The invention belongs to the field of intelligent traffic, and discloses a highway abnormal event type identification and position estimation method, which adopts a BERT and LSTM model combined method, can accurately identify different types of traffic abnormal events, and improves the accuracy of event type identification. And secondly, by using a CNN-GRU model and a K-Means clustering algorithm, the travel time and delay time of the vehicle can be accurately predicted, so that the initial position of the traffic jam can be accurately estimated. By comprehensively considering topographic factors and traffic flow characteristics, the influence of different event types on traffic flow is comprehensively analyzed, and the applicability and reliability of the model are improved. In addition, through real-time data analysis and dynamic adjustment, adaptation of the model to complex traffic situations such as sudden large traffic flow is enhanced. In conclusion, the safety and efficiency of traffic in the highway tunnel can be improved.
本发明属于智能交通领域,公开了一种高速公路异常事件类型识别与位置估计方法,采用BERT和LSTM模型结合的方法,能够准确识别不同类型的交通异常事件,提升事件类型识别的准确性。其次,利用CNN‑GRU模型和K‑Means聚类算法,能够精确预测车辆行程时间和延误时间,从而精确估计交通拥堵的初发位置。通过综合考虑地形因素和交通流特征,全面分析不同事件类型对交通流的影响,提高模型的适用性和可靠性。此外,通过实时数据分析和动态调整,增强了模型对突发大车流等复杂交通情境的适应。综上,本发明能够使高速公路隧道内交通的安全和效率方面都获得提高。
Expressway abnormal event type identification and position estimation method
一种高速公路异常事件类型识别与位置估计方法
2024-10-25
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 / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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