Clustering Vehicle Temporal and Spatial Travel Behavior Using License Plate Recognition Data
Understanding travel patterns of vehicle can support the planning and design of better services. In addition, vehicle clustering can improve management efficiency through more targeted access to groups of interest and facilitate planning by more specific survey design. This paper clustered 854,712 v...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2017/1738085 |
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| author | Huiyu Chen Chao Yang Xiangdong Xu |
| author_facet | Huiyu Chen Chao Yang Xiangdong Xu |
| author_sort | Huiyu Chen |
| collection | DOAJ |
| description | Understanding travel patterns of vehicle can support the planning and design of better services. In addition, vehicle clustering can improve management efficiency through more targeted access to groups of interest and facilitate planning by more specific survey design. This paper clustered 854,712 vehicles in a week using K-means clustering algorithm based on license plate recognition (LPR) data obtained in Shenzhen, China. Firstly, several travel characteristics related to temporal and spatial variability and activity patterns are used to identify homogeneous clusters. Then, Davies-Bouldin index (DBI) and Silhouette Coefficient (SC) are applied to capture the optimal number of groups and, consequently, six groups are classified in weekdays and three groups are sorted in weekends, including commuting vehicles and some other occasional leisure travel vehicles. Moreover, a detailed analysis of the characteristics of each group in terms of spatial travel patterns and temporal changes are presented. This study highlights the possibility of applying LPR data for discovering the underlying factor in vehicle travel patterns and examining the characteristic of some groups specifically. |
| format | Article |
| id | doaj-art-5fa17c2fdb3547868ca4dc1550c856a0 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-5fa17c2fdb3547868ca4dc1550c856a02025-02-03T06:01:20ZengWileyJournal of Advanced Transportation0197-67292042-31952017-01-01201710.1155/2017/17380851738085Clustering Vehicle Temporal and Spatial Travel Behavior Using License Plate Recognition DataHuiyu Chen0Chao Yang1Xiangdong Xu2Key Laboratory of Road and Traffic Engineering, Tongji University, 4800 Cao’an Road, Shanghai 201804, ChinaKey Laboratory of Road and Traffic Engineering, Tongji University, 4800 Cao’an Road, Shanghai 201804, ChinaKey Laboratory of Road and Traffic Engineering, Tongji University, 4800 Cao’an Road, Shanghai 201804, ChinaUnderstanding travel patterns of vehicle can support the planning and design of better services. In addition, vehicle clustering can improve management efficiency through more targeted access to groups of interest and facilitate planning by more specific survey design. This paper clustered 854,712 vehicles in a week using K-means clustering algorithm based on license plate recognition (LPR) data obtained in Shenzhen, China. Firstly, several travel characteristics related to temporal and spatial variability and activity patterns are used to identify homogeneous clusters. Then, Davies-Bouldin index (DBI) and Silhouette Coefficient (SC) are applied to capture the optimal number of groups and, consequently, six groups are classified in weekdays and three groups are sorted in weekends, including commuting vehicles and some other occasional leisure travel vehicles. Moreover, a detailed analysis of the characteristics of each group in terms of spatial travel patterns and temporal changes are presented. This study highlights the possibility of applying LPR data for discovering the underlying factor in vehicle travel patterns and examining the characteristic of some groups specifically.http://dx.doi.org/10.1155/2017/1738085 |
| spellingShingle | Huiyu Chen Chao Yang Xiangdong Xu Clustering Vehicle Temporal and Spatial Travel Behavior Using License Plate Recognition Data Journal of Advanced Transportation |
| title | Clustering Vehicle Temporal and Spatial Travel Behavior Using License Plate Recognition Data |
| title_full | Clustering Vehicle Temporal and Spatial Travel Behavior Using License Plate Recognition Data |
| title_fullStr | Clustering Vehicle Temporal and Spatial Travel Behavior Using License Plate Recognition Data |
| title_full_unstemmed | Clustering Vehicle Temporal and Spatial Travel Behavior Using License Plate Recognition Data |
| title_short | Clustering Vehicle Temporal and Spatial Travel Behavior Using License Plate Recognition Data |
| title_sort | clustering vehicle temporal and spatial travel behavior using license plate recognition data |
| url | http://dx.doi.org/10.1155/2017/1738085 |
| work_keys_str_mv | AT huiyuchen clusteringvehicletemporalandspatialtravelbehaviorusinglicenseplaterecognitiondata AT chaoyang clusteringvehicletemporalandspatialtravelbehaviorusinglicenseplaterecognitiondata AT xiangdongxu clusteringvehicletemporalandspatialtravelbehaviorusinglicenseplaterecognitiondata |