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中文摘要：
已有的轨迹预测算法针对移动对象运动模式,使用数学模型进行交通流模拟,难以对路网中的移动对象进行准确的描述.为了解决这一问题,提出基于隐马尔可夫模型（hidden Markov model,简称HMM）的自适应轨迹预测模型SATP（self-adaptive trajectory prediction model based on HMM）,对大数据环境下移动对象海量轨迹利用基于密度的聚类方法进行位置密度分区和高效分段处理,减少HMM的状态数量.根据输入轨迹自动选取参数组合,避免HMM模型中隐状态不连续、状态停留等问题.实验结果表明,SATP模型在实验中表现出较高的预测准确性,并维持较低的时间开销.针对速度随机改变的移动对象,其平均预测准确率为84.1%;相同情况下,平均高出朴素预测算法46.7%.
英文摘要：
The existing trajectory prediction algorithms focus on the mobility pattern of objects and simulate the traffic flow via mathematical models which are inaccurate at describing network-constraint objects. In order to cope with this problem, a self-adaptive parameter selection trajectory prediction model based on hidden Markov models （SATP） is proposed. The new model can efficiently cluster and partition location big data, and extract the hidden and observable states by using a density-based clustering approach in order to reduce the number of states in HMM. SATP can automatically select the parameters on the input trajectories and avoid the problems of discontinuous hidden states and state retention. Experimental results demonstrate that the SATP model has high prediction accuracy with less time overhead. The average prediction accuracy of SATP is 84.1% while the moving objects have a random changing speed, which is higher than the Naive algorithm with an average gap of 46.7%.
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Key techniques for predicting uncertain trajectories of moving objects with dynamic environment awar
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