摘要
通过对高速公路交通事件的性质和特征进行分析,选择对持续时间影响较大的属性(事件类别、发生时间、地点、天气、伤亡程度、涉及车辆数、占用车道数)构成了描述交通事件的向量,对各属性进行了分类与量化.以交通事件的历史数据集合为基础构建N维搜索空间,计算了当前交通事件与历史交通事件之间的欧式距离,通过寻找距离最近的K个元素建立了最近邻预测模型.采用单因素方差分析法标定了变量权重,根据最小误差法确定了最佳K值.实例应用表明,K-最近邻预测模型对持续时间范围为30 min≤T<90 min、90 min≤T<180 min交通事件预测精度较高,适合高速公路有大量历史数据的情况下应用.
Through analyzing freeway traffic incidents’characters and properties,this paper describe traffic inci-dent by a series variables,such as the categories,time,site,whether,dead and injury,vehicle,lanes,et.al. Since traffic incident has similarities,a N dimensional space has been built up based on historic traffic incident. The prediction model set up using K-nearest neighbor method,which predict the duration time by finding the most nearest neighbor in the historic space.In the model,the weight of the variable determined by using ANO-VA analysis,and the optimal K got by minimizing the prediction error.Finally,this paper use K-nearest neigh-bor model to predict different groups of field data,it shows that this model has a good performance for the mid-group of incidents,which duration times are：30 min≤T〈90 min,and 90 min≤T〈180 min.Apparently,this model could be used for the freeway which has a large amount of historic data.
出处
《昆明理工大学学报（自然科学版）》
CAS
北大核心
2014年第6期45-50,共6页
Journal of Kunming University of Science and Technology(Natural Science)
基金
国家自然科学基金项目(51308569)
关键词
高速公路
交通事件
K-最近邻
持续时间
预测
Freeway
Traffic incident
K-nearest neighbor
Duration time
Prediction
