Traffic control sub-area dividing method based on vehicle trajectory data

Abstract

The invention discloses a traffic control sub-area dividing method based on vehicle trajectory data. The method comprises the following steps of: numbering intersections of a certain area, and acquiring block port data, road section length data and a road network base map of the area; collecting the block port data, driving the travel chain of a single vehicle to trajectory data, and eliminating invalid tracks; performing repairing of the missing vehicle trajectory based on the shortest path principle; calculating the dual-direction flow between each intersection pair in a certain time frame to form an intersection pair flow matrix; taking the intersections as nodes and taking the dual-direction flow between the intersection pairs as the association degree of the node connection lines to establish a full-connected directed network diagram, and employing a random walk map algorithm to perform initial division of the network diagram; performing the connectivity inspection of the same type of intersections, and performing correction of the divided non-connection portions of the real road network; and finishing the division, and projecting the division result onto the real road network. The traffic control sub-area dividing method can more fully excavate the information in the traffic flow and can analyze the structure in the traffic network from the global angle.

Description

technical field [0001] The invention relates to the technical field of traffic control, in particular to a traffic control sub-area division method based on vehicle track data. Background technique [0002] Community discovery algorithms originated from the research on social networks, which represent the interpersonal relationship between people in the real society, thus abstracting the concept of social networks in the academic sense. A social network is composed of nodes and connections between nodes. Each node in the social network represents everyone in the real society, and each edge in the social network represents the relationship between people. In a real social network, it can often be found that some nodes are very closely connected, while some nodes are relatively sparsely connected. In order to discover such a community structure in a social network, the research on community discovery algorithms is promoted. The purpose of the community discovery algorithm is ...

AI Technical Summary

Problems solved by technology

[0005] At present, most of the division of signal control sub-areas is based on the actual road network and the correlation degree of adjacent intersections. Such a division scheme only considers the correlation of adjacent intersections, but does not consider non-adjacent intersections. At the same time, there is a lack of analysis of the traffic flow direction, and a lot of information has been lost before the sub-area is divided, so it is difficult to guarantee the control effect after the sub-area is divided.

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