Markov-model-based position prediction method

Abstract

The invention discloses a markov-model-based position prediction method. The method comprises: a historical track is collected, data centralization probabilities of all sampling positions are determined, normalization processing is carried out, discrete probability distribution is determined, and a variable-order global markov model is established; according to a historical track of each moving object, an individual markov model of each moving object is constructed; and on the basis of linear regression, the global markov model and the individual markov models are combined to generate a probability vector linear combination, time period division is carried out, all tracks are mapped to the time periods according time stamps, probabilities of falling into all time periods by all objects are calculated, clustering is carried out, and then a next position is predicted by combining a clustering result and the markov models. According to the invention, the time factor is taken into consideration and different models are trained at different time periods. When a next position is predicted, a proper model is selected based on the time stamp, so that the prediction accuracy is improved substantially.

Description

technical field [0001] The invention relates to a position prediction method based on a Markov model. Background technique [0002] The popularity of location technology has made it possible to track the movement of people and other objects, thus giving rise to a variety of location-based applications. For example, GPS tracking using vehicle-mounted positioning devices has become the method of choice for taxi fleet management. In many social networking applications, such as Foursquare, users are encouraged to share their geographic location with other users. In addition, in a growing number of cities, vehicles are photographed when they pass by surveillance cameras installed on highways and streets, and vehicle traffic records including license plate numbers, time and location are transmitted to data centers for storage and further processing. [0003] In these location-based applications, it is very important to be able to accurately predict the next location of a moving ...

AI Technical Summary

Problems solved by technology

[0006] 2. In some cases (such as social login and vehicle monitoring), the data points are very sparse, and the trajectory of some objects may contain only one record, and meaningful frequent patterns cannot be constructed using these trajectories

[0007] 3. Existing methods do not properly consider the time factor

However, if it's 11:30am, he's more likely to go to a restaurant; if it's 3pm on a weekend, he's more likely to go shopping

Failure to properly account for time when predicting the next position will result in a higher error rate

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