The invention discloses a traffic prediction method, belongs to the technical field of traffic flow and trajectory optimization, and particularly relates to a traffic prediction method based on a hybrid LSTM-GCN architecture. A data set is preprocessed, and a solid data foundation is provided for a model through data cleaning, integration and feature engineering; meanwhile, a traffic prediction model based on LSTM and GCN effectively captures the dynamic change and long-term dependence of traffic flow by virtue of the excellent time sequence analysis capability, the GCN model improves the accuracy of traffic flow change prediction by analyzing the topological structure of a road network, and meanwhile, the Dijkstra algorithm is adjusted and optimized by dynamic weight, so that the traffic flow change prediction accuracy is improved. According to the method and the system, path planning can adapt to real-time traffic conditions, the practicability of the model is further improved, the method and the system have important significance in improving the intelligent level of an urban traffic system, and powerful support can be provided for urban traffic management through traffic prediction and path planning.
本发明公开了一种交通预测方法,属于交通流量及其轨迹优化技术领域,具体涉及一种基于混合LSTM‑GCN架构的交通预测方法,本发明对数据集进行了预处理,通过数据清洗、集成和特征工程,为模型提供了坚实的数据基础,同时基于LSTM和GCN的交通预测模型凭借其出色的时间序列分析能力,有效捕捉了交通流量的动态变化和长期依赖性,GCN模型通过分析路网的拓扑结构,提高了交通流量变化预测的准确,同时通过动态权重调整优化了Dijkstra算法,使路径规划能够适应实时交通状况,进一步提高了模型的实用性,本发明提高城市交通系统的智能化水平具有重要意义,通过交通预测和路径规划,可以为城市交通管理提供有力支持。
Traffic prediction method based on mixed LSTM-GCN architecture
基于混合LSTM-GCN架构的交通预测方法
2025-05-13
Patent
Electronic Resource
Chinese
IPC: | G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung |
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