【摘要】：通過對高速公路交通事件的性質(zhì)和特征進(jìn)行分析,選擇對持續(xù)時(shí)間影響較大的屬性(事件類別、發(fā)生時(shí)間、地點(diǎn)、天氣、傷亡程度、涉及車輛數(shù)、占用車道數(shù))構(gòu)成了描述交通事件的向量,對各屬性進(jìn)行了分類與量化.以交通事件的歷史數(shù)據(jù)集合為基礎(chǔ)構(gòu)建N維搜索空間,計(jì)算了當(dāng)前交通事件與歷史交通事件之間的歐式距離,通過尋找距離最近的K個(gè)元素建立了最近鄰預(yù)測模型.采用單因素方差分析法標(biāo)定了變量權(quán)重,根據(jù)最小誤差法確定了最佳K值.實(shí)例應(yīng)用表明,K-最近鄰預(yù)測模型對持續(xù)時(shí)間范圍為30 min≤T90 min、90 min≤T180 min交通事件預(yù)測精度較高,適合高速公路有大量歷史數(shù)據(jù)的情況下應(yīng)用.
[Abstract]:By analyzing the nature and characteristics of expressway traffic events, we select the attributes (event category, time, place, weather, casualty degree, number of vehicles involved) that have a great impact on duration. The number of lanes occupied constitutes the vector describing traffic events, and classifies and quantifies each attribute. Based on the historical data set of traffic events, the N-dimensional search space is constructed, the Euclidean distance between current traffic events and historical traffic events is calculated, and the nearest neighbor prediction model is established by searching for the nearest K elements. The variable weight is calibrated by single factor variance analysis method and the optimum K value is determined according to the minimum error method. The practical application shows that the K- nearest neighbor prediction model has a high prediction accuracy for traffic events in the duration range of 30 min 鈮，

本文編號：2160574