Traffic flow prediction method based on data-driven k-nearest non-parametric regression

A non-parametric regression and data-driven technology, which is applied in the field of intelligent transportation system and Internet of Vehicles, can solve the problems of low efficiency of KNN prediction method and long execution time of KNN, so as to reduce the time of searching historical data, ensure the accuracy rate, and reduce the execution time. the effect of time

Active Publication Date: 2019-04-09
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0003] Even so, the disadvantage of data-driven—the long execution time of KNN in a time-based system such as a dynamic intelligent transportation system, leads to the low efficiency of the KNN prediction method
Therefore, a high-speed model framework for the KNN algorithm is necessary, which represents a challenge

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  • Traffic flow prediction method based on data-driven k-nearest non-parametric regression

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[0031] Below in conjunction with accompanying drawing and specific embodiment, further illustrate the present invention, should be understood that these examples are only for illustrating 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 aspects of the present invention All modifications of the valence form fall within the scope defined by the appended claims of the present application.

[0032] A data-driven k-neighborhood non-parametric regression based traffic flow forecasting method, developed based on a two-step data search algorithm, first seeks and identifies candidate input data from historical databases to approximate the current state during the non-prediction period, and then The best decision input data for prediction is identified from the candidate input data at the prediction point, and finally the best decision input data is used to gen...

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Abstract

The present invention discloses a traffic flow prediction method based on data-driven k-nearest non-parametric regression. The method is characterized in that, based on the development of a two-step data search algorithm, firstly, in a non-predictive time period, candidate input data is searched and identified from a historical database to be approximated with the current state; optimal decision input data for prediction is identified from the candidate input data at the prediction point; and finally the prediction is generated through the prediction algorithm by using the optimal decision input data. According to the algorithm provided by the present invention, the time for searching historical data can be effectively reduced, the execution time in the system prediction process can be reduced, the prediction efficiency of the prediction system can be improved, and the accuracy for the system prediction can be ensured.

Description

technical field [0001] The invention is applied to urban short-term traffic flow prediction, relates to the practical application of research on intelligent traffic systems (ITS) and traffic flow prediction models, and belongs to the field of intelligent traffic systems and Internet of Vehicles. Background technique [0002] The evolution of time-series traffic flow states is usually a chaotic system, where the temporal development of the state determines the given initial conditions. The KNN method basically relies on a large amount of information contained in historical data to determine the input and output, so there are no statistical assumptions, nor is the formula artificially speculated. Due to its theoretical and practical advantages, KNN has become a promising prediction model in the field of intelligent transportation, and prediction methods based on KNN methods are at least comparable to the performance of parametric / or nonlinear models in terms of prediction reli...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G08G1/01
CPCG08G1/0129G08G1/0133
Inventor 张登银陈肯丁飞严嘉赟
Owner NANJING UNIV OF POSTS & TELECOMM
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