Traffic travel pattern recognition based on sparse Global Positioning System trajectory data
This article mainly uses sparse Global Positioning System trajectory data to identify traffic travel pattern. In this article, the data are preprocessed and the eigenvalues are calculated. Then, the Global Positioning System track points are identified and extracted by walking and non-walking segmen...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2020-10-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/1550147720968469 |
| _version_ | 1797766743590961152 |
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| author | Juan Chen Kepei Qi Shiyu Zhu |
| author_facet | Juan Chen Kepei Qi Shiyu Zhu |
| author_sort | Juan Chen |
| collection | DOAJ |
| description | This article mainly uses sparse Global Positioning System trajectory data to identify traffic travel pattern. In this article, the data are preprocessed and the eigenvalues are calculated. Then, the Global Positioning System track points are identified and extracted by walking and non-walking segments. Finally, the three machine learning models of support-vector machine, decision tree, and convolutional neural network are used for comparison experiments. The innovation of this article is to propose a walking and non-walking identification method based on density-based spatial clustering of applications with noise clustering. The method takes into account the continuous state between the geographical distributions, and it has better noise immunity against the influence of external factors. In this process, this article directly achieves better conversion point recognition results through the Global Positioning System track point information, which lays a good foundation for the accuracy of travel pattern recognition. The experimental results of this article show that compared with threshold-based and multi-layer perceptron–based methods, the recognition method based on density-based spatial clustering of applications with noise clustering has the highest accuracy, reaching 82.20%. Then, support-vector machine, decision tree, and convolutional neural network are used for traffic travel pattern recognition. The F1-score of support-vector machine is the highest, reaching 0.84, and the F1-scores of decision tree and convolutional neural network are 0.78 and 0.80, respectively. Finally, the support-vector machine was used for the recognition test to achieve an accuracy of 76.83%. |
| first_indexed | 2024-03-12T20:28:40Z |
| format | Article |
| id | doaj.art-ce59408343c4439f9825a744155493b3 |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T20:28:40Z |
| publishDate | 2020-10-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-ce59408343c4439f9825a744155493b32023-08-02T00:16:43ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772020-10-011610.1177/1550147720968469Traffic travel pattern recognition based on sparse Global Positioning System trajectory dataJuan ChenKepei QiShiyu ZhuThis article mainly uses sparse Global Positioning System trajectory data to identify traffic travel pattern. In this article, the data are preprocessed and the eigenvalues are calculated. Then, the Global Positioning System track points are identified and extracted by walking and non-walking segments. Finally, the three machine learning models of support-vector machine, decision tree, and convolutional neural network are used for comparison experiments. The innovation of this article is to propose a walking and non-walking identification method based on density-based spatial clustering of applications with noise clustering. The method takes into account the continuous state between the geographical distributions, and it has better noise immunity against the influence of external factors. In this process, this article directly achieves better conversion point recognition results through the Global Positioning System track point information, which lays a good foundation for the accuracy of travel pattern recognition. The experimental results of this article show that compared with threshold-based and multi-layer perceptron–based methods, the recognition method based on density-based spatial clustering of applications with noise clustering has the highest accuracy, reaching 82.20%. Then, support-vector machine, decision tree, and convolutional neural network are used for traffic travel pattern recognition. The F1-score of support-vector machine is the highest, reaching 0.84, and the F1-scores of decision tree and convolutional neural network are 0.78 and 0.80, respectively. Finally, the support-vector machine was used for the recognition test to achieve an accuracy of 76.83%.https://doi.org/10.1177/1550147720968469 |
| spellingShingle | Juan Chen Kepei Qi Shiyu Zhu Traffic travel pattern recognition based on sparse Global Positioning System trajectory data International Journal of Distributed Sensor Networks |
| title | Traffic travel pattern recognition based on sparse Global Positioning System trajectory data |
| title_full | Traffic travel pattern recognition based on sparse Global Positioning System trajectory data |
| title_fullStr | Traffic travel pattern recognition based on sparse Global Positioning System trajectory data |
| title_full_unstemmed | Traffic travel pattern recognition based on sparse Global Positioning System trajectory data |
| title_short | Traffic travel pattern recognition based on sparse Global Positioning System trajectory data |
| title_sort | traffic travel pattern recognition based on sparse global positioning system trajectory data |
| url | https://doi.org/10.1177/1550147720968469 |
| work_keys_str_mv | AT juanchen traffictravelpatternrecognitionbasedonsparseglobalpositioningsystemtrajectorydata AT kepeiqi traffictravelpatternrecognitionbasedonsparseglobalpositioningsystemtrajectorydata AT shiyuzhu traffictravelpatternrecognitionbasedonsparseglobalpositioningsystemtrajectorydata |