As the usage rate of cars is getting higher and higher, the injuries and losses caused by traffic accidents are also getting bigger and bigger. If some traffic accidents can be predicted, then such losses can be greatly solved. Although there are abundant research results on intelligent transportation, there are not many research results on how to predict traffic accidents. For this issue, the main aim of this paper is to propose a continuous non-convex optimization of the K-means algorithm in order to solve the model problem in the traffic prediction process. First, this paper uses clustering algorithm for feature analysis and big data for the establishment of simulation model in cloud environment. Through this paper an equivalent model, using matrix optimization theory to analyze and process K-means problem, and design efficient and theoretically guaranteed algorithms for big data. By simulating the traffic situation in Shanghai city within three years, the outcomes display that the model endorsed in the given paper can predict traffic accidents at a rate of 93.88% and the accuracy rate of traffic accident processing time is 78%, which fully illustrates the effectiveness of the model established in this paper.

随着汽车的使用率越来越高，交通事故造成的伤害和损失也越来越大。如果可以预见到一些交通事故，那么这种损失就可以大大解决。尽管关于智能交通的研究成果很多，但是关于如何预测交通事故的研究成果却很少。针对这个问题，本文的主要目的是提出一种连续的K-means算法的非凸优化，以解决交通预测过程中的模型问题。首先，本文采用聚类算法进行特征分析，并利用大数据建立云环境下的仿真模型。通过本文的等效模型，使用矩阵优化理论分析和处理K-means问题，并为大数据设计高效且在理论上有保证的算法。通过对上海市三年内的交通状况进行仿真，结果表明，本文所认可的模型可以预测交通事故的发生率为93.88％，交通事故处理时间的准确率为78％，充分说明了本文建立的模型的有效性。