Trajectory dimensionality reduction and hyperparameter settings of DBSCAN for trajectory clustering
Abstract The density‐based spatial clustering of application with noise (DBSCAN) algorithm has good robustness and is widely employed to cluster vehicle trajectories for vehicle movement pattern recognition. However, the distance or similarity between two trajectories varies from tens to hundreds of...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-05-01
|
| Series: | IET Intelligent Transport Systems |
| Online Access: | https://doi.org/10.1049/itr2.12166 |
| _version_ | 1811183741803954176 |
|---|---|
| author | Xiaohong Yu Wei Long Yanyan Li Lin Gao Xiaoqiu Shi |
| author_facet | Xiaohong Yu Wei Long Yanyan Li Lin Gao Xiaoqiu Shi |
| author_sort | Xiaohong Yu |
| collection | DOAJ |
| description | Abstract The density‐based spatial clustering of application with noise (DBSCAN) algorithm has good robustness and is widely employed to cluster vehicle trajectories for vehicle movement pattern recognition. However, the distance or similarity between two trajectories varies from tens to hundreds of thousands, and there is no effective method for determining the values of the hyperparameters eps and MinPts of DBSCAN. In addition, with increasing sizes of trajectory datasets, some trajectory clustering methods that directly analyse points and line segments incur large computational costs and time overhead. To solve these two dilemmas, the authors propose an effective trajectory dimensionality reduction method and a DBSCAN hyperparameter initial value setting method. The trajectory dimensionality reduction algorithm processes trajectories with different lengths into the same dimensionality (the same number of feature points). The reserved points preserve the spatial and temporal information of these trajectories as much as possible. The DBSCAN hyperparameter initial value setting algorithm obtains the effective initial values of eps and MinPts for facilitating subsequent adjustments. Finally, we validate these proposed methods on two trajectory datasets collected from two real‐world scenes, and the experimental results are promising and effective. |
| first_indexed | 2024-04-11T09:50:57Z |
| format | Article |
| id | doaj.art-b456d80edcb04ff89a02bb057f3fccdd |
| institution | Directory Open Access Journal |
| issn | 1751-956X 1751-9578 |
| language | English |
| last_indexed | 2024-04-11T09:50:57Z |
| publishDate | 2022-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj.art-b456d80edcb04ff89a02bb057f3fccdd2022-12-22T04:30:47ZengWileyIET Intelligent Transport Systems1751-956X1751-95782022-05-0116569171010.1049/itr2.12166Trajectory dimensionality reduction and hyperparameter settings of DBSCAN for trajectory clusteringXiaohong Yu0Wei Long1Yanyan Li2Lin Gao3Xiaoqiu Shi4School of Mechanical Engineering Sichuan University Chengdu ChinaSchool of Mechanical Engineering Sichuan University Chengdu ChinaSchool of Mechanical Engineering Sichuan University Chengdu ChinaSchool of Mechanical Engineering Sichuan University Chengdu ChinaSchool of Mechanical Engineering Sichuan University Chengdu ChinaAbstract The density‐based spatial clustering of application with noise (DBSCAN) algorithm has good robustness and is widely employed to cluster vehicle trajectories for vehicle movement pattern recognition. However, the distance or similarity between two trajectories varies from tens to hundreds of thousands, and there is no effective method for determining the values of the hyperparameters eps and MinPts of DBSCAN. In addition, with increasing sizes of trajectory datasets, some trajectory clustering methods that directly analyse points and line segments incur large computational costs and time overhead. To solve these two dilemmas, the authors propose an effective trajectory dimensionality reduction method and a DBSCAN hyperparameter initial value setting method. The trajectory dimensionality reduction algorithm processes trajectories with different lengths into the same dimensionality (the same number of feature points). The reserved points preserve the spatial and temporal information of these trajectories as much as possible. The DBSCAN hyperparameter initial value setting algorithm obtains the effective initial values of eps and MinPts for facilitating subsequent adjustments. Finally, we validate these proposed methods on two trajectory datasets collected from two real‐world scenes, and the experimental results are promising and effective.https://doi.org/10.1049/itr2.12166 |
| spellingShingle | Xiaohong Yu Wei Long Yanyan Li Lin Gao Xiaoqiu Shi Trajectory dimensionality reduction and hyperparameter settings of DBSCAN for trajectory clustering IET Intelligent Transport Systems |
| title | Trajectory dimensionality reduction and hyperparameter settings of DBSCAN for trajectory clustering |
| title_full | Trajectory dimensionality reduction and hyperparameter settings of DBSCAN for trajectory clustering |
| title_fullStr | Trajectory dimensionality reduction and hyperparameter settings of DBSCAN for trajectory clustering |
| title_full_unstemmed | Trajectory dimensionality reduction and hyperparameter settings of DBSCAN for trajectory clustering |
| title_short | Trajectory dimensionality reduction and hyperparameter settings of DBSCAN for trajectory clustering |
| title_sort | trajectory dimensionality reduction and hyperparameter settings of dbscan for trajectory clustering |
| url | https://doi.org/10.1049/itr2.12166 |
| work_keys_str_mv | AT xiaohongyu trajectorydimensionalityreductionandhyperparametersettingsofdbscanfortrajectoryclustering AT weilong trajectorydimensionalityreductionandhyperparametersettingsofdbscanfortrajectoryclustering AT yanyanli trajectorydimensionalityreductionandhyperparametersettingsofdbscanfortrajectoryclustering AT lingao trajectorydimensionalityreductionandhyperparametersettingsofdbscanfortrajectoryclustering AT xiaoqiushi trajectorydimensionalityreductionandhyperparametersettingsofdbscanfortrajectoryclustering |