Details
| Original language | English |
|---|---|
| Title of host publication | Advances in Knowledge Discovery and Data Mining |
| Subtitle of host publication | 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023, Proceedings, Part II |
| Editors | Hisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng |
| Place of Publication | Cham |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 79-91 |
| Number of pages | 13 |
| ISBN (electronic) | 978-3-031-33377-4 |
| ISBN (print) | 9783031333767 |
| Publication status | Published - 2023 |
| Event | 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023 - Osaka, Japan Duration: 25 May 2023 → 28 May 2023 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 13936 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
While the ever-increasing amount of available data has enabled complex machine learning algorithms in various application areas, maintaining data privacy has become more and more critical. This is especially true for mobility data. In nearly all cases, mobility data is personal and therefore the drivers’ privacy needs to be protected. However, mobility data is particularly hard to anonymize, hindering its use in machine learning algorithms to its full potential. In this paper, we address these challenges by generating synthetic vehicle trajectories that are not subject to personal data protection but have the same statistical characteristics as the originals. We present CondTraj-GAN– Conditional Trajectory Generative Adversarial Network. – a novel end-to-end framework to generate entirely synthetic vehicle trajectories. We introduce a specialized training and inference procedure that enables the application of GANs to discrete trajectory data conditioned on their sequence length. We demonstrate the data utility of the synthetic trajectories by comparing their spatial characteristics with the original dataset. Finally, our evaluation shows that CondTraj-GAN reliably outperforms state-of-the-art trajectory generation baselines.
Keywords
- Generative models, road networks, vehicle trajectories
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
Advances in Knowledge Discovery and Data Mining : 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023, Proceedings, Part II. ed. / Hisashi Kashima; Tsuyoshi Ide; Wen-Chih Peng. Cham: Springer Science and Business Media Deutschland GmbH, 2023. p. 79-91 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13936 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - CondTraj-GAN
T2 - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023
AU - Henke, Nils
AU - Wonsak, Shimon
AU - Mitra, Prasenjit
AU - Nolting, Michael
AU - Tempelmeier, Nicolas
N1 - Funding Information: This work was partially funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK) under the project “CampaNeo” (grant ID 01MD 19007A).
PY - 2023
Y1 - 2023
N2 - While the ever-increasing amount of available data has enabled complex machine learning algorithms in various application areas, maintaining data privacy has become more and more critical. This is especially true for mobility data. In nearly all cases, mobility data is personal and therefore the drivers’ privacy needs to be protected. However, mobility data is particularly hard to anonymize, hindering its use in machine learning algorithms to its full potential. In this paper, we address these challenges by generating synthetic vehicle trajectories that are not subject to personal data protection but have the same statistical characteristics as the originals. We present CondTraj-GAN– Conditional Trajectory Generative Adversarial Network. – a novel end-to-end framework to generate entirely synthetic vehicle trajectories. We introduce a specialized training and inference procedure that enables the application of GANs to discrete trajectory data conditioned on their sequence length. We demonstrate the data utility of the synthetic trajectories by comparing their spatial characteristics with the original dataset. Finally, our evaluation shows that CondTraj-GAN reliably outperforms state-of-the-art trajectory generation baselines.
AB - While the ever-increasing amount of available data has enabled complex machine learning algorithms in various application areas, maintaining data privacy has become more and more critical. This is especially true for mobility data. In nearly all cases, mobility data is personal and therefore the drivers’ privacy needs to be protected. However, mobility data is particularly hard to anonymize, hindering its use in machine learning algorithms to its full potential. In this paper, we address these challenges by generating synthetic vehicle trajectories that are not subject to personal data protection but have the same statistical characteristics as the originals. We present CondTraj-GAN– Conditional Trajectory Generative Adversarial Network. – a novel end-to-end framework to generate entirely synthetic vehicle trajectories. We introduce a specialized training and inference procedure that enables the application of GANs to discrete trajectory data conditioned on their sequence length. We demonstrate the data utility of the synthetic trajectories by comparing their spatial characteristics with the original dataset. Finally, our evaluation shows that CondTraj-GAN reliably outperforms state-of-the-art trajectory generation baselines.
KW - Generative models
KW - road networks
KW - vehicle trajectories
UR - http://www.scopus.com/inward/record.url?scp=85163292975&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-33377-4_7
DO - 10.1007/978-3-031-33377-4_7
M3 - Conference contribution
AN - SCOPUS:85163292975
SN - 9783031333767
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 79
EP - 91
BT - Advances in Knowledge Discovery and Data Mining
A2 - Kashima, Hisashi
A2 - Ide, Tsuyoshi
A2 - Peng, Wen-Chih
PB - Springer Science and Business Media Deutschland GmbH
CY - Cham
Y2 - 25 May 2023 through 28 May 2023
ER -