Short-term traffic flow weighted combination prediction method

Abstract

The invention discloses a short-term traffic flow weighted combination prediction method, which comprises the following steps of: (1) organizing historical traffic flow data by utilizing a dynamic clustering algorithm; (2) performing short-term traffic flow prediction by using an improved nearest neighbor nonparametric regression method; (3) performing the short-term traffic flow prediction by taking a cluster which is the most similar to a current point in a historical database as a training sample of a fuzzy neural network and using a fuzzy neural network model; and (4) determining the weight of a combined prediction method according to a prediction error of the improved nearest neighbor nonparametric regression method and the fuzzy neural network model in the last time bucket, and outputting a final prediction result in a weighted combination way. A traffic flow in the last time bucket and a traffic flow of related turning at an upstream road junction are taken into account, the training sample of the fuzzy neural network is optimized, and the final prediction result is output in the weighted combination way, so that short-term traffic flow prediction accuracy and real-time performance are improved.

Description

technical field [0001] The invention relates to a traffic flow forecasting method, in particular to a short-term traffic flow weighted combination forecasting method. Background technique [0002] In recent years, with the development of social economy and the rapid increase of motor vehicles, urban traffic problems are becoming more and more serious, and traffic pressure is increasing. Under such realistic conditions, intelligent transportation systems can flourish. The intelligent transportation system is mainly to realize the all-round, real-time, accurate and efficient induction and control of transportation in a large range. Predicting the traffic flow in the next period according to the current traffic flow is the premise and basis of dynamic traffic guidance. Only with high-precision real-time traffic flow information can we further use modern communication technology and computer technology to provide travelers with the best traffic flow information. driving route ...

AI Technical Summary

Problems solved by technology

[0004] At present, the single short-term traffic flow forecasting method requires unique information characteristics and specific applicable conditions, resulting in a single forecasting model with low accuracy for complex traffic flow forecasting, and often requires a lot of analysis and judgment before forecasting to select the best traffic flow. best method

Some combined short-term traffic flow forecasting methods often cannot take into account the accuracy and real-time performance of the forecast at the same time. Although some of these combined methods can improve the forecasting accuracy to a certain extent, their algorithms are complex and the amount of calculation is large, which is not conducive to real-time traffic flow forecasting. traffic flow prediction

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