浮动车数据在时空维度呈现较强的稀疏性，是其应用于城市路网交通流估计所面临的主要难题之一。本文通过分析路网交通流速度的时空特征，构建了一种基于朴素贝叶斯法的估计模型，实现对路网中未被样本覆盖路段交通流速度的估计。时间特征主要考虑目标路段相邻时段的交通流速度，空间特征根据路段间交通流相似关系进行分析，突破了传统基于欧氏空间或拓扑关系的度量方式。结果显示，模型能有效地估计出样本缺失路段的交通流速度，且在精度方面相对传统基于拓扑关系的算法优势显著，较好地解决了数据时空稀疏性问题，对基于浮动车数据的交通应用具有较强的实践意义。%The sample spatio-temporalsparsity is one of the major challenges for traffic estimation when using floating car data (FCD).Spatio-temporal characteristics of road traffic flow are analysed and applied to build a naive Bayes-based traffic estimation model which is proposed to estimate the missing traffic state of the roads which are not covered by samples.In the model,the adjacent period traffic flow speed of the target road segment is considered for the representation of the time characteristic.And instead of Euclidean distance and topology relationship,urban traffic flow similarity relationships are mined to quantify the interior space features of urban traffic.The result demonstrates that the method is effective for missing traffic state estimation and more precision compared to traditional methods based on topology relationship.As a conclusion, the proposed model can solve the spatio-temporal sparsity problem effectively,which has a strong practical significance for traffic appl ication based on FCD.
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