Intelligent transportation system is currently recognized as the best measure to solve the problem of urban traffic congestion in the world. Real-time and accurate traffic flow prediction is one of the key technologies of intelligent transportation system and the premise of realizing intelligent traffic induction and control. This paper first compares the theories and advantages and disadvantages of several important traffic flow prediction models, analyzes the factors affecting the prediction model, and then proposes a traffic combination prediction method based on genetic algorithm. This method uses the characteristics of genetic algorithm group search, combines various algorithms, optimizes the prediction ideas, and fully explores the differences and advantages of different algorithms. Practice has proved that this idea is effective.
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