Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Chuanpan Zheng"'
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 23:11314-11324
Nowadays, a large number of sensors are equipped on mobile or stationary platforms, which continuously generate geo-tagged and time-stamped readings (i.e., geo-sensory data) that contain rich information about the surrounding environment. These data
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7fb58fb4913e5598046e7707e2fc461b
https://doi.org/10.1109/tits.2021.3102747
https://doi.org/10.1109/tits.2021.3102747
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 23:7743-7758
Accurate spatio-temporal traffic forecasting serves as the basis of dynamic strategy and applications for intelligent transportation systems, which is of great practical significance for improving traffic safety and mitigating road congestion. Recent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d93ddb944120e9f7f5f28416608fb8c2
https://doi.org/10.1109/tits.2021.3072118
https://doi.org/10.1109/tits.2021.3072118
Spatio-temporal kriging is an important problem in web and social applications, such as Web or Internet of Things, where things (e.g., sensors) connected into a web often come with spatial and temporal properties. It aims to infer knowledge for (the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d0ccd7cc63e6a3d50398bcdf9eb49b29
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 21:3744-3755
Deep learning techniques have been widely applied to traffic flow prediction, considering underlying routine patterns, and multiple context factors (e.g., time and weather). However, the complex spatio-temporal dependencies between inherent traffic p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1495f23b500bb167f5f40adfc35a7b52
https://doi.org/10.1109/tits.2019.2932785
https://doi.org/10.1109/tits.2019.2932785
Publikováno v:
AAAI
Long-term traffic prediction is highly challenging due to the complexity of traffic systems and the constantly changing nature of many impacting factors. In this paper, we focus on the spatio-temporal factors, and propose a graph multi-attention netw
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bf36a041fa5f2a566af668c37519261b
http://arxiv.org/abs/1911.08415
http://arxiv.org/abs/1911.08415
Publikováno v:
2018 Sixth International Conference on Advanced Cloud and Big Data (CBD).
Traffic accident is considered as one of main causes for traffic congestion in cities. There are many causal factors that may give rise to traffic accidents, e.g. driver characteristics, road conditions, traffic flows and weather conditions, etc. Due
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::156a726c7372b2ad86d1be2009098450
https://doi.org/10.1109/cbd.2018.00065
https://doi.org/10.1109/cbd.2018.00065
Publikováno v:
ICWS
Unlicensed taxis are widely considered as major obstacles to city traffic regulation and public safety. Thus, many governments have issued restrictions for car-hailing services and alleged that the use of unlicensed vehicles was illegal. However, it
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::750ebd59a84e40b98087c22117bd38e7
https://doi.org/10.1109/icws.2017.106
https://doi.org/10.1109/icws.2017.106