方志祥, 倪雅倩, 黄守倩. 融合Markov与多类机器学习模型的个体出行位置预测模型[J]. 武汉大学学报 ( 信息科学版), 2021, 46(6): 799-806. DOI: 10.13203/j.whugis20190404
引用本文: 方志祥, 倪雅倩, 黄守倩. 融合Markov与多类机器学习模型的个体出行位置预测模型[J]. 武汉大学学报 ( 信息科学版), 2021, 46(6): 799-806. DOI: 10.13203/j.whugis20190404
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FANG Zhixiang, NI Yaqian, HUANG Shouqian. A Multi-model Fusion Model of Individual Travel Location Prediction Using Markov and Machine Learning Methods[J]. Geomatics and Information Science of Wuhan University, 2021, 46(6): 799-806. DOI: 10.13203/j.whugis20190404
Citation: FANG Zhixiang, NI Yaqian, HUANG Shouqian. A Multi-model Fusion Model of Individual Travel Location Prediction Using Markov and Machine Learning Methods[J]. Geomatics and Information Science of Wuhan University, 2021, 46(6): 799-806. DOI: 10.13203/j.whugis20190404
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融合Markov与多类机器学习模型的个体出行位置预测模型

A Multi-model Fusion Model of Individual Travel Location Prediction Using Markov and Machine Learning Methods

    摘要
  • 摘要: 随着城市化的发展, 人们出行的方式逐渐多样化, 对人类行为的深入理解以及对个体出行行为的建模预测有助于解释若干复杂的社会经济现象, 且在基于位置的服务、交通规划、公共安全等方面具有重要价值。个体出行行为预测建立在深入理解人类活动特性的基础上, 而在移动互联网时代, 网络空间的上网行为与现实空间的出行行为密不可分。首先基于上网行为特征, 融合马尔可夫(Markov)模型和多类机器学习模型, 构建了个体出行位置预测模型, 该模型使用了基于频率分布图的自适应融合规则, 融合了传统的Markov模型和机器学习多分类模型的结果进行个体出行位置预测；然后利用手机数据、上网流量数据、兴趣点数据及天气等多源数据进行个体出行位置预测实验。实验结果表明, 该模型的第1个和前3个预测结果中包括正确结果的准确率分别为74.59%、94.19%, 均优于基础模型的准确率和利用投票法融合规则融合基础模型的准确率, 且预测时间粒度为30 min时, 该模型的预测效果较好。

     

    Abstract:
      Objectives  With the development of urbanization, people's travel behaviors have diversified. An in-depth understanding of human behavior and the modeling and prediction of individual travel behaviors are helpful in explaining several complex socio-economic phenomena, and are important in offering location-based services, transportation planning, and public safety. Individual travel behavior prediction is based on a deep understanding of human activity characteristics. In the era of mobile Internet, the online behavior of cyberspace is inseparable from the travel behavior of real space.
      Methods  This paper integrates individuals' mobile phone tracking data and Internet traffic data, and constructs a multi-model fusion model of individual travel location prediction on Markov and machine learning methods. Considering the classification probability of prediction results, an adaptive fusion strategy based on frequency distribution graph is proposed. The prediction results of Markov model and machine learning multi-classification model are merged together to obtain the final mobile phone user travel location prediction result.
      Results  This paper performs individual travel location prediction experiments on the basis of multi-source data. And the experiments show that the correct rate of the first result and the top three results of the multi-model fusion location prediction model based on histogram is respectively 74.59% and 94.19%, higher than the prediction accuracy of the basic model with the highest accuracy and the vote strategy.
      Conclusions  Under the prediction time granularity of 30 minutes, the individual travel location prediction is better.

     

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