Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Zheyi Pan"'
Publikováno v:
Human-Centric Intelligent Systems, Vol 4, Iss 3, Pp 406-416 (2024)
Abstract Government collaboration tasks are integral for grassroots governance and essential for government administration. Large-scale government collaboration tasks often involve multiple departments working together to solve complex tasks that req
Externí odkaz:
https://doaj.org/article/18e8c5ef817b4f69890ba417f795cba2
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 34:1462-1476
Predicting urban traffic is of great importance to intelligent transportation systems and public safety, yet is very challenging in three aspects: 1) complex spatio-temporal correlations of urban traffic, including spatial correlations between locati
Autor:
Yuxuan Liang, Kun Ouyang, Yiwei Wang, Zheyi Pan, Yifang Yin, Hongyang Chen, Junbo Zhang, Yu Zheng, David S. Rosenblum, Roger Zimmermann
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. :1-15
Publikováno v:
Artificial Intelligence. 318:103899
Autor:
Zhaoyuan Wang, Shun Chen, Shenggong Ji, Zheyi Pan, Chuishi Meng, Junbo Zhang, Tianrui Li, Yu Zheng
Publikováno v:
Knowledge-Based Systems. 268:110474
Autor:
Tianfu He, Zheyi Pan, Junbo Zhang, Yu Zheng, Hui He, Chuishi Meng, Chen Guochun, Yuan Ye, Huajun He, Sijie Ruan, Yexin Li, Huimin Ren, Ruiyuan Li, Jie Bao
Publikováno v:
SIGSPATIAL/GIS
People often refer to a place of interest (POI) by an alias. In e-commerce scenarios, the POI alias problem affects the quality of the delivery address of online orders, bringing substantial challenges to intelligent logistics systems and market deci
Publikováno v:
WWW
Spatio-temporal graphs are important structures to describe urban sensory data, e.g., traffic speed and air quality. Predicting over spatio-temporal graphs enables many essential applications in intelligent cities, such as traffic management and envi
Autor:
Zhaoyuan Wang, Zheyi Pan, Shun Chen, Shenggong Ji, Xiuwen Yi, Junbo Zhang, Jingyuan Wang, Zhiguo Gong, Tianrui Li, Yu Zheng
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. :1-1
Publikováno v:
CIKM
Predicting urban flow is essential for city risk assessment and traffic management, which profoundly impacts people's lives and property. Recently, some deep learning models, focusing on capturing spatio-temporal (ST) correlations between urban regio