Crowd movement flow drawing method based on sparse trajectory data

Abstract

The invention provides a crowd movement flow drawing method based on sparse trajectory data. The method comprises the following steps: step 1, extracting a sparse track by using a movement extractionmethod, performing sorting according to time, connecting records before and after the current position of a track sequence, taking every two adjacent records as a group, and taking the space coordinates of the previous record as a starting point and the space coordinates of the later record as an end point in each group to obtain a local movement vector; step 2, performing space-time division on the motion vector in the step 1; 3, smoothing the motion vector in the step 2 by using a kernel density estimation method, and then performing aggregating to obtain a motion vector cluster in a local space; step 4, processing the motion vector in the step 2; step 5, clustering the motion vectors in the step 3 to obtain a significant global motion flow; and step 6, circularly displaying the global movement flow in the step 5, and displaying the abnormal value and the crowd distribution attribute obtained in the step 4.

Description

technical field [0001] The invention relates to a method for drawing crowd movement flow, in particular to a method for drawing crowd movement flow based on sparse trajectory data. Background technique [0002] In recent years, with the continuous development of GPS (Global Positioning System) positioning technology, sensors and other sensing technologies, and social media, a large amount of data carrying geographic location information has been generated in cities, which makes the display and mapping of crowd movement a recent research hotspot. and has been widely used. [0003] The study of crowd movement in cities has important practical significance and application value, such as providing support for traffic optimization, urban planning, safety management, etc., and providing data-based reference information for decision-making tasks such as commercial location selection. [0004] Most of the existing research methods are aimed at dense trajectories with relatively sma...

AI Technical Summary

Problems solved by technology

This method mainly studies the traffic congestion at traffic units, ignoring the overall situation of movement in the city; and the visualized results are related to the number of traffic units that exist. If the number of traffic units is large, it is easy to cause occlusion and clutter. The small number of traffic units will affect the observation and analysis of traffic congestion in the city

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