Traffic prediction method for Internet of Vehicles communication based on machine learning

Abstract

The invention discloses a traffic prediction method for Internet of Vehicles communication based on machine learning, and the method comprises the steps: selecting eight types of data indexes by utilizing a traffic speed database issued by a traffic data platform, and completing the all-weather traffic flow prediction through a random forest algorithm after parameter optimization; exporting an urban road vehicle traffic scene in a certain place by utilizing the openstreetmap, obtaining traffic data, configuring a communication simulation file, obtaining communication data, mixing the two kinds of data, and analyzing the relationship between the traffic flow and the communication flow; exporting a road section selected by a traffic data platform by utilizing openstreetmap, configuring a communication simulation file, acquiring communication data, selecting nine types of related indexes from flow speed data and communication data released by the traffic data platform, and performing communication flow prediction through a Bagging model. The method is good in generalization performance and high in accuracy, a reliable vehicle-mounted communication analysis method can be provided for later-stage utilization of economic and efficient data distribution, and the driving safety of vehicle users is enhanced.

Description

technical field [0001] The invention relates to the technical field of vehicle flow forecasting in urban road vehicle traffic scenarios, in particular to a method for predicting existing traffic data by using machine learning algorithms, and combining communication simulation to complete vehicle network communication flow forecasting method. Background technique [0002] Vehicular ad hoc network is a revolutionary development of new generation information technology relying on computer network, modern wireless communication and cloud computing, and it was developed to provide reliable in-vehicle communication through cost-effective data distribution. Vehicle communication can be used to reduce traffic accidents, traffic congestion, travel time, fuel consumption, etc. In-vehicle communication allows road users to be aware of their surroundings in the event of critical and dangerous situations that may occur to them by exchanging information. Therefore, the research on the co...

AI Technical Summary

Problems solved by technology

For example, the two-step optimal selection method is a statistical method for predicting time series, but it can only detect and count based on a single time series, and it is relatively difficult for multiple time series curves

Another combined method combining wave theory analysis and spectrum analysis is to divide traffic data into three types of components according to spectrum analysis, and different traffic components are predicted by corresponding models, but it is dealing with huge traffic flow and network communication flow data It is not only time-consuming and labor-intensive, but also the distribution characteristics of the data indicators are not well displayed

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