Urban-road traffic forecasting method based on multi-source data combination

Abstract

The invention provides an urban-road traffic forecasting method based on multi-source data combination. The method comprises the steps of firstly, extracting permanent residents' travel OD according to cell-phone signaling data, and distributing the travel OD to an urban road network to obtain distributional traffic flows of each road section; secondly, according to a bayonet record, obtaining a total observed traffic flow and an observed traffic flow of frequently-used cars in a road section corresponding to the bayonet; thirdly, selecting a road section with observed traffic flows within the region, and building an equation of linear regression which represents the time-varying correlation between the distributional traffic flows and the observed traffic flows in the road sections, according to the distributional traffic flows and the observed traffic flow data; fourthly, according to the equation of linear regression and a proportion of the frequently-used cars within the region, constituting a dynamic forecasting model of traffic flows of the road sections within the region; fifthly, as for the road sections without observed traffic flows in the region, inputting the distributional traffic flows of the road sections into the dynamic forecasting model to forecast the time-varying traffic flows of the road sections. The urban-road traffic forecasting method based on multi-source data combination has the advantages of providing convenience for obtaining information and conducting traffic forecasting work in multiple cities, and being low in costs and easy to operate.

Description

Technical field [0001] The invention relates to an urban road flow prediction method based on multi-source data fusion. Background technique [0002] Road intersections and road cross-section flow are important components of urban traffic conditions. Accurate and reasonable traffic forecasting is the basis for traffic control and traffic flow guidance. There are three traditional methods for obtaining urban road traffic cross-section flow. The first and common method is to obtain it through population surveys. This not only consumes a lot of human and material resources, but also has a long survey period. These reasons lead to lack of timeliness in the results of population distribution perception. . The second is to use hardware devices such as loop coil detectors and video vehicle detectors to detect road cross-section traffic using recognition video or pressure sensing. The third is to obtain the urban traffic flow through the city short-term traffic flow forecast; the curre...

AI Technical Summary

Problems solved by technology

[0003] 1) Large area coverage still needs to invest a large cost;

[0004] 2) Due to the actual conditions of the testing site and hardware configuration problems, there are abnormal information in the testing;

[0005] 3) The method and model involve a large number of vector calculations, the algorithm is complex, and the amount of calculation is too large

[0006] To sum up, the current road flow forecasting methods still have deficiencies, or the survey coverage is too small, and it is difficult to obtain real-time information; or the forecasting methods are complex in technology, difficult to implement, and the model has a large amount of calculation, making it difficult to apply to actual traffic in a large area.

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