Road traffic flow condition prediction method in data sparse time period

Abstract

The invention provides a road traffic flow condition prediction method in a data sparse time period. When traffic flow data are insufficient, a transformation rule of a to-be-predicted road traffic flow in a time sequence is explored by using a time dynamic sequence supplementing method; a road traffic condition of a data sparse time period is restored by environment information feature extractionbased on a conditional random domain, so that a time evolution sequence of the road traffic flow in a period of time is obtained; matching with a historical time sequence of the road traffic flow isperformed to find out a time sequence fragment with a similar evolution trend and a traffic flow condition of a prediction time point is deduced. The test and on-site detection show that the prediction result of the traffic flow data sparse time period is basically accurate and reliable, so that defects of road traffic flow condition prediction in the data sparse time period in the prior art are effectively overcome, the traffic flow condition prediction weakness is eliminated, and the overall quality of the road traffic flow condition prediction method is improved.

Description

technical field [0001] The invention relates to a method for predicting road traffic flow conditions, in particular to a method for predicting road traffic flow conditions in a data-sparse period, and belongs to the technical field of road traffic flow condition prediction. Background technique [0002] At present, urban road traffic congestion is becoming more and more serious. Although there are more and more urban planning and construction road networks, due to the lack of efficient intelligent traffic guidance systems, some road sections are crowded and congested, and the utilization rate of some road sections is low. The overall traffic efficiency of the network is not high, and there is an urgent need to improve the ability to predict and plan urban road traffic conditions. With the development of intelligent road traffic systems, high-precision prediction of urban road traffic conditions can be widely used in traffic route planning, coordination of traffic congestion,...

AI Technical Summary

Problems solved by technology

This is a big problem in the prediction of realistic road traffic flow conditions. In reality, there are a large number of roads with sparse traffic flow data, and accurate prediction of road traffic flow conditions is also required. If the prediction accuracy of these road sections with sparse traffic flow data If it is too low, it will seriously affect the quality of the road traffic flow prediction of the whole road network, and even make the road traffic flow prediction of the whole road network into chaos. Therefore, the road traffic flow prediction in the data sparse time period is very important

[0004] Through the existing research, it is found that in some specific time periods such as sparse traffic flow data, especially in the early morning and other sparse time periods of traffic flow data, the existing technology cannot achieve a good prediction effect, and the prediction of road traffic flow conditions in sparse time periods The effect is very poor. This is because the existing technology needs a large amount of traffic flow training data as the basis to train an effective network for feature extraction. Therefore, in the time period when the traffic flow data is sparse, the existing technology cannot train an efficient network and cannot play a predictive role

[0005] On the whole, the existing technology mainly has the following defects: First, the prediction of some road traffic flow conditions in the existing technology mainly depends on a large amount of historical data and real-time road traffic flow information, and requires a large amount of reliable historical data of road tr...

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