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Geographical location prediction method based on continuous time sequence Markov model

A Markov model and geographic location technology, which can be used in location-based services, geographic information databases, and electrical digital data processing, and can solve problems such as rough prediction results.

Active Publication Date: 2017-08-04
BEIJING UNIV OF TECH
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Problems solved by technology

[0003] At present, when using the Markov model to predict the position in real time, it is necessary to determine the time point of position transfer by dividing the time into equivalent values, which leads to the problem of rough prediction results.

Method used

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  • Geographical location prediction method based on continuous time sequence Markov model
  • Geographical location prediction method based on continuous time sequence Markov model
  • Geographical location prediction method based on continuous time sequence Markov model

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Embodiment 1

[0076] The specific implementation steps of the present invention are divided into two parts, the first part is data preprocessing, and the second part is model training and prediction.

[0077] For better explanation later, the symbol table is defined as follows:

[0078]

[0079]

[0080] For the data preprocessing part, proceed as follows:

[0081] Step 1: Take all trajectories, first for any trajectory point in a certain trajectory If the distance between adjacent track points greater than a certain threshold, from the trajectory delete track point In the present invention, the threshold is set to be 100m.

[0082] Step 2: For the remaining trajectories and trajectory points after filtering in step 1, calculate the velocity v of each trajectory point j , if the velocity v of a trajectory point j is greater than a certain threshold η, delete the track middle Three track points.

[0083] Among them, η represents the moving speed of the user, and the valu...

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Abstract

The invention discloses a geographical location prediction method based on a continuous time sequence Markov model. The method comprises the following steps: step 1, filtering and clustering the original user trajectory data to generate a series of candidate locations; step 2, converting the user trajectory data into a [time T, location L] sequence according to the information of the candidate locations; step 3, implementing Gaussian mixture modelling on the sequence density of each location, and improving an original Markov model in combination with a transition probability matrix, a sequence point probability and other information to establish a Markov model based on a continuous time sequence; and step 4, predicting the geographical location of a target time point by using the Markov model based on the continuous time sequence. By adopting the technical scheme disclosed by the invention, the accuracy of prediction can be improved.

Description

technical field [0001] The invention belongs to the field of data mining based on geographic location services, and in particular relates to a geographic location prediction method based on a continuous time series Markov model. Background technique [0002] With the advancement of the trend of Internet mobility today, location-based services such as navigation and traffic management are developing rapidly. In order to provide a better service experience, more and more location-based service systems need to predict the user's location in advance. For example: personalized location guidance, location-based reminder service, location-based advertisement delivery, etc. Assuming that the user is at location A at 5:00 p.m., if we can predict that the user is at location B at 8:00 p.m., then the location-based service provider can provide the user with advertising information related to location B in advance. It can be seen that location prediction technology It has high practic...

Claims

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Application Information

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IPC IPC(8): H04W4/02H04W64/00G06F17/30
CPCG06F16/29G06F16/9537H04W4/025H04W4/029H04W64/006
Inventor 杜永萍王辰成乔岩磊
Owner BEIJING UNIV OF TECH
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