Aggregation mode discovery method and system in road network environment

Abstract

The invention provides an aggregation mode discovery method and a system in a road network environment. According to the method, the aggregation mode of motion is discovered by employing motion trackdata of moving objects including vehicles and pedestrians in the road network environment. The method includes: step 1, simplifying tracks, and obtaining similar track segments through the track structure similarity; step 2, comparing characteristics including motion directions and motion speeds of the tracks and recognizing the similar motion tracks through extraction of structural characteristics of the tracks; step 3, clustering the similar track segments through distances, and finding out candidate aggregation mode track sets satisfying a clustering requirement; and step 4, screening a track set in accordance with a space-time condition by employing time clustering, and obtaining an aggregation mode track set. The system comprises three modules including a data preprocessing module, anaggregation mode discovery module and a visualization data analysis display module.

Description

technical field [0001] The invention belongs to the field of spatio-temporal data mining, in particular to a method and system for discovering aggregation patterns in a road network environment. Background technique [0002] In recent years, the rapid development and wide application of technologies such as GPS devices, RFID sensors, and wireless networks have enabled moving objects to be effectively tracked, resulting in massive location data. These location data contain a lot of information, and researchers need to analyze them effectively. Therefore, the research direction of moving object data mining came into being. At present, scholars at home and abroad have conducted a lot of research and analysis on a series of tracking data such as trajectory data and traffic trajectory data of moving objects. But with the deepening of the research, the demand is also increasing, and the problems will also increase. The data mining of moving objects has become one of the most imp...

AI Technical Summary

Problems solved by technology

Since the moving object is an independent individual, the trajectory of the moving object is unrestricted, so it is sensitive to the fixed area size, which makes the Flock mode prone to lossy-flock phenomenon

figure 1 In, O 1 , O 2 , O 3 , O 4 for 4 moving objects, figure 1 On the left is the trajectory of a group of objects moving together, and from figure 1 It can be seen on the right that although O 4 The motion trajectory of is very similar to the other three trajectories, but O 4 Did not move within a fixed circular area and was not detected, resulting in errors in the results

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