A Segment-Based Trajectory Similarity Measure in the Urban Transportation Systems
With the rapid spread of built-in GPS handheld smart devices, the trajectory data from GPS sensors has grown explosively. Trajectory data has spatio-temporal characteristics and rich information. Using trajectory data processing techniques can mine the patterns of human activities and the moving pat...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-03-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/17/3/524 |
| _version_ | 1818001236605009920 |
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| author | Yingchi Mao Haishi Zhong Xianjian Xiao Xiaofang Li |
| author_facet | Yingchi Mao Haishi Zhong Xianjian Xiao Xiaofang Li |
| author_sort | Yingchi Mao |
| collection | DOAJ |
| description | With the rapid spread of built-in GPS handheld smart devices, the trajectory data from GPS sensors has grown explosively. Trajectory data has spatio-temporal characteristics and rich information. Using trajectory data processing techniques can mine the patterns of human activities and the moving patterns of vehicles in the intelligent transportation systems. A trajectory similarity measure is one of the most important issues in trajectory data mining (clustering, classification, frequent pattern mining, etc.). Unfortunately, the main similarity measure algorithms with the trajectory data have been found to be inaccurate, highly sensitive of sampling methods, and have low robustness for the noise data. To solve the above problems, three distances and their corresponding computation methods are proposed in this paper. The point-segment distance can decrease the sensitivity of the point sampling methods. The prediction distance optimizes the temporal distance with the features of trajectory data. The segment-segment distance introduces the trajectory shape factor into the similarity measurement to improve the accuracy. The three kinds of distance are integrated with the traditional dynamic time warping algorithm (DTW) algorithm to propose a new segment–based dynamic time warping algorithm (SDTW). The experimental results show that the SDTW algorithm can exhibit about 57%, 86%, and 31% better accuracy than the longest common subsequence algorithm (LCSS), and edit distance on real sequence algorithm (EDR) , and DTW, respectively, and that the sensitivity to the noise data is lower than that those algorithms. |
| first_indexed | 2024-04-14T03:31:52Z |
| format | Article |
| id | doaj.art-55587bd8788b48b2ab13c23abb1e6655 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T03:31:52Z |
| publishDate | 2017-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-55587bd8788b48b2ab13c23abb1e66552022-12-22T02:14:55ZengMDPI AGSensors1424-82202017-03-0117352410.3390/s17030524s17030524A Segment-Based Trajectory Similarity Measure in the Urban Transportation SystemsYingchi Mao0Haishi Zhong1Xianjian Xiao2Xiaofang Li3College of Computer and Information, Hohai University, Nanjing 210098, ChinaCollege of Computer and Information, Hohai University, Nanjing 210098, ChinaSchool of Computer Information & Engineering, Changzhou Institute of Technology, Changzhou 213032, ChinaSchool of Computer Information & Engineering, Changzhou Institute of Technology, Changzhou 213032, ChinaWith the rapid spread of built-in GPS handheld smart devices, the trajectory data from GPS sensors has grown explosively. Trajectory data has spatio-temporal characteristics and rich information. Using trajectory data processing techniques can mine the patterns of human activities and the moving patterns of vehicles in the intelligent transportation systems. A trajectory similarity measure is one of the most important issues in trajectory data mining (clustering, classification, frequent pattern mining, etc.). Unfortunately, the main similarity measure algorithms with the trajectory data have been found to be inaccurate, highly sensitive of sampling methods, and have low robustness for the noise data. To solve the above problems, three distances and their corresponding computation methods are proposed in this paper. The point-segment distance can decrease the sensitivity of the point sampling methods. The prediction distance optimizes the temporal distance with the features of trajectory data. The segment-segment distance introduces the trajectory shape factor into the similarity measurement to improve the accuracy. The three kinds of distance are integrated with the traditional dynamic time warping algorithm (DTW) algorithm to propose a new segment–based dynamic time warping algorithm (SDTW). The experimental results show that the SDTW algorithm can exhibit about 57%, 86%, and 31% better accuracy than the longest common subsequence algorithm (LCSS), and edit distance on real sequence algorithm (EDR) , and DTW, respectively, and that the sensitivity to the noise data is lower than that those algorithms.http://www.mdpi.com/1424-8220/17/3/524GPS trajectoryGPS sensortrajectory similarity measurespatial-temporal data |
| spellingShingle | Yingchi Mao Haishi Zhong Xianjian Xiao Xiaofang Li A Segment-Based Trajectory Similarity Measure in the Urban Transportation Systems Sensors GPS trajectory GPS sensor trajectory similarity measure spatial-temporal data |
| title | A Segment-Based Trajectory Similarity Measure in the Urban Transportation Systems |
| title_full | A Segment-Based Trajectory Similarity Measure in the Urban Transportation Systems |
| title_fullStr | A Segment-Based Trajectory Similarity Measure in the Urban Transportation Systems |
| title_full_unstemmed | A Segment-Based Trajectory Similarity Measure in the Urban Transportation Systems |
| title_short | A Segment-Based Trajectory Similarity Measure in the Urban Transportation Systems |
| title_sort | segment based trajectory similarity measure in the urban transportation systems |
| topic | GPS trajectory GPS sensor trajectory similarity measure spatial-temporal data |
| url | http://www.mdpi.com/1424-8220/17/3/524 |
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