Short-time traffic flow prediction method considering spatial-temporal correlation

Abstract

The invention relates to a short-time traffic flow prediction method considering spatial-temporal correlation. The influence of temporal correlation on the traffic flow of a target detection point is considered, and a short-time traffic flow temporal correlation prediction value is acquired; the spatial correlation of the object traffic flow is analyzed and researched by using a hierarchical clustering method, and multiple key spatial correlation points are determined; the influence of the traffic flow of the spatial correlation points on the traffic flow of the target detection point is considered, and a short-time traffic flow spatial correlation prediction value is acquired; the temporal correlation prediction value, the spatial correlation prediction value and the prediction value of the present method are integrated by using an "entropy method" so that the final prediction result of the short-time traffic flow of the target detection point is generated; and the prediction error is evaluated and analyzed according to the prediction result of the traffic flow and the actual traffic data. According to the method, the defect of the present method that the spatial-temporal characteristics cannot be fully utilized can be overcome, and the spatial-temporal correlation prediction result and the prediction result of the present method can be further integrated so that the accuracy of the short-time traffic flow prediction result can be effectively enhanced.

Description

technical field [0001] The invention relates to the field of intelligent transportation, in particular to a short-term traffic flow prediction method considering temporal and spatial correlation. Background technique [0002] While today's intelligent transportation system brings convenience to citizens, it inevitably leads to problems such as environmental pollution, resource waste, and traffic congestion. How to effectively alleviate the bottleneck of urban traffic congestion has become an unresolved problem faced by major cities. Accurate traffic flow forecasting can provide guiding suggestions for citizens' travel and urban traffic planning, and at the same time, pre-guided traffic flow can effectively prevent the occurrence and evolution of traffic congestion. [0003] The traffic flow prediction methods that have been proposed at home and abroad mainly include time series method, Kalman filter, chaos theory, neural network and support vector machine (SVM) and so on. ...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a short-term traffic flow forecasting method that considers temporal-spatial correlation. The method utilizes temporal-spatial correlation information of urban road traffic flow for prediction, which can overcome the deficiency that existing methods cannot make full use of temporal-spatial features, and can further The prediction results of spatio-temporal correlation and the prediction results of existing methods are fused to improve the accuracy of short-term traffic flow prediction

Images