CAJ | 학술논문

高速公路事件检测是交通信息工程及控制学科中的一项重要研究课题，以高速公路交通流的特点为研究对象，提出了一种基于支持向量机（SVM ）的高速公路事件检测算法。根据支持向量机的基本原理，分别设计了基于线性不可分SVM 、齐次多项式核函数、高斯径向基核函数、双曲线正切核函数等不同核函数的事件检测算法。仿真结果表明：针对不同的交通流状况，选择合适的SVM 模型和核函数，得到的检测结果与经典的加利福尼亚算法相比，检测效率高，性能指标好，具有较高的实际应用价值。
고속공로사건검측시교통신식공정급공제학과중적일항중요연구과제，이고속공로교통류적특점위연구대상，제출료일충기우지지향량궤（SVM ）적고속공로사건검측산법。근거지지향량궤적기본원리，분별설계료기우선성불가분SVM 、제차다항식핵함수、고사경향기핵함수、쌍곡선정절핵함수등불동핵함수적사건검측산법。방진결과표명：침대불동적교통류상황，선택합괄적SVM 모형화핵함수，득도적검측결과여경전적가리복니아산법상비，검측효솔고，성능지표호，구유교고적실제응용개치。
Freeway Incident Detection (FID) has been studied as an important research subject in Traffic Information Engineering and Control . This paper , based on the freeway traffic flow characteristics ,presents new algorithms for FID with the support vector machine (SVM ) .According to the principal of SVM ,four different simulation experiments are carried out based on the linearly non-separable SVM ,the homogeneous polynomial kernel function ,the radial basis kernel function and the sigmoid kernel function respectively .The testing results obtained by the new algorithms were compared with those by the California algorithm .The simulation results show that choosing the right SVM model and kernel function according to different traffic flow characteristics can achieve better performance than the California algorithm does .The new algorithms prove of greater practical value .