A Traffic Flow Forecasting Method Based on Spatial Copula Theory

Abstract

The invention discloses a traffic flow prediction method based on the spatial copula theory. The traffic flow prediction method comprises the steps of obtaining the geographical positions of magnetic induction loop devices in a road section and the traffic flow data counted by the magnetic induction loop devices, calculating the distances between each two magnetic induction loop devices based on the geographic positions of the sample points, determining a correlation function model suitable for the samples according to semivariable functions, conducting edge distribution fitting through the sample traffic flow, selecting a copula model to calculate the correlation coefficient of traffic flow based on distance, so as to verify the feasibility of the selected copula model, and calling the model for prediction. The spatial characteristics of traffic flow are taken into consideration, the traffic flow distribution types are explored, and high precision and reliability are ensured.

Description

technical field [0001] The invention belongs to the technical field of intelligent traffic information processing, in particular to a traffic flow prediction method based on space copula (association) theory. Background technique [0002] With the development of the economy and the popularization of automobiles, the traffic flow on the roads increases year by year, and its growth rate exceeds the construction speed of the roads, causing traffic jams to always exist. Therefore, it is a foregone conclusion to build roads to expand traffic capacity. Traffic flow refers to the amount of traffic passing through a certain location, a certain section or a certain lane of a road within a selected period of time. At the same time, traffic flow is also one of the elements of the traffic system, which is of great significance to the intelligent transportation system (ITS). Reduce road congestion and improve the utilization of road resources. In the data age, the accuracy and complet...

AI Technical Summary

Problems solved by technology

The ARIMA model is suitable for short-term passenger flow forecasting and requires complete and accurate historical data, but the lack of data affects the continuity of the time series and cannot capture the dependence between adjacent time series observations; the K-NN algorithm is based on the k samples to determine the samples to be predicted, and the scope of application is limited to areas with stable traffic flow; the spatial interpolation method uses the semivariogram relationship between sample points and performs interpolation prediction on unknown points based on adjacent sample points. It is better to know that the area where the sample points are densely distributed, but in the area where the sample points are sparse, there will be a large error

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