Regional Traffic Flow Prediction System and Method for Internet of Vehicles

Abstract

The invention discloses a regional traffic flow prediction system and method oriented to the Internet of Vehicles. The system includes: an external influence data module (1), a vehicle networking data module (2), a data processing module (3) and a support vector regression machine module (4). The data processing module (3) uses the data of the external influence data module (1) and the Internet of Vehicles data module (2) to generate row vector data including weather, holidays, date and time; the support vector regression machine module (4) uses these data to train Learn the prediction model, and use the prediction model to combine the row vector data of the next cycle time to complete the prediction of the regional traffic flow at the next cycle time. The invention comprehensively considers the influence of weather, holidays, date and time on the regional traffic flow, and can effectively predict the regional traffic flow. It can be used to guide traffic and allocate Internet of Vehicles resources, improve the ability of traffic control and the utilization efficiency of Internet of Vehicles resources.

Description

technical field [0001] The invention belongs to the technical field of traffic forecasting, and in particular relates to a regional traffic flow forecasting method, which can be used for traffic control and vehicle network resource allocation. Background technique [0002] The Internet of Vehicles (IoV) is based on the vehicle intranet, vehicle-mounted mobile Internet and the vehicle-to-vehicle network, and communicates between vehicles and vehicles, vehicles and roads, vehicles and pedestrians, vehicles and the Internet, and vehicles and the cloud through unified communication protocols and data. The large-scale system network for wireless communication and information interaction based on interactive standards is an integrated network capable of intelligent traffic management, intelligent dynamic information services, and intelligent vehicle control. The network completes the collection of its own environment and state information through GPS, RFID, sensors, camera image p...

AI Technical Summary

Problems solved by technology

However, in the real traffic environment, due to the movement of vehicles, at the same period of time, some areas will have higher traffic flow, while some areas will have lower traffic flow. The unbalanced traffic flow in these areas will affect traffic control and vehicle control. Serious impact on network resource utilization efficiency

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