Modeling of vehicle trajectory using K-means and fuzzy C-means clustering
The implementation of information technology in transportation system is becoming a leading trend nowadays due to alleviating the traffic problems such as traffic congestions and accidents are targeted as primary concerns by the traffic operators. Thus, monitoring the traffic scene serves as basis f...
| Main Authors: | , , , , |
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| Format: | Proceedings |
| Language: | English English |
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Institute of Electrical and Electronics Engineers Inc
2019
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| Online Access: | https://eprints.ums.edu.my/id/eprint/31903/1/Modeling%20of%20vehicle%20trajectory%20using%20K-means%20and%20fuzzy%20C-means%20clustering.ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/31903/2/Modeling%20of%20vehicle%20trajectory%20using%20K-means%20and%20fuzzy%20C-means%20clustering%28Conference%20Paper%29.pdf |
| _version_ | 1825714594461515776 |
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| author | Choong, Mei Yeen Lorita Angeline Chin, Renee Ka Yin Yeo, Kiam Beng Teo, Kenneth Tze Kin |
| author_facet | Choong, Mei Yeen Lorita Angeline Chin, Renee Ka Yin Yeo, Kiam Beng Teo, Kenneth Tze Kin |
| author_sort | Choong, Mei Yeen |
| collection | UMS |
| description | The implementation of information technology in transportation system is becoming a leading trend nowadays due to alleviating the traffic problems such as traffic congestions and accidents are targeted as primary concerns by the traffic operators. Thus, monitoring the traffic scene serves as basis for the traffic operators especially at traffic intersection. Extracted traffic data from the monitoring system is often massive which requires efforts in searching for significant patterns in it. These patterns describe the vehicle movements are useful for observation of any abnormal behavior that leads to traffic conflicts. However, it will be a tremendous work for traffic operators to observe the vehicle flows manually where thousands of vehicles may travel through an intersection. Hence, the clustering of vehicle trajectory dataset for similar patterns identification is implemented with k-means and fuzzy c-means (FCM) clustering algorithm. As these clustering algorithms require the number of clusters as input parameter of the algorithms, the study of number of clusters for the clustering is served as focus in this paper. The evaluation of clustering performance with different input parameter of number of clusters is discussed in this paper. |
| first_indexed | 2024-03-06T03:14:08Z |
| format | Proceedings |
| id | ums.eprints-31903 |
| institution | Universiti Malaysia Sabah |
| language | English English |
| last_indexed | 2024-03-06T03:14:08Z |
| publishDate | 2019 |
| publisher | Institute of Electrical and Electronics Engineers Inc |
| record_format | dspace |
| spelling | ums.eprints-319032022-03-18T05:15:22Z https://eprints.ums.edu.my/id/eprint/31903/ Modeling of vehicle trajectory using K-means and fuzzy C-means clustering Choong, Mei Yeen Lorita Angeline Chin, Renee Ka Yin Yeo, Kiam Beng Teo, Kenneth Tze Kin HE1-9990 Transportation and communications QA1-939 Mathematics The implementation of information technology in transportation system is becoming a leading trend nowadays due to alleviating the traffic problems such as traffic congestions and accidents are targeted as primary concerns by the traffic operators. Thus, monitoring the traffic scene serves as basis for the traffic operators especially at traffic intersection. Extracted traffic data from the monitoring system is often massive which requires efforts in searching for significant patterns in it. These patterns describe the vehicle movements are useful for observation of any abnormal behavior that leads to traffic conflicts. However, it will be a tremendous work for traffic operators to observe the vehicle flows manually where thousands of vehicles may travel through an intersection. Hence, the clustering of vehicle trajectory dataset for similar patterns identification is implemented with k-means and fuzzy c-means (FCM) clustering algorithm. As these clustering algorithms require the number of clusters as input parameter of the algorithms, the study of number of clusters for the clustering is served as focus in this paper. The evaluation of clustering performance with different input parameter of number of clusters is discussed in this paper. Institute of Electrical and Electronics Engineers Inc 2019-02-08 Proceedings PeerReviewed text en https://eprints.ums.edu.my/id/eprint/31903/1/Modeling%20of%20vehicle%20trajectory%20using%20K-means%20and%20fuzzy%20C-means%20clustering.ABSTRACT.pdf text en https://eprints.ums.edu.my/id/eprint/31903/2/Modeling%20of%20vehicle%20trajectory%20using%20K-means%20and%20fuzzy%20C-means%20clustering%28Conference%20Paper%29.pdf Choong, Mei Yeen and Lorita Angeline and Chin, Renee Ka Yin and Yeo, Kiam Beng and Teo, Kenneth Tze Kin (2019) Modeling of vehicle trajectory using K-means and fuzzy C-means clustering. https://ieeexplore.ieee.org/document/8638471 |
| spellingShingle | HE1-9990 Transportation and communications QA1-939 Mathematics Choong, Mei Yeen Lorita Angeline Chin, Renee Ka Yin Yeo, Kiam Beng Teo, Kenneth Tze Kin Modeling of vehicle trajectory using K-means and fuzzy C-means clustering |
| title | Modeling of vehicle trajectory using K-means and fuzzy C-means clustering |
| title_full | Modeling of vehicle trajectory using K-means and fuzzy C-means clustering |
| title_fullStr | Modeling of vehicle trajectory using K-means and fuzzy C-means clustering |
| title_full_unstemmed | Modeling of vehicle trajectory using K-means and fuzzy C-means clustering |
| title_short | Modeling of vehicle trajectory using K-means and fuzzy C-means clustering |
| title_sort | modeling of vehicle trajectory using k means and fuzzy c means clustering |
| topic | HE1-9990 Transportation and communications QA1-939 Mathematics |
| url | https://eprints.ums.edu.my/id/eprint/31903/1/Modeling%20of%20vehicle%20trajectory%20using%20K-means%20and%20fuzzy%20C-means%20clustering.ABSTRACT.pdf https://eprints.ums.edu.my/id/eprint/31903/2/Modeling%20of%20vehicle%20trajectory%20using%20K-means%20and%20fuzzy%20C-means%20clustering%28Conference%20Paper%29.pdf |
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