| 贺玉海,周庆琨,程埮晟,王勤鹏.基于改进K-Medoids的组合聚类算法及异常值检测研究[J].,2022,62(4):403-410 |
| 基于改进K-Medoids的组合聚类算法及异常值检测研究 |
| Research on combinatorial clustering algorithm and anomaly detection based on improved K-Medoids |
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| DOI：10.7511/dllgxb202204009 |
| 中文关键词:车辆轨迹聚类分析异常值检测相似性度量DBSCAN算法 |
| 英文关键词:vehicle trajectorycluster analysisanomaly detectionsimilarity measureDBSCAN algorithm |
| 基金项目:国家自然科学基金资助项目(51009112). |
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| 摘要点击次数:316 |
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| 中文摘要: |
| 采用聚类算法和异常值检测算法进行车辆轨迹信息的提取与挖掘，在交通控制与管理、道路拥堵时空分析与治理、用户出行线路规划与推荐，以及自动驾驶决策规划等应用中具有重要意义．针对现有聚类算法和异常值检测算法参数难以控制、算法存在随机性的不足，提出基于K-Medoids与DBSCAN组合的聚类算法．通过对模拟十字交叉路口数据集的训练，得到一个交叉路口最佳聚类模型，并用真实轨迹数据集验证、优化该模型．然后，将交叉路口区域内一段时间内的轨迹聚类数据流进行可视化再现，取得了异常轨迹少、聚类速度快的聚类效果，同时比较选择出算法各个参数的最优值．最后，通过参数传递使DBSCAN算法能够更精确地识别出异常轨迹，为交通治理与自动驾驶决策提供指导． |
| 英文摘要: |
| The extraction and mining of vehicle trajectory information using clustering algorithm and anomaly detection algorithm are of great significance in applications such as traffic control and management, spatial and temporal analysis and management of road congestion, user travel route planning and recommendation, and autonomous driving decision planning. A clustering algorithm based on a combination of K-Medoids and DBSCAN is proposed to address the shortcomings of existing clustering algorithms and anomaly detection algorithms, which are difficult to control the parameters and have randomness. Through training on simulated four exit intersection datasets, an optimal clustering model for intersections is obtained, and the model is validated and optimized with real trajectory datasets. Then, the trajectory clustering data flow in the intersection area over some time is reproduced visually, and the clustering effect of fewer abnormal trajectories and faster clustering is achieved, while the optimal values of each parameter of the algorithm are selected by comparison. Finally, the parameter transfer enables the DBSCAN algorithm to identify the abnormal trajectories more accurately and provide guidance for traffic management and autonomous driving decisions. |
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