Floating car aggregation detection method based on multivariate data fusion

Abstract

The invention discloses a floating car aggregation detection method based on multivariate data fusion. According to the method, a region in need of analysis is mapped to a virtual grid, GPS data and bayonet data of the floating cars are combined to form a high-efficiency and reliable calculation platform. Through use of the multivariate data, the calculation can be ensured to be more accurate, and thus, the floating car aggregation detection accuracy and the efficiency can be improved.

Description

technical field [0001] The invention belongs to the field of monitoring of floating cars, and in particular relates to a method for detecting the aggregation of floating cars based on multivariate data fusion. Background technique [0002] With the development of smart cities, information-based traffic management platforms have been established around the world to realize real-time monitoring of traffic information. Floating vehicles refer to buses and taxis equipped with on-board GPS positioning devices. Through the GPS positioning satellite traffic management platform, information such as the position, direction and speed of vehicles can be obtained regularly. With the continuous increase of the number of floating vehicles, the monitoring and management of floating vehicles is particularly important. Among them, the detection of floating car gathering is of great significance to the prevention of criminal activities, because a large number of floating cars gathering will ...

AI Technical Summary

Problems solved by technology

In the existing technologies for detecting the accumulation of floating cars, the judgment basis is based on the data of floating cars, and the total number of floating cars in a certain area is judged regularly, but this kind of unit data judgment often leads to the accumulation of floating cars due to complex road conditions. The detection is not accurate, and the aggregation behavior of floating vehicles cannot be well distinguished from the congestion of vehicles; at the same time, the existing technology cannot make accurate judgments in time at the beginning of the aggregation of floating vehicles, and it is often only when the aggregation of floating vehicles occurs. Knowing that it is difficult to effectively prevent and deal with floating car gathering events

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