CAJ | 학술논문

交通事件的检测与确认是交通事件管理中的首要问题。基于线圈和视频数据的检测方法由于成本高，检测效果不明显，在实际应用中受到限制。提出了一种基于离群点挖掘的交通事件检测算法。该算法通过使用浮动车（floating car data，FCD）技术得到路况信息，并提取交通事件特征，建立特征向量。算法简单、高效、易于部署。实验结果表明，同模式识别方法相比，该算法具有较高的准确度，能有效区分常规拥堵与交通事件。
교통사건적검측여학인시교통사건관리중적수요문제。기우선권화시빈수거적검측방법유우성본고，검측효과불명현，재실제응용중수도한제。제출료일충기우리군점알굴적교통사건검측산법。해산법통과사용부동차（floating car data，FCD）기술득도로황신식，병제취교통사건특정，건립특정향량。산법간단、고효、역우부서。실험결과표명，동모식식별방법상비，해산법구유교고적준학도，능유효구분상규옹도여교통사건。
Traffic incident detection and confirmation is the primary problem in traffic incident management, but detection method based on loop detector and video data is restricted in practical applications due to the high cost and inefficiency. This paper proposes a traffic incident detection algorithm based on outlier mining by use of feature vector related to traffic incident. The feature vector is obtained from the traffic information processed by the float car technology. The algorithm is simple, efficient, and easy to deploy. The experimental results show that the algorithm has higher accuracy than the pattern recognition method, and can effectively distinguish between the conventional congestion and traffic incident.