Research road safety prediction problem. Due to the stochastic volatility, the prediction accuracy of larger sample data sequence is poor, and the traditional road traffic safety prediction based on grey prediction theory algorithm can not solve the problems of traffic congestion and traffic imbalance. In order to solve the above problems, the paper put forward a safety prediction method of traffic condition monitoring base on Internet of things technology, which mainly used the Internet of things technology to monitor the road traffic situation in real-time. Based on the collected traffic information, the early security warning index was designed, and a road traffic safety prediction model was built based on gray theory. Based on the model, a two-dimensional Marco chain model was introduced to estab lish a new two-dimensional Marco grey extension theory prediction model of traffic safety. The simulation results show that the new prediction method can effectively predict the road traffic safety risk and hazard, and the prediction preci sion is higher than the traditional grey model, which provides the basis for traffic safety system design.%研究道路安全预测精确度问题,由于随机波动性较大的样本数据序列预测精度较差,传统的道路交通安全预测灰色预测理论算法难以解决交通拥塞和道路交通不平衡状况.为了解决上述问题,提出了一种基于物联网技术的交通状态监测安全预测方法,主要采用物联网技术实时监测道路的交通情况,依据搜集的交通信息设计安全预警指标,建立灰色理论道路交通安全预测模型,并在模型的基础上引入二维马尔科夫链时空模型,建立一种新的二维马尔科夫理论的灰色扩展交通安全预测模型.仿真结果表明,提出的新的预测方法能够有效地预测道路交通安全风险以及隐患,预测的精确度要比传统的灰色理论预测模型更高,为交通安全系统设计提供了依据.
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