GeoSDVA: A Semi-Supervised Dirichlet Variational Autoencoder Model for Transportation Mode Identification
Inferring the transportation modes of travelers is an essential part of intelligent transportation systems. With the development of mobile services, it is easy to effectively obtain massive location readings of travelers with GPS-enabled smart devices, such as smartphones. These readings make unders...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/11/5/290 |
| _version_ | 1797499385438797824 |
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| author | Xiaoxi Zhang Yuan Gao Xin Wang Jun Feng Yan Shi |
| author_facet | Xiaoxi Zhang Yuan Gao Xin Wang Jun Feng Yan Shi |
| author_sort | Xiaoxi Zhang |
| collection | DOAJ |
| description | Inferring the transportation modes of travelers is an essential part of intelligent transportation systems. With the development of mobile services, it is easy to effectively obtain massive location readings of travelers with GPS-enabled smart devices, such as smartphones. These readings make understanding human activities very convenient. Therefore, how to automatically infer transportation modes from these massive readings has come into the spotlight. The existing methods for transportation mode identification are usually based on supervised learning. However, the raw GPS readings do not contain any labels, and it is expensive and time-consuming to annotate sufficient samples for training supervised learning-based models. In addition, not enough attention is paid to the problem that GPS readings collected in urban areas are affected by surrounding geographic information (e.g., the level of road transportation or the distribution of stations). To solve this problem, a geographic information-fused semi-supervised method based on a Dirichlet variational autoencoder, named GeoSDVA, is proposed in this paper for transportation mode identification. GeoSDVA first fuses the motion features of the GPS trajectories with the nearby geographic information. Then, both labeled and unlabeled trajectories are used to train the semi-supervised model based on the Dirichlet variational autoencoder architecture for transportation mode identification. Experiments on three real GPS trajectory datasets demonstrate that GeoSDVA can train an excellent transportation mode identification model with only a few labeled trajectories. |
| first_indexed | 2024-03-10T03:46:35Z |
| format | Article |
| id | doaj.art-9cb9dc7ed2a54a3688974aa752578f29 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-03-10T03:46:35Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-9cb9dc7ed2a54a3688974aa752578f292023-11-23T11:19:43ZengMDPI AGISPRS International Journal of Geo-Information2220-99642022-04-0111529010.3390/ijgi11050290GeoSDVA: A Semi-Supervised Dirichlet Variational Autoencoder Model for Transportation Mode IdentificationXiaoxi Zhang0Yuan Gao1Xin Wang2Jun Feng3Yan Shi4School of Information Science and Technology, Northwest University, Xi’an 710127, ChinaSchool of Economics and Management, Northwest University, Xi’an 710127, ChinaSchool of Information Science and Technology, Northwest University, Xi’an 710127, ChinaSchool of Information Science and Technology, Northwest University, Xi’an 710127, ChinaSchool of Foreign Languages, Northwest University, Xi’an 710127, ChinaInferring the transportation modes of travelers is an essential part of intelligent transportation systems. With the development of mobile services, it is easy to effectively obtain massive location readings of travelers with GPS-enabled smart devices, such as smartphones. These readings make understanding human activities very convenient. Therefore, how to automatically infer transportation modes from these massive readings has come into the spotlight. The existing methods for transportation mode identification are usually based on supervised learning. However, the raw GPS readings do not contain any labels, and it is expensive and time-consuming to annotate sufficient samples for training supervised learning-based models. In addition, not enough attention is paid to the problem that GPS readings collected in urban areas are affected by surrounding geographic information (e.g., the level of road transportation or the distribution of stations). To solve this problem, a geographic information-fused semi-supervised method based on a Dirichlet variational autoencoder, named GeoSDVA, is proposed in this paper for transportation mode identification. GeoSDVA first fuses the motion features of the GPS trajectories with the nearby geographic information. Then, both labeled and unlabeled trajectories are used to train the semi-supervised model based on the Dirichlet variational autoencoder architecture for transportation mode identification. Experiments on three real GPS trajectory datasets demonstrate that GeoSDVA can train an excellent transportation mode identification model with only a few labeled trajectories.https://www.mdpi.com/2220-9964/11/5/290transportation mode identificationdeep learningsemi-supervised learningvariational autoencoder |
| spellingShingle | Xiaoxi Zhang Yuan Gao Xin Wang Jun Feng Yan Shi GeoSDVA: A Semi-Supervised Dirichlet Variational Autoencoder Model for Transportation Mode Identification ISPRS International Journal of Geo-Information transportation mode identification deep learning semi-supervised learning variational autoencoder |
| title | GeoSDVA: A Semi-Supervised Dirichlet Variational Autoencoder Model for Transportation Mode Identification |
| title_full | GeoSDVA: A Semi-Supervised Dirichlet Variational Autoencoder Model for Transportation Mode Identification |
| title_fullStr | GeoSDVA: A Semi-Supervised Dirichlet Variational Autoencoder Model for Transportation Mode Identification |
| title_full_unstemmed | GeoSDVA: A Semi-Supervised Dirichlet Variational Autoencoder Model for Transportation Mode Identification |
| title_short | GeoSDVA: A Semi-Supervised Dirichlet Variational Autoencoder Model for Transportation Mode Identification |
| title_sort | geosdva a semi supervised dirichlet variational autoencoder model for transportation mode identification |
| topic | transportation mode identification deep learning semi-supervised learning variational autoencoder |
| url | https://www.mdpi.com/2220-9964/11/5/290 |
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