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| 大数据环境下移动对象自适应轨迹预测模型 |
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乔少杰1, 李天瑞1, 韩楠2, 高云君3, 元昌安4, 王晓腾1, 唐常杰5
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1.西南交通大学 信息科学与技术学院, 四川 成都 610031;2.西南交通大学 生命科学与工程学院, 四川 成都 610031;3.浙江大学 计算机科学与技术学院, 浙江 杭州 310027;4.科学计算与智能信息处理广西高校重点实验室(广西师范学院), 广西 南宁 530023;5.四川大学 计算机学院, 四川 成都 610065
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| 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%. |
| 关键词: 位置大数据 智能交通 轨迹预测 隐马尔可夫模型 自适应 |
| DOI：10.13328/j.cnki.jos.004889 |
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| 基金项目:国家自然科学基金(61100045, 61165013); 高等学校博士学科点专项科研基金(20110184120008); 教育部人文社会科学研究规划基金(15YJAZH058); 教育部人文社会科学研究青年基金(14YJCZH046); 中央高校基本科研业务费专项资金(2682013 BR023); 四川省教育厅资助科研项目(14ZB0458); 科学计算与智能信息处理广西高校重点实验室开放课题(GXSCIIP201407) |
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| Self-Adaptive Trajectory Prediction Model for Moving Objects in Big Data Environment |
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QIAO Shao-Jie1, LI Tian-Rui1, HAN Nan2, GAO Yun-Jun3, YUAN Chang-An4, WANG Xiao-Teng1, TANG Chang-Jie5
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1.School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China;2.School of Life Science and Engineering, Southwest Jiaotong University, Chengdu 610031, China;3.College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China;4.Science Computing and Intelligent Information Processing of Guangxi Higher Education Key Laboratory (Guangxi Teachers Education University), Nanning 530023, China;5.College of Computer Science, Sichuan University, Chengdu 610065, China
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| Abstract: |
| 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 Na?ve algorithm with an average gap of 46.7%. |
| Key words: location big data intelligent transportation trajectory prediction hidden Markov model self-adaptive |
