The invention discloses a traffic intersection congestion prediction method based on machine learning, and belongs to the technical field of machine learning. According to the method, the congestion degree of a corresponding traffic intersection is predicted through multiple structural features, the requirement for equipment is low, and the speed is high; meanwhile, the degree of intersection congestion within a long time can be predicted, and an unstable model is adopted as a base learner for training; and finally, the deviation and variance of the model are reduced through integrated learning and model fusion, so that the generalization ability of a prediction result of the model in an actual scene is ensured.
本发明公开了一种基于机器学习的交通路口拥堵的预测方法,属于机器学习技术领域。本发明所述方法通过多种结构性特征预测相应交通路口的拥堵程度,对设备需求低,速度快;同时可以预测长时间内的路口拥堵的程度,采用不稳定模型作为基学习器训练,最后通过集成学习和模型融合减少了模型的偏差和方差,保证了模型在实际场景中的预测结果的泛化能力。
Traffic intersection congestion prediction method based on machine learning
一种基于机器学习的交通路口拥堵的预测方法
2020-07-28
Patent
Elektronische Ressource
Chinesisch
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