A dynamical spatial-temporal graph neural network for traffic demand prediction
F Huang, P Yi, J Wang, M Li, J Peng, X Xiong - Information Sciences, 2022 - Elsevier
Traffic demand prediction is significant and practical in the resource scheduling of
transportation application systems. Meanwhile, it remains a challenging topic due to the …
transportation application systems. Meanwhile, it remains a challenging topic due to the …
Directed graph contrastive learning
Abstract Graph Contrastive Learning (GCL) has emerged to learn generalizable
representations from contrastive views. However, it is still in its infancy with two concerns: 1) …
representations from contrastive views. However, it is still in its infancy with two concerns: 1) …
Combined deep prior with low-rank tensor SVD for thick cloud removal in multitemporal images
The thick cloud coverage phenomenon severely disturbs optical satellite observation
missions (covering approximately 40–60% areas in the global scale). Therefore, the manner …
missions (covering approximately 40–60% areas in the global scale). Therefore, the manner …
Fine-grained urban flow prediction
Urban flow prediction benefits smart cities in many aspects, such as traffic management and
risk assessment. However, a critical prerequisite for these benefits is having fine-grained …
risk assessment. However, a critical prerequisite for these benefits is having fine-grained …
A novel hybrid short-term electricity forecasting technique for residential loads using Empirical Mode Decomposition and Extreme Learning Machines
In recent years, the residential load forecasting problem has been gaining renewed interest
due to the advent of Smart Meters and Data Analytics. A novel hybrid method based on …
due to the advent of Smart Meters and Data Analytics. A novel hybrid method based on …
Forecasting fine-grained urban flows via spatio-temporal contrastive self-supervision
As a critical task of the urban traffic services, fine-grained urban flow inference (FUFI)
benefits in many fields including intelligent transportation management, urban planning …
benefits in many fields including intelligent transportation management, urban planning …
Copy Move Forgery Detection based on double matching
Q Lyu, J Luo, K Liu, X Yin, J Liu, W Lu - Journal of Visual Communication …, 2021 - Elsevier
Copy Move is a technique widespreadly used in digital image tampering, meaning Copy
Move Forgery Detection (CMFD) is still a significant research. In this paper, a novel CMFD …
Move Forgery Detection (CMFD) is still a significant research. In this paper, a novel CMFD …
Learning spatial-temporal dynamics and interactivity for short-term passenger flow prediction in urban rail transit
J Wu, X Li, D He, Q Li, W Xiang - Applied Intelligence, 2023 - Springer
Accurate short-term passenger flow prediction in urban rail transit is critical in ensuring the
stable operation of urban rail systems. However, accurate passenger flow prediction still …
stable operation of urban rail systems. However, accurate passenger flow prediction still …
Missing value imputation for multi-view urban statistical data via spatial correlation learning
As a developing trend of urbanization, massive amounts of urban statistical data with
multiple views (eg, views of Population and Economy) are increasingly collected and …
multiple views (eg, views of Population and Economy) are increasingly collected and …
Fine-grained crowd distribution forecasting with multi-order spatial interactions using mobile phone data
Fine-grained crowd distribution forecasting benefits smart transportation operations and
management, such as public transport dispatch, traffic demand prediction, and transport …
management, such as public transport dispatch, traffic demand prediction, and transport …