Urban intersection automatic identification method based on low precision space-time trajectory data

Abstract

The invention provides an urban intersection automatic identification method based on low precision space-time trajectory data, which reduces the cost for obtaining urban intersection and provides basic data and a reference method for future auto-drive and intelligent traffic. The method comprises following steps: first, performing pretreatment to the data according to the spatial density and time interval of the trajectory data; secondly, tracking and identifying turning processes of the trajectory and extracting turning point pairs where the change of course angle exceeds a turning angle threshold value and the time interval is less than a time threshold value in one turning process; then, obtaining clusters and cluster centers of the turning point pairs by means of growing cluster method based on distance and space; finally, classifying the turning point pair clusters based on a cluster method of local point connection and completing intersection automatic identification through analyzing the turning property and the number of center points of the cluster center points. The intersection identification accuracy by means of the method is 94.3%.

Description

technical field [0001] The invention relates to a method for automatically identifying urban intersections based on low-precision spatio-temporal trajectory data, and belongs to the field of geographic information system and intelligent transportation research. Background technique [0002] Urban road intersection is an important part of the urban road system. It is the place where all kinds of traffic on the urban road meet, convert and pass, and is the control point for managing and organizing all kinds of traffic on the road. In the entire road network, the intersection becomes the checkpoint of traffic capacity and traffic safety, and plays a pivotal role in the application of intelligent traffic navigation. The current methods and data sources of urban intersection detection are mainly divided into two categories: identifying intersections from high-resolution remote sensing image data and extracting urban intersection locations from spatiotemporal trajectory data. Ext...

AI Technical Summary

Problems solved by technology

However, the above studies did not conduct an in-depth analysis and identification of the plane structure in the local area of ​​the intersection

At the same time, using the time-space trajectory data with high sampling rate and high positioning accuracy to obtain the location of urban intersections has the characteristics of reliable accuracy, but there are also problems caused by high data acquisition costs, long cycles, and inability to respond to changes in urban construction and lane functions. Limitations of Road Intersection Structural Changes

Compared with the space-time trajectory data with high sampling rate and high positioning accuracy, the low-precision space-time trajectory data from the urban taxi system or other acquisition equipment has low positioning accuracy and low sampling frequency, but its massive information contains rich road information

At present, it is difficult to obtain urban intersection structure information using low-precision spatio-temporal GPS trajectory data, and a large number of researches are still stuck in the extraction of road network maps, and the automatic identification of intersections is still in the research stage

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