic flow fluctuations, the time series model can meet the prediction requirements due to its good real-time performance and stability, but the prediction The accuracy is too dependent on the number of samples; the Kalman filter model has high prediction accuracy for steady-state traffic flow, but the prediction accuracy depends on the linear characteristics of traffic flow, which is suitable for linear non-real-time online traffic flow prediction; neural network model adaptive learning ability Strong, good real-time performance, high prediction accuracy but slow convergence speed, suitable for complex, changeable and nonlinear traffic flow prediction

However, most of these models only consider the nonlinear characteristics of traffic flow. Traffic flow has many characteristics. If only one characteristic is considered, the prediction accuracy is limited.

[0004] In addition, due to the periodicity of people's work and rest, the traffic flow generated by travel has similarity and regularity in units of days, weeks, and months. Most jobs are based on similarities in units of days, which is rare. to work on similarity based on long-term scale measurements

Moreover, if the traffic flow of major holidays is predicted in units of adjacent days, there will be a large error, which is obviously not feasible

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