Road-network traffic jam discrimination method based on macroscopic fundamental diagram

Abstract

The invention discloses a road-network traffic jam discrimination method based on a macroscopic fundamental diagram. The method comprises the steps that 1) the road network is divided into traffic cells; 2) the macroscopic fundamental diagram of the traffic cells is drafted; 3) critical points of jamming levels are defined in the macroscopic fundamental diagram, and a traffic jamming state is defined; 4) jamming levels of the traffic cells are determined; and 5) the jamming state is output. According to the method, the related data bulk is low, data processing is simple, the jamming state discrimination speed is high, the method can be used to discriminate the jamming state of areas of different ranges and sizes, and the traffic cells are merged and decomposed flexibly to change the jamming discrimination range rapidly.

Description

technical field [0001] The invention relates to the field of urban road network traffic jam screening, in particular to a road network traffic jam screening method based on a macro basic graph. Background technique [0002] With the continuous expansion of urban traffic network scale and the increasing traffic flow, traffic congestion has become a common problem in major cities. How to quickly and effectively identify road network traffic congestion is the key issue to alleviate traffic congestion. For travelers, real-time road traffic status information can provide a reference for travelers to make travel decisions; for traffic managers, road Network status screening is conducive to quickly and accurately find the source of congestion, so as to take timely management measures to prevent further spread of congestion in time and space. Traditional traffic state recognition methods mainly use some related algorithms such as California series algorithm, McMaster algorithm, sta...

AI Technical Summary

Problems solved by technology

[0003] As mentioned above, the current traffic congestion status recognition technology has the following deficiencies: First, the current traffic congestion status judgment is mainly aimed at intersections or road sections, and reflects the congestion situation at intersections or road sections, which is not enough to represent The traffic status of the entire road network; second, the division of traffic status levels is not systematic, only simple statistics and averages are made, and it is difficult to reflect the relevance and integrity of each intersection and road section; third, the current congestion status Most of the evaluation indicators use speed, occupancy, saturation and other indicators that need to be calculated and processed. The acquisition process is complicated, and the effect of rapid screening cannot be achieved and the reliability is reduced.

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