Road network traffic bottleneck identification method integrating wavelet transform and image binaryzation

Abstract

The invention discloses a road network traffic bottleneck identification method integrating wavelet transform and image binarization. The method mainly comprises the following steps: inputting speed time sequence data, and searching time and positions of a speed sudden change point and an output speed sudden change point according to a wavelet transform principle; sorting the speed values of the speed sudden change points from small to large, selecting a speed threshold value according to a quartile method, and converting speed time sequence data into a speed space-time thermodynamic diagram;setting a speed threshold as an image binaryzation parameter, and dividing the speed space-time thermodynamic diagram into a congestion area and a non-congestion area through image binaryzation; finally, for irregularly scattered congestion areas in the binarized image, considering that the influence of the irregularly scattered congestion areas on the congestion of the whole traffic network is relatively small, eliminating irregularly and randomly distributed pixel points and small bottleneck points by utilizing median filtering so as to improve the calculation efficiency and the identification accuracy of frequently occurring traffic bottlenecks in the urban road network.

Description

technical field [0001] This application relates to the field of traffic information, in particular, to a road network traffic bottleneck identification method integrating wavelet transform and image binarization. Background technique [0002] The problem of traffic congestion seriously affects the operating efficiency of the city and people's daily travel. Expressway has the characteristics of large capacity and fast speed, and has obvious effect on traffic improvement, so it is often the first choice of travelers. However, the main and auxiliary roads of the expressway are not well connected, the traffic organization at the entrance and exit is unreasonable, the traffic capacity does not match, and the surrounding supporting road network is not perfect, etc., resulting in frequent traffic congestion on the expressway. [0003] In practice, GPS data are especially important for the study of traffic congestion. By processing, analyzing and mining massive GPS data, the road ...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the problem of lack of accuracy and reliability in current bottleneck identification, the present invention proposes a road network traffic bottleneck identification method integrating wavelet transform and image binarization to realize the goal of quickly and accurately identifying the bottleneck of urban expressway

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