TY - GEN
T1 - STS2ANet
T2 - 29th International Conference on Database Systems for Advanced Applications, DASFAA 2024
AU - Wang, Haoli
AU - Xia, Jiangnan
AU - Yang, Yu
AU - Wang, Senzhang
AU - Cao, Jiannong
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - Accurately predicting Origin-Destination(OD) traffic flow is crucial in traffic planning, vehicle dispatching, user travel, etc. However, existing works mainly focus on modeling prolonged spatial-temporal trends of traffic flow, neglecting the divergence of spatial and temporal patterns at cross-day periods, especially the shift between weekdays and weekends. In this paper, we propose a Spatio-temporal Synchronized Sliding Attention Network (STS2ANet) to tackle this issue for accurate OD prediction. Specifically, we devise a Sliding Attention layer (SA) to learn the divergence of temporal traffic flow patterns at cross-day periods. Additionally, a Dynamic Graph Embedding module (DE) is proposed to properly learn the cross-day changes in spatial patterns of traffic flow. Notably, STS2ANet simultaneously learns the tightly coupled spatial-temporal patterns and their divergence over time, resulting in accurate OD prediction. Extensive experiments have been conducted in a real-world dataset, and the results demonstrate the performance superiority of STS2ANet against baselines.
AB - Accurately predicting Origin-Destination(OD) traffic flow is crucial in traffic planning, vehicle dispatching, user travel, etc. However, existing works mainly focus on modeling prolonged spatial-temporal trends of traffic flow, neglecting the divergence of spatial and temporal patterns at cross-day periods, especially the shift between weekdays and weekends. In this paper, we propose a Spatio-temporal Synchronized Sliding Attention Network (STS2ANet) to tackle this issue for accurate OD prediction. Specifically, we devise a Sliding Attention layer (SA) to learn the divergence of temporal traffic flow patterns at cross-day periods. Additionally, a Dynamic Graph Embedding module (DE) is proposed to properly learn the cross-day changes in spatial patterns of traffic flow. Notably, STS2ANet simultaneously learns the tightly coupled spatial-temporal patterns and their divergence over time, resulting in accurate OD prediction. Extensive experiments have been conducted in a real-world dataset, and the results demonstrate the performance superiority of STS2ANet against baselines.
KW - Attention
KW - Cross-day trend
KW - OD prediction
UR - https://www.scopus.com/pages/publications/85206383736
U2 - 10.1007/978-981-97-5552-3_12
DO - 10.1007/978-981-97-5552-3_12
M3 - Conference article published in proceeding or book
AN - SCOPUS:85206383736
SN - 9789819755516
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 186
EP - 202
BT - Database Systems for Advanced Applications - 29th International Conference, DASFAA 2024, Proceedings
A2 - Onizuka, Makoto
A2 - Lee, Jae-Gil
A2 - Tong, Yongxin
A2 - Xiao, Chuan
A2 - Ishikawa, Yoshiharu
A2 - Lu, Kejing
A2 - Amer-Yahia, Sihem
A2 - Jagadish, H.V.
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 2 July 2024 through 5 July 2024
ER -