Real-time limit learning machine short-time traffic flow prediction method based on fusion

Abstract

The invention discloses a real-time limit learning machine short-time traffic flow prediction method based on fusion. The method, for prediction technology in short-time unstable traffic flow scene, predicts short-time traffic flow on the basis of the realtimeness, the accuracy, and the reliability of short-time traffic flow and a fused real-time limit learning machine. The short-time traffic flow prediction method is based on a simplified single-implicit-strata feedforward neural network structure, may fast train historical data at a traffic flow peak value and updates reached data in an increment way, and saves learning time while guaranteeing certain prediction precision. Further, the method guarantees the stability and the robustness of short-time traffic flow prediction by using a fusing mechanism, performs reconstruction during a data missing and violent fluctuation period, and is short in training time. The root mean square error and the standard error percentage of a prediction result are both in a confidence region.

Description

technical field [0001] The invention mainly relates to the fields of intelligent transportation systems such as machine learning and traffic flow forecasting, especially a fusion-based real-time extreme learning machine short-term traffic flow forecasting method. Background technique [0002] With the development of the global economy and the progress of social urbanization, the development of the transportation industry has become more and more important. As an important material basis for the progress of human society, the transportation industry is the lifeblood of the entire national economic development. However, in recent years, the gradual increase of road vehicles has led to a deteriorating traffic operation efficiency. Traffic congestion, traffic exhaust pollution, complicated and inefficient traffic operations, and frequent traffic accidents have brought troubles to people's travel and other social activities. In order to alleviate traffic problems, a series of ad...

AI Technical Summary

Problems solved by technology

[0003] In the early short-term traffic flow forecasting methods, most of the traffic flow forecasting methods were forecasted under the assumption of simple and balanced traffic flow data, and there were many restrictions on the data set, such as equal interval sampling, moderate interval length, appropriate number of historical data samples, The sample data has no noise, etc., but in the real traffic scene, due to the failure of the equipment itself or the interference of external factors (such as bad weather, abnormal roads, etc.), the traffic flow data is prone to loss and mutation during the collection and transmission process; During holidays, the traffic flow tends to surge and reach the peak value. These situations can lead to non-stationary and nonlinear heterogeneity in the traffic flow data. Here, the heterogeneity value data distribution is uneven and complex, coupled with the consideration of equipment cost factors , many data monitoring, collection, processing, transmission and other equipment cannot cover the entire traffic network, which increases the heterogeneity of traffic flow data. All these factors add difficulty to the modeling of traffic flow prediction models. Performance, accuracy, and stability need to be improved

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