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

Abstract

The invention discloses a machine learning-based vehicle network traffic flow forecasting method, using the traffic speed database released by the traffic data platform, selecting eight types of data indicators, and completing all-weather traffic flow forecasting through the random forest algorithm after optimizing parameters; using openstreetmap exports the vehicle traffic scene of a certain urban road, obtains traffic data, configures the communication simulation file, obtains communication data, mixes the two kinds of data, and analyzes the relationship between traffic flow and communication flow; uses openstreetmap to export the road section selected on the traffic data platform , configure the communication simulation file, obtain communication data, select nine types of related indicators from the traffic speed data and communication data released by the traffic data platform, and use the Bagging model to predict communication traffic. The method of the invention has good generalization performance and high accuracy, and can provide a reliable vehicle communication analysis method for later utilization of economical and efficient data distribution, thereby enhancing the driving safety of vehicle users.

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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