An Traffic Trajectory Data Analysis Method Based on Trajectory Feature Division

doi:10.13474/j.cnki.11-2246.2018.0353

Abstract

Abstract:

Most of the current division methods of traffic trajectory data do not take into account the arbitrary nature of its spatial distribution,combined with the division of a single point,resulting in the analysis is not ideal.In order to solve this problem,a multi-feature trajectory data point combined with data space division method is proposed.The recorded points of the massively-trafficked traffic trajectory are extracted,pre-processed and denoised by α-Shapes method,and the trajectory feature points are calculated.Click the spatial proximity to group,and then according to the location of the Voronoi division.The method overcomes the shortcomings that the division effect is not obvious due to the randomness of spatial distribution when the trajectory data is divided,and effectively improves the trajectory data analysis effect.This method is validated by the taxi data of Zibo city,Shandong province.The results of the distribution heat map after the data are divided show that this method is more effective than the traditional data classification method and also contributes to the de-noising of the trajectory data.

Key words: trajectory data, α-Shapes algorithm, trajectory feature points, voronoi division

CLC Number:

P208

ZHAO Shuxu, QU Ruitao, LIU Changrong. An Traffic Trajectory Data Analysis Method Based on Trajectory Feature Division[J]. 测绘通报, 2018, 0(11): 73-77,82.

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