【摘要】：出租車全球定位系統(tǒng)數(shù)據(jù)中蘊含城市交通和移動對象行為的宏觀信息,從中可以挖掘出有價值的異常軌跡模式。將位置和幾何形狀、行駛時間分別作為出租車軌跡的空間與時間特征,根據(jù)特征偏離情況劃分時間、空間和時空異常軌跡。從軌跡數(shù)據(jù)中提取相同起終點的軌跡集,將軌跡劃分成軌跡片段,計算軌跡間的相似度并進行基于距離和密度的聚類,在空間特征上初步分離出頻繁和稀疏軌跡,根據(jù)數(shù)據(jù)異常判定的kσ準則確定時間特征異常的分離閾值,對時間特征進行再次劃分,最終實現(xiàn)出租車異常軌跡檢測。實驗結(jié)果表明,該方法能從異常軌跡中挖掘出個性化路線、異常停留位置和交通路段,為智能交通、物流高效規(guī)劃和執(zhí)行等提供參考信息。
[Abstract]:The global positioning system (GPS) data of taxis contain macro information about the behavior of urban traffic and moving objects, from which valuable abnormal trajectory patterns can be mined. The position, geometric shape and travel time are regarded as the space and time characteristics of the taxi track respectively, and the time, space and time abnormal track are divided according to the characteristic deviation. The trace sets of the same beginning and end point are extracted from the trajectory data, the trajectory is divided into trajectory segments, the similarity between the tracks is calculated and clustering based on distance and density is carried out, and the frequent and sparse trajectories are preliminarily separated from the spatial features. According to the k 蟽 criterion of data anomaly determination, the separation threshold of time feature anomaly is determined, and the time feature is divided again, finally the taxi abnormal track detection is realized. The experimental results show that the method can mine personalized routes, abnormal stop positions and traffic sections from abnormal tracks, and provide reference information for intelligent transportation, efficient planning and execution of logistics, etc.
【作者單位】： 信息工程大學地理空間信息學院;
【基金】：國家自然科學基金“空間數(shù)據(jù)流的概念漂移問題研究”(41571394)
【分類號】：U495;TP311.13
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本文編號：2226391