Urban intersection lane-level structure extraction method based on time-space trajectory big data

Abstract

The invention provides an urban intersection lane-level structure extraction method based on time-space trajectory big data, which reduces the cost for obtaining urban intersection structure, has simple detection method and is easy to realize. The method comprises following steps: first, obtaining position points of urban intersections according to an existing road axis-level road network graph, and then setting up a circular buffer area; extracting trajectory points whose course angle variation exceeds 45 degrees in trajectory data as turning changing points by means of a trajectory tracking method; secondary, performing clustering to the turning changing points by means of a density clustering method, and establishing an intersection range circle by extracting cluster centers and the spatial distance among them; finally, calculating the intersection points of the trajectory passing through the intersection range circle by means of the trajectory tracking method again, extracting turning entrance points and exit points of the intersection, and matching the turning entrance points and exit points of the intersection with lane axis of adjacent roads by means of spatial matching method to complete the extraction of urban intersection lane-level structure. The accuracy of intersection entrance point and exit point plane structure graph obtained by means of the method is 94.3%.

Description

Technical field [0001] The invention relates to an urban intersection lane-level structure extraction method based on time-space trajectory big data, and belongs to the field of geographic information system and intelligent transportation research. Background technique [0002] Urban road network information is the basis of intelligent transportation related applications. According to the fineness of the urban road network information, the urban road network can be divided into: road centerline network, roadway level network, and lane level network. Among them, the arcs of the road sections and the nodes that express the topological points of the road network are composed of the road network. important parts of. At present, the data sources for obtaining urban road network information include: high-resolution remote sensing images, spatio-temporal trajectory data, laser point cloud data, etc. With the continuous popularization of GPS devices, spatiotemporal trajectory data has b...

AI Technical Summary

Problems solved by technology

However, the above studies did not conduct an in-depth analysis and identification of the planar structure in the local area of ​​the intersection, nor did they further explore the spatial integration of the lane-level road network and the planar structure of the intersection.

Images