Track predication method based on Gauss mixture time series model

A time series model and trajectory prediction technology, which is applied in traffic flow detection, character and pattern recognition, special data processing applications, etc., to reduce time overhead, eliminate cumbersome processes, and ensure dynamic and real-time effects

Active Publication Date: 2018-01-19
HOHAI UNIV
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Problems solved by technology

[0005] Purpose of the invention: In view of the shortcomings of traditional models such as the existing Gaussian mixture model for trajectory prediction under the sudden change of road traffic flow during non-working days, the present invention analyzes the traditional Gaussian mixture model from the aspects of the dynamic historical traj...

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  • Track predication method based on Gauss mixture time series model
  • Track predication method based on Gauss mixture time series model
  • Track predication method based on Gauss mixture time series model

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Embodiment Construction

[0054]Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0055] like Figure 4 As shown, the principle of the trajectory prediction method of the Gaussian mixture time series model is divided into four steps:

[0056] (1) The vehicle-related data collected by GPS is preprocessed by ETL technology to realize the separation of traffic flow data and vehicle historical trajectory data. Traffic flow data is two-dimensional data, including traffic flow and time stamp; historical track data is three-dimensional data, including longitude, latitude and time stam...

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Abstract

The invention discloses a track predication method based on a Gauss mixture time series model (GMTSM for short) to carry out model regression and analysis of road vehicle flow on a large quantity of vehicle historical tracks to realize vehicle track predication. The method mainly comprises the following steps: (1) carrying out unsupervised learning cluster on vehicle historical tracks through a k-means algorithm; (2) constructing a historical track probability distribution model through a Gauss mixture time series model; and (3) predicating tracks of mobile objects through a regressive processof the mixture model. An experiment result shows that the Gauss mixture time series model automatically adjusts the weight of sub-models and selects a predication track with the largest probability in a condition that road vehicle flows change suddenly.

Description

technical field [0001] The invention relates to a vehicle trajectory prediction method based on a Gaussian mixture time series model, which belongs to the technical application field of big data value mining, and mainly uses an intelligent transportation network system. By establishing a Gaussian mixture time series trajectory prediction model, a method for judging by a probability model , realize the optimal selection of user lines, reduce the probability of traffic congestion, and solve the problem of accurate prediction of traffic conditions by the intelligent transportation network system. Background technique [0002] Intelligent transportation system is an intelligent system formed in the process of urbanization, which alleviates urban traffic congestion. However, with the further expansion of urbanization, traffic congestion is still an urgent problem to be solved. To monitor and predict traffic conditions in advance, Recommending reasonable routes is a reasonable sol...

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Application Information

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IPC IPC(8): G08G1/01G06K9/62G06F17/30
Inventor 毛莺池李志涛钟海士平萍戚荣志
Owner HOHAI UNIV
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