المؤلفون: HeounMo Go, SangHyun Park

المصدر: Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)

مصطلحات موضوعية: Crowd flow prediction, Deep learning, Spatio-temporal data mining, Medicine, Science

وصف الملف: electronic resource

Relation: https://doaj.org/toc/2045-2322

URL الوصول: https://doaj.org/article/3b6a114c6e6f4af39687636bb95ce5e7

المؤلفون: Genan Dai, Hu Huang, Xiaojiang Peng, Bowen Zhang, Xianghua Fu

المصدر: Mathematics, Vol 12, Iss 11, p 1739 (2024)

مصطلحات موضوعية: crowd flow prediction, graph convolutional network, time-aware gating mechanism, Mathematics, QA1-939

وصف الملف: electronic resource

Relation: https://www.mdpi.com/2227-7390/12/11/1739; https://doaj.org/toc/2227-7390

URL الوصول: https://doaj.org/article/ea8607af4ed24242bf3f1b388626a91b

المؤلفون: Du He, Jing Jia, Yaoqing Wang, Lan You, Zhijun Chen, Jiawen Li, Qiyao Wu, Yongsen Wang

المصدر: Electronics; Volume 12; Issue 10; Pages: 2255

مصطلحات موضوعية: crowd flow prediction, region association digraph, spatio-temporal hybrid neural network, urban crowd flow, deep learning, graph embedding algorithm, crowd flow feature

وصف الملف: application/pdf

Relation: Computer Science & Engineering; https://dx.doi.org/10.3390/electronics12102255

الاتاحة: https://doi.org/10.3390/electronics12102255

المؤلفون: Qiaoshuang Teng, Shangyu Sun, Weidong Song, Jinzhong Bei, Chongchang Wang

المصدر: Electronics, Vol 13, Iss 1, p 172 (2023)

مصطلحات موضوعية: indoor regional crowd flow prediction, crowd flow trajectories, deep learning, spatial-temporal attention mechanism, feature fusion method, Electronics, TK7800-8360

Relation: https://www.mdpi.com/2079-9292/13/1/172; https://doaj.org/toc/2079-9292; https://doaj.org/article/d95abe9a7e9c48a18a7042d4c682757f

الاتاحة: https://doi.org/10.3390/electronics13010172
https://doaj.org/article/d95abe9a7e9c48a18a7042d4c682757f

المؤلفون: Bing Yang, Yan Kang, Hao Li, Yachuan Zhang, Yan Yang, Lan Zhang

المصدر: IET Intelligent Transport Systems, Vol 14, Iss 5, Pp 313-322 (2020)

مصطلحات موضوعية: crowd flow prediction, deep learning methods, traffic flow characteristics, traffic trajectory, traffic duration, inverted residual convolution structures, Transportation engineering, TA1001-1280, Electronic computers. Computer science, QA75.5-76.95

وصف الملف: electronic resource

Relation: https://doaj.org/toc/1751-956X; https://doaj.org/toc/1751-9578

URL الوصول: https://doaj.org/article/6884f6d362dc481bb54c6f1d6f33a9d2

المؤلفون: Lablack Mourad, Heng Qi, Yanming Shen, Baocai Yin

المصدر: IEEE Access, Vol 7, Pp 175159-175165 (2019)

مصطلحات موضوعية: Deep learning, crowd flow prediction, spatio-temporal modelling, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

وصف الملف: electronic resource

Relation: https://ieeexplore.ieee.org/document/8889654/; https://doaj.org/toc/2169-3536

URL الوصول: https://doaj.org/article/6f11e9534c2947ce9b9169bc94f0c3b1

المؤلفون: Wei Li, Wei Tao, Junyang Qiu, Xin Liu, Xingyu Zhou, Zhisong Pan

المصدر: IEEE Access, Vol 7, Pp 140488-140498 (2019)

مصطلحات موضوعية: Data mining, spatiotemporal modeling, crowd flow prediction, densely connected convolutional network, long short-term memory, attention mechanism, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

وصف الملف: electronic resource

Relation: https://ieeexplore.ieee.org/document/8850005/; https://doaj.org/toc/2169-3536

URL الوصول: https://doaj.org/article/234d08d6146440c681266db079314000

المؤلفون: Enze YANG, Kai FANG, Shuoyan LIU, Yuxin LIU

المصدر: IEICE Transactions on Information and Systems. 2021, E104.D(10):1780

المؤلفون: BarlacchiGianni, PappalardoLuca, LucaMassimiliano, LepriBruno

المصدر: ACM computing surveys 55 (2023). doi:10.1145/3485125

مصطلحات موضوعية: FOS: Computer and information sciences, I.2, Computer Science - Machine Learning, Mobility flows, Artificial intelligence, Human mobility, General Computer Science, Computer Science - Artificial Intelligence, Computer science, Next-location prediction, Trajectory, Trajectory generation, 02 engineering and technology, Theoretical Computer Science, Machine Learning (cs.LG), Urban planning, Disease spreading, 020204 information systems, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Datasets, Social and Information Networks (cs.SI), business.industry, Deep learning, Next location prediction, Computer Science - Social and Information Networks, Data science, Crowd flow prediction, Artificial Intelligence (cs.AI), 020201 artificial intelligence & image processing, business

