An Improved Fuzzy Trajectory Clustering Method for Exploring Urban Travel Patterns
Exploring urban travel patterns can analyze the mobility regularity of residents to provide guidance for urban traffic planning and emergency decision. Clustering methods have been widely applied to explore the hidden information from large-scale trajectory data on travel patterns exploring. How to...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/6651718 |
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| author | Fang Liu Wei Bi Wei Hao Fan Gao Jinjun Tang |
| author_facet | Fang Liu Wei Bi Wei Hao Fan Gao Jinjun Tang |
| author_sort | Fang Liu |
| collection | DOAJ |
| description | Exploring urban travel patterns can analyze the mobility regularity of residents to provide guidance for urban traffic planning and emergency decision. Clustering methods have been widely applied to explore the hidden information from large-scale trajectory data on travel patterns exploring. How to implement soft constraints in the clustering method and evaluate the effectiveness quantitatively is still a challenge. In this study, we propose an improved trajectory clustering method based on fuzzy density-based spatial clustering of applications with noise (TC-FDBSCAN) to conduct classification on trajectory data. Firstly, we define the trajectory distance which considers the influence of different attributes and determines the corresponding weight coefficients to measure the similarity among trajectories. Secondly, membership degrees and membership functions are designed in the fuzzy clustering method as the extension of the classical DBSCAN method. Finally, trajectory data collected in Shenzhen city, China, are divided into two types (workdays and weekends) and then implemented in the experiment to explore different travel patterns. Moreover, three indices including Silhouette Coefficient, Davies–Bouldin index, and Calinski–Harabasz index are used to evaluate the effectiveness among the proposed method and other traditional clustering methods. The results also demonstrate the advantage of the proposed method. |
| format | Article |
| id | doaj-art-2ffba9928c8a4a889f683b3f4d793dbb |
| institution | DOAJ |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-2ffba9928c8a4a889f683b3f4d793dbb2025-08-20T03:20:23ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/66517186651718An Improved Fuzzy Trajectory Clustering Method for Exploring Urban Travel PatternsFang Liu0Wei Bi1Wei Hao2Fan Gao3Jinjun Tang4School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, ChinaSmart Transportation Key Laboratory of Hunan Province, School of Traffic and Transportation Engineering, Central South University, Changsha 410075, ChinaSchool of Traffic and Transportation Engineering, Changsha University of Science and Technology, Changsha 410114, ChinaSmart Transportation Key Laboratory of Hunan Province, School of Traffic and Transportation Engineering, Central South University, Changsha 410075, ChinaSmart Transportation Key Laboratory of Hunan Province, School of Traffic and Transportation Engineering, Central South University, Changsha 410075, ChinaExploring urban travel patterns can analyze the mobility regularity of residents to provide guidance for urban traffic planning and emergency decision. Clustering methods have been widely applied to explore the hidden information from large-scale trajectory data on travel patterns exploring. How to implement soft constraints in the clustering method and evaluate the effectiveness quantitatively is still a challenge. In this study, we propose an improved trajectory clustering method based on fuzzy density-based spatial clustering of applications with noise (TC-FDBSCAN) to conduct classification on trajectory data. Firstly, we define the trajectory distance which considers the influence of different attributes and determines the corresponding weight coefficients to measure the similarity among trajectories. Secondly, membership degrees and membership functions are designed in the fuzzy clustering method as the extension of the classical DBSCAN method. Finally, trajectory data collected in Shenzhen city, China, are divided into two types (workdays and weekends) and then implemented in the experiment to explore different travel patterns. Moreover, three indices including Silhouette Coefficient, Davies–Bouldin index, and Calinski–Harabasz index are used to evaluate the effectiveness among the proposed method and other traditional clustering methods. The results also demonstrate the advantage of the proposed method.http://dx.doi.org/10.1155/2021/6651718 |
| spellingShingle | Fang Liu Wei Bi Wei Hao Fan Gao Jinjun Tang An Improved Fuzzy Trajectory Clustering Method for Exploring Urban Travel Patterns Journal of Advanced Transportation |
| title | An Improved Fuzzy Trajectory Clustering Method for Exploring Urban Travel Patterns |
| title_full | An Improved Fuzzy Trajectory Clustering Method for Exploring Urban Travel Patterns |
| title_fullStr | An Improved Fuzzy Trajectory Clustering Method for Exploring Urban Travel Patterns |
| title_full_unstemmed | An Improved Fuzzy Trajectory Clustering Method for Exploring Urban Travel Patterns |
| title_short | An Improved Fuzzy Trajectory Clustering Method for Exploring Urban Travel Patterns |
| title_sort | improved fuzzy trajectory clustering method for exploring urban travel patterns |
| url | http://dx.doi.org/10.1155/2021/6651718 |
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