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A traffic flow prediction method based on data-driven k-neighborhood nonparametric regression

A technology of non-parametric regression and prediction method, which is applied in the field of intelligent transportation system and Internet of Vehicles. It can solve the problems of long execution time of KNN and low efficiency of KNN prediction method, and achieve the effect of ensuring accuracy, reducing time and improving prediction efficiency

Active Publication Date: 2021-09-03
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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  • A traffic flow prediction method based on data-driven k-neighborhood nonparametric regression
  • A traffic flow prediction method based on data-driven k-neighborhood nonparametric 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 invention discloses a traffic flow forecasting method based on data-driven k-neighborhood non-parametric regression, which is developed based on a two-step data search algorithm. First, within a non-prediction period, candidate input data is searched and identified from a historical database to be compared with the current The state is approximated, 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 utilized to generate a prediction by a prediction algorithm. This algorithm can effectively reduce the time for searching historical data, thereby reducing the execution time in the system forecasting process, improving the forecasting efficiency of the forecasting system, and at the same time ensuring the accuracy of the system's forecasting.

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 Patents(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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