Traffic road condition prediction method based on car-networking big data

Abstract

The invention relates to a function-type non-parameter model and a KNN estimation method adopting the model. Traffic road condition prediction is effective application of car-networking big data, is conductive to urban traffic management and provides effective references for vehicle owners to select driving routes. Non-parametric regression serves as a parameter-free and high-precision algorithm,a prediction effect is more superior to parametric regression, and errors are smaller. In addition, the speed value in a certain period of time is regarded as a continuous function curve and is analyzed from the perspective of function-type data. A K-neighbor estimation method is adopted, and road flow can be predicted in real time only by determining parameters such as optimum window width.

Description

technical field [0001] The invention belongs to the technical field of intelligent network connection, and in particular refers to a traffic road condition prediction method based on the big data of the Internet of Vehicles. Background technique [0002] The Internet of Vehicles is an important intersection of the two major fields of the Internet of Things and intelligent vehicles in strategic emerging industries, and is a key component of urban smart transportation. The concept of the Internet of Vehicles originates from the Internet of Things. It uses technologies such as sensors, communication networks, and system integration to realize network interconnection and information exchange between people and vehicles, vehicles and vehicles, and vehicles and roads. In order to achieve intelligent traffic management, the Internet of Vehicles embodies the macro concept of Internet of Things technology in vehicles and modern transportation. Big data analysis refers to the analysi...

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a method for predicting traffic road conditions based on the big data of the Internet of Vehicles and using a non-parametric regression algorithm to solve the problems in the prior art that the short-term traffic flow prediction is affected by many factors and the prediction accuracy is poor

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