TY  - JOUR
PY  - 2022//
TI  - MGCAF: a novel multigraph cross-attention fusion method for traffic speed prediction
JO  - International journal of environmental research and public health
A1  - Ma, Tian
A1  - Wei, Xiaobao
A1  - Liu, Shuai
A1  - Ren, Yilong
SP  - e14490
EP  - e14490
VL  - 19
IS  - 21
N2  - Traffic speed prediction is an essential part of urban transportation systems that contributes to minimizing the environmental pollution caused by vehicle emissions. The existing traffic speed prediction studies have achieved good results, but some challenges remain. Most previously developed methods only account for road network characteristics such as distance while ignoring road directions and time patterns, resulting in lower traffic speed prediction accuracy. To address this issue, we propose a novel model that utilizes multigraph and cross-attention fusion (MGCAF) mechanisms for traffic speed prediction. We construct three graphs for distances, position relationships, and temporal correlations to adequately capture road network properties. Furthermore, to adaptively aggregate multigraph features, a multigraph attention mechanism is embedded into the network framework, enabling it to better connect the traffic features between the temporal and spatial domains. Experiments are performed on real-world datasets, and the results demonstrate that our method achieves positive performance and outperforms other baselines.<p />  <p>Language: en</p>
LA  - en
SN  - 1661-7827
UR  - http://dx.doi.org/10.3390/ijerph192114490
ID  - ref1
ER  -