The invention relates to the field of intelligent traffic, in particular to a clustering-based real-time traffic jam prediction method and equipment. According to the method, the congestion degree is taken as an index, the clustering method is adopted to classify the road network, the spatial and temporal characteristic analysis is performed on the key congestion road section, the congestion rule is excavated, and the incidence relation of the road network congestion in time and space, the congestion characteristic and the congestion probability are obtained, so that the key congestion road section is accurately identified and predicted. Therefore, a traffic management department can formulate more reasonable traffic management strategies and measures, actual requirements are better met, the road traffic condition is improved, and the travel experience of people is improved.
本发明涉及智能交通领域,特别是一种基于聚类的实时交通拥堵预测方法及设备。本发明以拥堵程度为指标,采用聚类方法将路网进行分类,对关键拥堵路段进行时空特征分析,挖掘拥堵规律,得出路网拥堵在时间和空间上的关联关系和拥堵特征以及拥堵概率,从而达到准确识别和预测关键拥堵路段。以便于交通管理部门制定更加合理的交通管理策略和措施,更好地满足实际需求,改善道路交通状况,提高人们的出行体验。
Clustering-based real-time traffic jam prediction method and equipment
一种基于聚类的实时交通拥堵预测方法及设备
2024-06-07
Patent
Electronic Resource
Chinese
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