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Travel trajectory clustering method, apparatus and device

A technology of travel trajectory and clustering method, applied in the field of big data, can solve problems such as large amount of calculation and low efficiency

Active Publication Date: 2017-09-01
NEUSOFT CORP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] This clustering method in the prior art has a large amount of calculation and low efficiency, especially when the number of sampling points in the travel trajectory is large

Method used

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  • Travel trajectory clustering method, apparatus and device
  • Travel trajectory clustering method, apparatus and device
  • Travel trajectory clustering method, apparatus and device

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0115] see figure 1 , which is a flowchart of a travel trajectory clustering method provided in Embodiment 1 of the present application.

[0116] The travel trajectory clustering method provided in this embodiment includes the following steps:

[0117] Step S101: Obtain multiple travel trajectories of the user.

[0118] In this embodiment, a track is a collection of location information of a user within a period of time (for example, one day). Trajectories include travel trajectories. Travel trajectories can be defined as a section of trajectory that the user continues to travel. If the user stays in an area for a long time, although there are also trajectories formed by the collection of location information during this period of time, this section of trajectory is not Travel trajectory. The travel trajectory in this embodiment includes a starting point, an ending point and an intermediate point. The intermediate point refers to a point between the starting point and the ...

Embodiment 2

[0144] see figure 2 , which is a flowchart of a travel trajectory clustering method provided in Embodiment 2 of the present application.

[0145] The travel trajectory clustering method provided in this embodiment includes the following steps:

[0146] Step S201: Obtain multiple travel trajectories of the user.

[0147] The multiple travel trajectories each include a starting point, an ending point, and an intermediate point between them.

[0148] Step S202: Using the start points and / or end points of the multiple travel trajectories to cluster the multiple travel trajectories to obtain a first set of travel trajectories.

[0149] The first set of travel trajectories includes travel trajectories with matching start points and / or end points, and the number of travel trajectories in the first set of travel trajectories is greater than or equal to a first threshold.

[0150] Step S203: Determine a third set of travel trajectories formed by travel trajectories with the same nu...

Embodiment 3

[0155] Clustering travel trajectories by using the start and end points of multiple travel trajectories is essentially a clustering of points. This embodiment provides a method for clustering travel trajectories by using start points and end points of multiple travel trajectories.

[0156] Firstly, the method of clustering using the starting point is introduced. see image 3 , the method includes the following steps:

[0157] Step S301: Select the first travel trajectory from the travel trajectories that are not clustered by the starting point.

[0158] Step S302: From the travel trajectories that are not clustered by the starting point except the first travel trajectory, determine that the distance between the starting point and the starting point of the first travel trajectory is less than or equal to the first domain The travel trajectories of the radius form the sixth travel trajectory set.

[0159] Step S303: clustering the sixth set of travel trajectories and the fir...

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Abstract

Embodiments of the invention disclose a travel trajectory clustering method, apparatus and device. The calculation amount of travel trajectory clustering is reduced and the travel trajectory clustering efficiency is improved. The method comprises the steps of obtaining multiple travel trajectories of a user, wherein each travel trajectory comprises a starting point, an ending point and a middle point located between the starting point and the ending point; by utilizing the starting points and / or the ending points of the travel trajectories, clustering the travel trajectories to obtain a first travel trajectory set, wherein the first travel trajectory set comprises the travel trajectories with the matched starting points and / or ending points, and the number of the travel trajectories in the first travel trajectory set is greater than or equal to a first threshold; and by utilizing the middle points in the travel trajectories, clustering the travel trajectories in the first travel trajectory set to obtain a second travel trajectory set, wherein the second travel trajectory set comprises the travel trajectories with the matched starting points and middle points, and / or, the travel trajectories with the matched ending points and middle points.

Description

technical field [0001] The present application relates to the field of big data, in particular to a travel trajectory clustering method, device and equipment. Background technique [0002] The user's travel trajectory is an important part of user behavior. By analyzing the user's travel trajectory, a lot of important information can be obtained. The user's travel trajectory is usually obtained according to the user's location information (such as GPS information) within a period of time. By analyzing the location information, the user's frequent route can be obtained, so as to push the user's road conditions about the route in advance to help Users can avoid the risk of congestion, or they can push the surrounding food, business information, etc. of the routes they often take. In addition, it can also provide services such as friend recommendation and push carpooling for users with the same route. Then it involves how to find the same travel trajectory from a large number ...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/29G06F16/35
Inventor 徐丽丽高睿
Owner NEUSOFT CORP
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