The invention relates to a road traffic safety risk early warning method based on machine learning, and relates to the field of road traffic safety risk management and control. The method comprises the following steps: acquiring target road data, wherein the target road data is associated with the state of a target road; and inputting the target highway data into a highway traffic safety risk early warning model, and outputting a target highway safety risk assessment result corresponding to the target highway, the target highway safety risk assessment result comprising a target highway segmentation result and a risk assessment score result corresponding to the target highway segmentation result. A traffic safety risk early warning model obtained through traffic accident data training is used as a risk prediction tool, and deep feature mining and information arrangement are performed on target road data to obtain a safety risk assessment result corresponding to a target road, so that road traffic accidents in a region are predicted, and the risk prediction efficiency is improved. And risk early warning of highway safety in a period is realized.
本申请关于基于机器学习的公路交通安全风险预警方法,涉及公路交通安全风险管控领域。该方法包括:获取目标公路数据,所述目标公路数据与目标公路的状态关联;将所述目标公路数据输入公路交通安全风险预警模型,输出得到与所述目标公路对应的目标公路安全风险评估结果,所述目标公路安全风险评估结果包括目标公路分段结果,以及与所述目标公路分段结果对应的风险评估分值结果。通过由交通事故数据训练得到的交通安全风险预警模型作为风险预测的工具,对目标公路数据进行深度的特征挖掘以及信息整理,以得到与目标公路对应的安全风险评估结果,从而实现对于区域内公路交通事故进行预测,并对周期内公路安全实现风险预警。
Road traffic safety risk early warning method based on machine learning
基于机器学习的公路交通安全风险预警方法
2024-09-24
Patent
Electronic Resource
Chinese
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