Method for identifying vehicles from GPS (global positioning system) data

Abstract

The invention discloses a method for identifying vehicles from GPS (global positioning system) data. The method includes steps of preliminarily labeling GPS data points; acquiring candidate transfer stations, and in other words, adding potential transfer stations into sets of the candidate transfer stations; acquiring true transfer stations and sectioning tracks; extracting characteristic acceleration change rates ACR; extracting characteristic time slice types TS and dividing user working peak hours into two types T_busy and T_idle; extracting 85% division speed and acceleration characteristics of road sections; extracting characteristics from the various road sections, inputting the characteristics into classification model random forest classifiers and outputting transport modes to obtain prediction results of the transport modes. The transport modes are output results. The method has the advantages that the effective characteristics are extracted according to laws presented by existing traffic, and the vehicle identification rate further can be increased by the aid of post-processing algorithms on the basis of ideology of track integrity.

Description

technical field [0001] The invention relates to the fields of data mining and urban calculation, in particular to a method for identifying vehicles from GPS data. Background technique [0002] Urban traffic is becoming more and more congested, and people's activities are becoming more complex and intensive. User behavior extraction, trajectory analysis, and traffic pattern recognition are playing increasingly important roles for service providers and decision makers. Generally, urban traffic modes are divided into road traffic modes (such as self-driving cars, buses, bicycles, and walking) and rail traffic modes (such as subways and trains). For researchers, it is easy to distinguish the road traffic mode from the rail traffic mode by the magnitude distribution of the speed. [0003] In the past few years, researchers have collected data on traffic patterns through questionnaires or telephone interviews, which often produce inaccurate or incomplete data due to the negligen...

AI Technical Summary

Problems solved by technology

In the fourth step, Jun Zhicai and others adopted the Bayesian network model, and did not adopt post-processing methods.

First of all, the life of modern people shows more characteristics, and the effect of traditional characteristics is less obvious

In addition, the accuracy and recall rate of exist

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