The invention discloses an urban road traffic accident black spot prediction method and system based on deep learning and density clustering, and the method comprises the steps: constructing a prediction model based on a CNN-LSTM model, learning the spatial distribution and influence factors of an accident from spatial data through a CNN, and learning the time change trend of the accident from time series data through the sequence modeling capability of LSTM; the long-term dependence and periodicity of the traffic accident are captured, and the accuracy and robustness of prediction are improved. CNN-LSTM is utilized to predict the number of future traffic accidents of urban roads, then clustering is carried out based on density through DBSCAN, areas or road sections with dense accidents are identified, and traffic accident black spots in any shape are found. According to the method, the advantages that mass data can be effectively utilized and the prediction precision is high through deep learning are utilized, and the traffic accident black spots in any shape can be recognized by combining the DBSCAN based density clustering capability.
本发明公开一种基于深度学习和密度聚类的城市道路交通事故黑点预测方法及系统,构建基于CNN‑LSTM模型的预测模型利用CNN从空间数据学习事故的空间分布和影响因素,通过LSTM的序列建模能力从时序数据中学习事故的时间变化趋势,捕捉交通事故的长期依赖和周期性,提高预测的准确性和鲁棒性。利用CNN‑LSTM预测城市道路未来交通事故数量,然后通过DBSCAN基于密度进行聚类,识别事故密集的区域或路段,发现任意形状的交通事故黑点。本发明利用深度学习能有效利用海量数据且预测精度高的优点,再结合DBSCAN基于密度聚类的能力,能够识别任意形状的交通事故黑点。
Urban road traffic accident black spot prediction method and system based on deep learning and density clustering
基于深度学习和密度聚类的城市道路交通事故黑点预测方法及系统
2024-05-03
Patent
Electronic Resource
Chinese
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