基于多尺度区域划分的车辆轨迹预测
首发时间:2017-01-04
摘要:本文针对车辆在区域层面的移动轨迹预测展开研究,提出了基于多尺度区域划分的车辆轨迹预测算法。首先,在城市区域划分方面,提出了基于轨迹起点-终点(Origin-destination,OD点)分布的多尺度区域划分算法,算法采用了先划分细粒度网格再合并形成子区域的两阶段策略,可以根据区域OD点的分布特征,调整子区域的面积和形状。与等尺度网格划分相比,该区域划分算法避免了热区边缘被破坏,区分了密集区域和稀疏区域。然后,在区域划分的基础上,应用哈希表实现多阶马尔科夫模型,提出了车辆区域层面的轨迹预测算法,该预测算法有效缓解了传统基于转移概率矩阵算法存在的状态空间膨胀的问题,并支持增量式更新。基于大量真实出租车轨迹数据验证了算法的性能,结果表明本文提出的区域划分方法显著提高了轨迹预测的正确率,同时对训练数据规模和模型阶数的影响进行了分析。
关键词: 轨迹预测 城市区域划分 马尔科夫模型 车辆移动轨迹
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Vehicle Trajectory Prediction Based on Multi-scale Region Division
Abstract:In this paper, the vehicle trajectory prediction at regional level is studied, and a prediction method based on multi-scale regional division is proposed. Firstly, a multi-scale region division algorithm based on the origin-destination (OD) distribution of trajectories is proposed. The algorithm uses a two-phase strategy to divide the area to grids and merge neighbouring grids into sub-regions. The algorithm can adjust the size and shape of the sub-area according to the distribution characteristics of OD. Compared with the equal-scale meshing, the proposed algorithm avoids the destruction of the edge of the hot zoneand distinguishes the dense area and the sparse area. Secondly, after area dividion, a multi-order Markov model based on hash table is proposed. Compared with the traditional implement based on transition probability matrix, the hash table based implement effectively alleviates the problem of state space expansion and supports incremental updating. In this paper, the performance of the algorithm is verified based on the actual trajectory data. Experimental results showthat the proposed area division algorithm greatly improves the accuracy of trajectory prediction.The influence of the training data scale and the model orderon the prediction algorithm are analyzed.
Keywords: trajectory prediction urban area division Markov model vehicle trajectory
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