PMID- 37809690
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231018
IS  - 2405-8440 (Print)
IS  - 2405-8440 (Electronic)
IS  - 2405-8440 (Linking)
VI  - 9
IP  - 9
DP  - 2023 Sep
TI  - STHSGCN: Spatial-temporal heterogeneous and synchronous graph convolution network 
      for traffic flow prediction.
PG  - e19927
LID - 10.1016/j.heliyon.2023.e19927 [doi]
LID - e19927
AB  - Nowadays, as a crucial component of intelligent transportation systems, traffic 
      flow prediction has received extensive concern. However, most of the existing 
      studies extracted spatial-temporal features with modules that do not 
      differentiate with time and space, and failed to consider spatial-temporal 
      heterogeneities. Furthermore, although previous works have achieved synchronous 
      modeling of spatial-temporal dependencies, the consideration of temporal 
      causality is still lacking in their graph structures. To address these 
      shortcomings, a spatial-temporal heterogeneous and synchronous graph convolution 
      network (STHSGCN) is proposed for traffic flow prediction. To be specific, 
      separate dilated causal spatial-temporal synchronous graph convolutional networks 
      (DCSTSGCNs) for various node clusters are designed to reflect spatial 
      heterogeneity, different dilated causal spatial-temporal synchronous graph 
      convolutional modules (DCSTSGCMs) for diverse time steps are deployed to take 
      account of temporal heterogeneity. In addition, causal spatial-temporal 
      synchronous graph (CSTSG) is proposed to capture temporal causality in 
      spatial-temporal synchronous learning. We further conducted extensive experiments 
      on four real-world datasets, and the results verified the consistent superiority 
      of our proposed approach compared with various existing baselines.
CI  - (c) 2023 Published by Elsevier Ltd.
FAU - Yu, Xian
AU  - Yu X
AD  - School of Information Science and Technology, Nantong University, Nantong 226019, 
      China.
FAU - Bao, Yin-Xin
AU  - Bao YX
AD  - School of Information Science and Technology, Nantong University, Nantong 226019, 
      China.
FAU - Shi, Quan
AU  - Shi Q
AD  - School of Information Science and Technology, Nantong University, Nantong 226019, 
      China.
AD  - School of Transportation and Civil Engineering, Nantong University, Nantong 
      226019, China.
LA  - eng
PT  - Journal Article
DEP - 20230911
PL  - England
TA  - Heliyon
JT  - Heliyon
JID - 101672560
PMC - PMC10559355
OTO - NOTNLM
OT  - Causality
OT  - Graph convolution
OT  - Heterogeneity
OT  - Traffic flow prediction
COIS- The authors declare that they have no known competing financial interests or 
      personal relationships that could have appeared to influence the work reported in 
      this paper.
EDAT- 2023/10/09 06:42
MHDA- 2023/10/09 06:43
PMCR- 2023/09/11
CRDT- 2023/10/09 05:56
PHST- 2023/03/29 00:00 [received]
PHST- 2023/09/05 00:00 [revised]
PHST- 2023/09/06 00:00 [accepted]
PHST- 2023/10/09 06:43 [medline]
PHST- 2023/10/09 06:42 [pubmed]
PHST- 2023/10/09 05:56 [entrez]
PHST- 2023/09/11 00:00 [pmc-release]
AID - S2405-8440(23)07135-9 [pii]
AID - e19927 [pii]
AID - 10.1016/j.heliyon.2023.e19927 [doi]
PST - epublish
SO  - Heliyon. 2023 Sep 11;9(9):e19927. doi: 10.1016/j.heliyon.2023.e19927. eCollection 
      2023 Sep.