Preprocessing method of mobile phone signaling trajectory based on clustering outlier analysis

Abstract

The invention discloses a preprocessing method of a mobile phone signaling trajectory based on clustering outlier analysis, which reasonably and effectively cleans and denoises base station location data to remove abnormal data, so as to acquire relatively accurate trajectory data on the premise of ensuring original motion characteristics of the data. The method comprises the following steps: on the basis of removing a time repeated sampling value and an invalid sampling value of the trajectory data, performing sub-trajectory segmentation by recognizing the abnormal sampling interval; using an outlier detection algorithm to detect outliers, and dealing with larger system noises; and using Kalman filtering to deal with inherent noises, so that the trajectory data which can perform subsequent mining processing can be obtained. Compared with the prior art, the preprocessing method of the mobile phone signaling trajectory provided by the invention has the following beneficial effects: reasonable and effective multi-level pretreatment, cleaning, denoising and anomalies removal are performed on the base station location data, so that relatively accurate trajectory data can be obtained on the premise of ensuring the original motion characteristics of the data.

Description

technical field [0001] The invention belongs to the technical field of data mining, and relates to a method for preprocessing mobile phone signaling traces based on clustering and outlier analysis. Background technique [0002] In the past few decades, the rapid development of mobile communication technology has led to the emergence of many emerging technologies. The signaling data of cellular mobile phones has the characteristics of low acquisition cost, large data volume, and wide coverage, making it a very valuable data for research in the field of urban transportation applications. Through the mobile trajectory information obtained by mobile phone positioning technology, we can estimate the travel speed, traffic flow, congestion and other traffic parameters of each road in the urban road network, and at the same time identify the travel mode that the user may use. Based on the travel division Further identify the main travel mode of the user. We can even get more valua...

AI Technical Summary

Problems solved by technology

[0021] Abnormal data is called outliers. There are usually two ways of thinking when processing trajectory data. One is to detect outlier trajectory data in a large number of trajectory data and remove the outlier trajectory data to achieve the effect of noise reduction. This method It is more complicated to implement, and may misjudgment and waste valid trajectory data; the other is to detect outlier positioning points in each trajectory, and remove the outlier positioning points to achieve the effect of noise reduction

Traditional outlier analysis methods such as K-means and DB-Scan have the disadvantages of high complexity, the need to specify the number of clusters in advance, and it is difficult to distinguish discrete trajectory points from real outliers (clusters)

[0022] The above several preprocessing methods are often used alone, so they cannot filter out noise well, and have obvious application limitations.

Images