Real-time traffic state estimation method based on multi-source sensing fusion for vehicle-road coordination

Abstract

The present invention discloses a real-time traffic state estimation method based on multi-source sensing fusion for vehicle-road coordination, including: collecting vehicle and traffic information data using multi-source data acquisition equipment; preprocessing the vehicle and traffic information data by a data preprocessing module to obtain a multi-source dataset; extracting synchronized data sequences from the multi-source dataset; completing data for the synchronized data sequences by a data supplementation module to obtain a continuous data stream; performing feature extraction and classification on the continuous data stream by a state estimation module to obtain an initial traffic state estimation result; optimizing the initial estimation result to obtain a traffic state evaluation result. The present invention, based on the efficient fusion and dynamic prediction capability of multi-source data, significantly improves the accuracy and real-time performance of traffic state prediction.

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