Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Advances in Knowledge Discovery and Data Mining |
| Untertitel | 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25–28, 2023, Proceedings, Part IV |
| Herausgeber/-innen | Hisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng |
| Herausgeber (Verlag) | Springer Science and Business Media Deutschland GmbH |
| Seiten | 57-69 |
| Seitenumfang | 13 |
| ISBN (elektronisch) | 978-3-031-33383-5 |
| ISBN (Print) | 9783031333828 |
| Publikationsstatus | Veröffentlicht - 26 Mai 2023 |
| Veranstaltung | 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023 - Osaka, Japan Dauer: 25 Mai 2023 → 28 Mai 2023 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 13938 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 1611-3349 |
Abstract
Spatio-temporal traffic patterns reflecting the mobility behavior of road users are essential for learning effective general-purpose road representations. Such patterns are largely neglected in state-of-the-art road representation learning, mainly focusing on modeling road topology and static road features. Incorporating traffic patterns into road network representation learning is particularly challenging due to the complex relationship between road network structure and mobility behavior of road users. In this paper, we present TrajRNE – a novel trajectory-based road embedding model incorporating vehicle trajectory information into road network representation learning. Our experiments on two real-world datasets demonstrate that TrajRNE outperforms state-of-the-art road representation learning baselines on various downstream tasks.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
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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 IV. Hrsg. / Hisashi Kashima; Tsuyoshi Ide; Wen-Chih Peng. Springer Science and Business Media Deutschland GmbH, 2023. S. 57-69 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 13938 LNCS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Road Network Representation Learning with Vehicle Trajectories
AU - Schestakov, Stefan
AU - Heinemeyer, Paul
AU - Demidova, Elena
N1 - Funding Information: This work was partially funded by the DFG, German Research Foundation (“WorldKG”, 424985896), the Federal Ministry for Economic Affairs and Climate Action (BMWK), Germany (“d-E-mand”, 01ME19009B and “ATTENTION!”, 01MJ22012D), and DAAD, Germany (“KOALA”, 57600865).
PY - 2023/5/26
Y1 - 2023/5/26
N2 - Spatio-temporal traffic patterns reflecting the mobility behavior of road users are essential for learning effective general-purpose road representations. Such patterns are largely neglected in state-of-the-art road representation learning, mainly focusing on modeling road topology and static road features. Incorporating traffic patterns into road network representation learning is particularly challenging due to the complex relationship between road network structure and mobility behavior of road users. In this paper, we present TrajRNE – a novel trajectory-based road embedding model incorporating vehicle trajectory information into road network representation learning. Our experiments on two real-world datasets demonstrate that TrajRNE outperforms state-of-the-art road representation learning baselines on various downstream tasks.
AB - Spatio-temporal traffic patterns reflecting the mobility behavior of road users are essential for learning effective general-purpose road representations. Such patterns are largely neglected in state-of-the-art road representation learning, mainly focusing on modeling road topology and static road features. Incorporating traffic patterns into road network representation learning is particularly challenging due to the complex relationship between road network structure and mobility behavior of road users. In this paper, we present TrajRNE – a novel trajectory-based road embedding model incorporating vehicle trajectory information into road network representation learning. Our experiments on two real-world datasets demonstrate that TrajRNE outperforms state-of-the-art road representation learning baselines on various downstream tasks.
UR - http://www.scopus.com/inward/record.url?scp=85173562197&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-33383-5_5
DO - 10.1007/978-3-031-33383-5_5
M3 - Conference contribution
AN - SCOPUS:85173562197
SN - 9783031333828
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 57
EP - 69
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
T2 - 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023
Y2 - 25 May 2023 through 28 May 2023
ER -