Zhiyong Cui
Zhiyong Cui
Postdoctoral Research Associate, University of Washington
在 uw.edu 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction
Z Cui, R Ke, Z Pu, Y Wang
6th International Workshop on Urban Computing (UrbComp 2017), Halifax, NS …, 2017
2582017
Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting
Z Cui, K Henrickson, R Ke, Y Wang
IEEE Transactions on Intelligent Transportation Systems 21 (11), 4883-4894, 2019
208*2019
Real-time bidirectional traffic flow parameter estimation from aerial videos
R Ke, Z Li, S Kim, J Ash, Z Cui, Y Wang
IEEE Transactions on Intelligent Transportation Systems 18 (4), 890-901, 2016
892016
Characterizing channel fading in vehicular visible light communications with video data
Z Cui, C Wang, HM Tsai
2014 IEEE Vehicular Networking Conference (VNC), 226-229, 2014
302014
A deep generative adversarial architecture for network-wide spatial-temporal traffic-state estimation
Y Liang, Z Cui, Y Tian, H Chen, Y Wang
Transportation Research Record 2672 (45), 87-105, 2018
242018
Two-stream multi-channel convolutional neural network for multi-lane traffic speed prediction considering traffic volume impact
R Ke, W Li, Z Cui, Y Wang
Transportation Research Record 2674 (4), 459-470, 2020
232020
Forecasting transportation network speed using deep capsule networks with nested LSTM models
X Ma, H Zhong, Y Li, J Ma, Z Cui, Y Wang
IEEE Transactions on Intelligent Transportation Systems, 2020
212020
Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values
Z Cui, R Ke, Z Pu, Y Wang
Transportation Research Part C: Emerging Technologies 118, 102674, 2020
192020
A vision-based hierarchical framework for autonomous front-vehicle taillights detection and signal recognition
Z Cui, SW Yang, HM Tsai
2015 IEEE 18th International Conference on Intelligent Transportation …, 2015
172015
Digital roadway interactive visualization and evaluation network applications to WSDOT operational data usage.
Y Wang, W Zhang, K Henrickson, R Ke, Z Cui
Washington (State). Dept. of Transportation, 2016
162016
Deep learning architecture for short-term passenger flow forecasting in urban rail transit
J Zhang, F Chen, Z Cui, Y Guo, Y Zhu
IEEE Transactions on Intelligent Transportation Systems, 2020
152020
Complement or competitior? comparing car2go and transit travel times, prices, and usage patterns in seattle
X Wang, D MacKenzie, Z Cui
Transportation Research Board 96th Annual MeetingTransportation Research Board, 2017
152017
New progress of DRIVE Net: An E-science transportation platform for data sharing, visualization, modeling, and analysis
Z Cui, S Zhang, KC Henrickson, Y Wang
2016 IEEE International Smart Cities Conference (ISC2), 1-2, 2016
122016
Learning traffic as a graph: A gated graph wavelet recurrent neural network for network-scale traffic prediction
Z Cui, R Ke, Z Pu, X Ma, Y Wang
Transportation Research Part C: Emerging Technologies 115, 102620, 2020
112020
Graph Markov network for traffic forecasting with missing data
Z Cui, L Lin, Z Pu, Y Wang
Transportation Research Part C: Emerging Technologies 117, 102671, 2020
82020
Evaluating the impacts of grades on vehicular speeds on interstate highways
X Chen, Z Li, Y Wang, Z Cui, C Shi, H Wu
PloS one 12 (9), e0184142, 2017
82017
Advanced framework for microscopic and lane-level macroscopic traffic parameters estimation from UAV video
R Ke, S Feng, Z Cui, Y Wang
IET Intelligent Transport Systems 14 (7), 724-734, 2020
62020
Characterizing evolution of extreme public transit behavior using smart card data
Z Cui, Y Long, R Ke, Y Wang
2015 IEEE First International Smart Cities Conference (ISC2), 1-6, 2015
62015
Perspectives on stability and mobility of passenger's travel behavior through smart card data
Z Cui, Y Long
UrbComp15, Proceedings of the 4nd ACM SIGKDD International Workshop on …, 2015
62015
Deep Stacked Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction. ArXiv e-prints
Z Cui, R Ke, Y Wang
arXiv preprint arXiv:1801.02143, 2018
52018
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