面向历史轨迹的多因素驾驶偏好挖掘算法

On Mining Multivariate Driving Preference from Historical Trajectories

  • 摘要: 提出一种新型基于窗口的数据挖掘算法,以用来挖掘所定义的多因素驾驶偏好.具体地,定义了多因素之间的两两权衡来刻画驾驶偏好;提出了一种用于估算偏好分布的挖掘算法,并根据假设引入了椭圆对挖掘算法进行了优化.结果证明,该方法能够发现本文所定义的多因素驾驶偏好,并且算法有效、快速,具有较好的扩展性

     

    Abstract: A novel window based data mining algorithm was proposed and used to mine the defined multivariate driving preference. Specifically, a model describing paired cost aspects to reflect driving preference was defined, a data mining algorithm to estimate the multivariate driving preference distribution was proposed, and ellipse to optimize the data mining algorithm was introduced. The results showed that the method in this paper could discover the defined multivariate driving preference and the proposed algorithm was effective, fast and had good scalability.

     

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