The invention discloses an intelligent traffic management method for adjustment based on traffic flow, relates to the technical field of traffic management, and adopts YOLOv3 target detection and MaskR-CNN image segmentation to accurately detect and segment various traffic participants such as pedestrians, bicycles and non-motor vehicles in a traffic image. Multi-mode traffic information is provided for a comprehensive traffic system, meanwhile, the change trend of future traffic flow is predicted according to historical traffic flow data based on a time sequence analysis method of an ARIMA model, traffic organization and scheduling arrangement are conveniently made in advance by predicting the traffic flow, traffic congestion and resource waste are avoided, the traffic flow is optimized, and the traffic efficiency is improved. According to the method, a support vector machine (SVM) machine learning algorithm is utilized, historical traffic flow data and other related factors are combined, the future traffic flow is predicted more accurately, interaction between different traffic modes is better coordinated, traffic organization and resource allocation are optimized, and therefore traffic congestion is reduced, and traffic mobility is improved.
本发明公开了一种基于车流量进行调节的智慧交通管理方法,涉及交通管理技术领域,本发明采用YOLOv3目标检测和MaskR‑CNN图像分割对交通图像中的行人、自行车和非机动车多种交通参与者进行精确检测和分割,为综合交通系统提供多模式交通信息,同时基于ARIMA模型的时间序列分析方法根据历史交通流量数据,预测未来交通流量的变化趋势,通过预测交通流量方便提前做好交通组织和调度安排,避免交通拥堵和资源浪费,在交通流量上做出优化,利用支持向量机SVM机器学习算法,结合历史交通流量数据和其他相关因素,更准确地预测未来的交通流量,更好地协调不同交通模式之间的互动,优化交通组织和资源分配,从而减少交通拥堵,改善交通流动性。
Intelligent traffic management method for adjustment based on traffic flow
一种基于车流量进行调节的智慧交通管理方法
2024-01-05
Patent
Electronic Resource
Chinese
IPC: | G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS |
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