The invention belongs to the technical field of traffic, and particularly relates to a long-term and short-term traffic flow prediction model and method. When the external environment influence data containing a randomness error is used for predicting the future traffic flow, the randomness error is amplified again, and inevitably, the accuracy of model prediction is greatly influenced and fluctuated. The invention provides a long-term and short-term traffic flow prediction model. The model comprises a context factor input layer, a feature learning and pattern recognition layer and a traffic flow data output layer. The multi-scale prediction model provided by the invention is high in accuracy, good in prediction effect, short in training time and strong in robustness, and is not influencedby historical data missing. Contextual factors are used as prediction input, the accuracy of the traffic flow prediction model is improved, and the model plays a crucial role in advanced traffic management and traveler route planning.
本申请属于交通技术领域,特别是涉及一种长短期交通流预测模型及方法。含有随机性误差的外部环境影响数据去预测未来的交通流,只会使得这种随机性误差被再次放大,不可避免地会对模型预测的准确性产生较大的影响和波动。本申请提供了一种长短期交通流预测模型,所述模型包括语境因素输入层、特征学习与模式识别层和交通流数据输出层。提供了一种准确率高,预测效果好,训练时间短,鲁棒性强,不受历史数据缺失影响的多尺度预测模型。使用语境因素作为预测输入,提高了交通流预测模型的准确率,对于先进的交通管理和旅行者路线规划起着至关重要的作用。
Long-term and short-term traffic flow prediction model and method
一种长短期交通流预测模型及方法
2020-08-11
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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