Intersection traffic flow feature parameter extraction method based on license plate data

Abstract

The invention discloses an intersection traffic flow feature parameter extraction method based on license plate data. The method comprises the following steps: 1, acquiring the license plate data detected by an intersection bayonet type electronic police in a road network, and acquiring a bayonet type electronic police equipment and intersection comparison table and a road segment and upstream-downstream intersection comparison table; 2, acquiring a run-off feature parameter of each detected vehicle; 3, adding passing information of the vehicle at an upstream intersection after each piece of passing data; 4, computing to acquire a vehicle arrival feature parameter of each passing data at the road segment upstream intersection; 5, respectively extracting driving feature parameters of the detected vehicle, the nearest vehicles which are in front of and behind the detected vehicle, in the same period with the detected vehicle and has a passing record at the upstream intersection; and 6, complementing the road segment travel time of the vehicle according to the obtained vehicle run off, arrival and driving feature parameters so as to obtain a traffic state of the road segment, uploading the traffic state to an intersection signal machine, and then adjusting the intersection signal time assignment.

Description

technical field [0001] The invention belongs to the technical field of parameter extraction in the traffic field, in particular to a method for extracting characteristic parameters of traffic flow at intersections based on number plate data. Background technique [0002] The traditional intersection traffic flow parameters are mostly obtained by statistical historical average method, which wastes a lot of manpower and material resources and cannot meet the real-time requirements of intersection signal timing. In recent years, with the coverage of various vehicle detectors on the road and the rise of online car-hailing companies represented by Didi and Uber, there are more channels for obtaining traffic flow information at intersections. Since the vehicle data obtained by online car-hailing companies are only vehicles with online car-hailing software installed, the overall parameters of traffic flow at intersections cannot be obtained directly for the time being. The most im...

AI Technical Summary

Problems solved by technology

[0005] (1) The extraction of intersection feature parameters is mainly based on manual observation and empirical methods, which wastes manpower and material resources, results in low accuracy, and a long parameter update cycle, which cannot meet the real-time and accuracy requirements of signal control

[0006] (2) The existing intersection traffic flow feature parameter extraction method based on video detector data and license plate data only extracts the departure...

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