Phone‐vehicle trajectory matching framework based on ALPR and cellular signalling data
Abstract With the advancement of positioning techniques, a large amount of trajectory data has been produced. Matching vehicles with mobile phones using different trajectories can benefit many applications, such as driving behaviour analysis and travel mode split. Moreover, as a privacy attack metho...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | IET Intelligent Transport Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/itr2.12008 |
| _version_ | 1797994927188082688 |
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| author | Wei Wan Ming Cai |
| author_facet | Wei Wan Ming Cai |
| author_sort | Wei Wan |
| collection | DOAJ |
| description | Abstract With the advancement of positioning techniques, a large amount of trajectory data has been produced. Matching vehicles with mobile phones using different trajectories can benefit many applications, such as driving behaviour analysis and travel mode split. Moreover, as a privacy attack method, it can provide theoretical inspiration for privacy protection theory. To address this problem, a new trajectory matching framework for processing massive Automatic License Plate Recognition (ALPR) and cellular signalling data is proposed. Information entropy was adopted to address the movement frequency of trajectories and then the infrequent vehicles and phones that did not meet the threshold were pruned. Next, an effective matching algorithm was devised to match the trajectories of vehicles and mobile phones. Moreover, to solve the problem of obtaining a small number of matching results, a data augmentation algorithm was proposed to add new, matching records. Last, a classification model was constructed with LightGBM to determine whether the vehicle matches the phone. Experimental results on real datasets show that the framework outperforms typical techniques in terms of effectiveness and efficiency. The data obtained by data augmentation have distribution characteristics similar to those of the original data. The proposed classification model achieves an accuracy of 93.6%. |
| first_indexed | 2024-04-11T09:53:31Z |
| format | Article |
| id | doaj.art-e2fc46973978476ca1799723c788860f |
| institution | Directory Open Access Journal |
| issn | 1751-956X 1751-9578 |
| language | English |
| last_indexed | 2024-04-11T09:53:31Z |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj.art-e2fc46973978476ca1799723c788860f2022-12-22T04:30:43ZengWileyIET Intelligent Transport Systems1751-956X1751-95782021-01-0115110711810.1049/itr2.12008Phone‐vehicle trajectory matching framework based on ALPR and cellular signalling dataWei Wan0Ming Cai1Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering Sun Yat‐sen University Guangzhou ChinaResearch Center of Intelligent Transportation Systems, School of Intelligent Systems Engineering Sun Yat‐sen University Guangzhou ChinaAbstract With the advancement of positioning techniques, a large amount of trajectory data has been produced. Matching vehicles with mobile phones using different trajectories can benefit many applications, such as driving behaviour analysis and travel mode split. Moreover, as a privacy attack method, it can provide theoretical inspiration for privacy protection theory. To address this problem, a new trajectory matching framework for processing massive Automatic License Plate Recognition (ALPR) and cellular signalling data is proposed. Information entropy was adopted to address the movement frequency of trajectories and then the infrequent vehicles and phones that did not meet the threshold were pruned. Next, an effective matching algorithm was devised to match the trajectories of vehicles and mobile phones. Moreover, to solve the problem of obtaining a small number of matching results, a data augmentation algorithm was proposed to add new, matching records. Last, a classification model was constructed with LightGBM to determine whether the vehicle matches the phone. Experimental results on real datasets show that the framework outperforms typical techniques in terms of effectiveness and efficiency. The data obtained by data augmentation have distribution characteristics similar to those of the original data. The proposed classification model achieves an accuracy of 93.6%.https://doi.org/10.1049/itr2.12008Image recognitionComputer vision and image processing techniquesData securityTraffic engineering computing |
| spellingShingle | Wei Wan Ming Cai Phone‐vehicle trajectory matching framework based on ALPR and cellular signalling data IET Intelligent Transport Systems Image recognition Computer vision and image processing techniques Data security Traffic engineering computing |
| title | Phone‐vehicle trajectory matching framework based on ALPR and cellular signalling data |
| title_full | Phone‐vehicle trajectory matching framework based on ALPR and cellular signalling data |
| title_fullStr | Phone‐vehicle trajectory matching framework based on ALPR and cellular signalling data |
| title_full_unstemmed | Phone‐vehicle trajectory matching framework based on ALPR and cellular signalling data |
| title_short | Phone‐vehicle trajectory matching framework based on ALPR and cellular signalling data |
| title_sort | phone vehicle trajectory matching framework based on alpr and cellular signalling data |
| topic | Image recognition Computer vision and image processing techniques Data security Traffic engineering computing |
| url | https://doi.org/10.1049/itr2.12008 |
| work_keys_str_mv | AT weiwan phonevehicletrajectorymatchingframeworkbasedonalprandcellularsignallingdata AT mingcai phonevehicletrajectorymatchingframeworkbasedonalprandcellularsignallingdata |