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b56383d78d52f5907c3e33a95616980
https://openportal.isti.cnr.it/doc?id=people______::eff1dcf983f709a0bebab238576dabeb

المؤلفون: Tang, Gaozhong, Li, Bo, Dai, Hong-Ning, Zheng, Xi

المصدر: Tang , G , Li , B , Dai , H-N & Zheng , X 2022 , ' SPRNN : a spatial–temporal recurrent neural network for crowd flow prediction ' , Information Sciences , vol. 614 , pp. 19-34 . https://doi.org/10.1016/j.ins.2022.09.053

مصطلحات موضوعية: Crowd flow prediction, Spatial feature, Temporal feature, Road structural information, Gated recurrent unit

الاتاحة: https://researchers.mq.edu.au/en/publications/bf432024-e9d1-40e6-8819-867ddf2fcca2
https://doi.org/10.1016/j.ins.2022.09.053
http://www.scopus.com/inward/record.url?scp=85139225309&partnerID=8YFLogxK
http://purl.org/au-research/grants/arc/DP210102447
http://purl.org/au-research/grants/arc/LP190100676

المؤلفون: J Ma, J Chan, Sutharshan Rajasegarar, G Ristanoski, C Leckie

مصطلحات موضوعية: crowd flow prediction, origin-destination, global attention, 3D convolutional networks, CORE2020 A, Faculty of Science Engineering and Built Environment, School of Information Technology

Relation: http://hdl.handle.net/10536/DRO/DU:30148663

الاتاحة: http://hdl.handle.net/10536/DRO/DU:30148663
https://figshare.com/articles/conference_contribution/Multi-attention_3D_residual_neural_network_for_origin-destination_crowd_flow_prediction/20674950

المؤلفون: Zhisong Pan, Xingyu Zhou, Junyang Qiu, Wei Li, Xin Liu, Wei Tao

المصدر: IEEE Access, Vol 7, Pp 140488-140498 (2019)

مصطلحات موضوعية: General Computer Science, Computer science, Feature extraction, 010501 environmental sciences, computer.software_genre, 01 natural sciences, Crowds, Urbanization, 0502 economics and business, General Materials Science, Autoregressive integrated moving average, Baseline (configuration management), Data mining, 0105 earth and related environmental sciences, 050210 logistics & transportation, business.industry, Deep learning, 05 social sciences, General Engineering, spatiotemporal modeling, crowd flow prediction, lcsh:Electrical engineering. Electronics. Nuclear engineering, Artificial intelligence, long short-term memory, attention mechanism, business, lcsh:TK1-9971, computer, densely connected convolutional network

URL الوصول: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e776dfd23f2367d8589fc5f62d838bda
https://doi.org/10.1109/access.2019.2943890

المؤلفون: Luca M., Barlacchi G., Lepri B., Pappalardo L.

المصدر: Research report, SoBigData++, 2020

مصطلحات موضوعية: Deep Learning, Artificial Intelligence, Trajectory, Datasets, Human Mobility, Next-location Prediction, Trajectory Generation, Crowd Flow Prediction, Mobility Flows

URL الوصول: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::b8286bd82d7dded719f6fd1467728c40
https://openportal.isti.cnr.it/doc?id=people______::b8286bd82d7dded719f6fd1467728c40

المؤلفون: Jiaman MA

مصطلحات موضوعية: Data mining and knowledge discovery, Deep learning, Crowd flow prediction, Intelligent public transport systems, Public transport station planning, Travel time prediction

Relation: https://figshare.com/articles/thesis/Improving_intelligent_public_transport_systems_for_smart_cities/27596526

الاتاحة: https://doi.org/10.25439/rmt.27596526
https://figshare.com/articles/thesis/Improving_intelligent_public_transport_systems_for_smart_cities/27596526

المؤلفون: Li, Wei, Tao, Wei, Qiu, Junyang, Liu, Xin, Zhou, Xingyu, Pan, Zhisong

مصطلحات الفهرس: Science & Technology, Technology, Computer Science, Information Systems, Engineering, Electrical & Electronic, Telecommunications, Computer Science, Engineering, Spatiotemporal phenomena, Correlation, Feature extraction, Predictive models, Urban areas, Meteorology, Time series analysis, Data mining, spatiotemporal modeling, crowd flow prediction, densely connected convolutional network, long short-term memory, attention mechanism, MODELS, Journal Article

URL: http://hdl.handle.net/10536/DRO/DU:30133128
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8850005
issn: 2169-3536
http://www.dx.doi.org/10.1109/ACCESS.2019.2943890
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8850005
IEEE Access