The invention relates to a traffic jam prediction method based on deep learning, and the method comprises the following steps: S1, collecting traffic data, and collecting related traffic data through a traffic sensor, a camera, and a vehicle-mounted GPS; s2, preprocessing the collected original traffic data; s3, constructing a neural network model by using a deep learning technology, and learning and predicting the traffic data; s4, model training: training a neural network model by using the collected data set, and optimizing parameter setting to achieve an accurate prediction result; and S5, performing real-time traffic jam prediction, and predicting a future traffic state by using the trained neural network model. The invention relates to the technical field of traffic jam prediction. The method constructs the neural network model based on the deep learning technology, is high in prediction precision, carries out the rapid prediction of the traffic state through a small number of data sets, is short in prediction time, can be flexibly suitable for different traffic environments, and is higher in adaptability.
本发明涉及一种基于深度学习的交通拥堵预测方法,包括以下步骤:S1、采集交通数据,利用交通传感器、摄像头、车载GPS来收集相关交通数据;S2、对收集到的原始交通数据进行预处理;S3、利用深度学习技术构建神经网络模型,对交通数据进行学习和预测;S4、模型训练,用已采集到的数据集来训练神经网络模型,以及优化参数的设置,达到准确的预测结果;S5、实时交通拥堵预测,用已训练好的神经网络模型预测未来的交通状态。本发明涉及交通拥堵预测的技术领域。本发明基于深度学习技术构建神经网络模型,预测精度高,通过较少的数据集对交通状态进行迅速的预测,预测时间短,且能够灵活的适用于不同的交通环境,适应性更强。
Traffic jam prediction method based on deep learning
一种基于深度学习的交通拥堵预测方法
2024-02-13
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 / G06K Erkennen von Daten , RECOGNITION OF DATA / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G06V |
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