# Zhiyong Cui

I am a PhD Candidate in the Smart Transportation Application and Research Lab (STAR Lab) in the Department of Civil & Environmental Engineering at the University of Washington advised by Prof. Yinhai Wang. I received my Master degree in Software Engineering from Peking University and my Bachelor degree in Software Engineering from Beihang University. I was a visiting master student in the College of Electrical Engineering and Computer Science at National Taiwan University advised by Prof. Hsin-Mu (Michael) Tsai.

## Research Focus

- Basic and Applied Research on Machine Learning and Deep Learning
- Urban Computing & Transportation Data Science
- Spatiotemporal Data Modeling/Imputation/Prediction
- Connected Vehicles and Autonomous Driving

## News Over the Past Half Year

- New! Mar. 2020. My first-author paper “Learning Traffic as a Graph: A Gated Graph Wavelet Recurrent Neural Network for Network-scale Traffic Prediction” is accepted by
*Transportation Research Part C: Emerging Technologies* - New! Mar. 2020. Our paper “Forecasting Transportation Network Speed Using Deep Capsule Networks with Nested LSTM Models “ is accepted by
*IEEE Transaction on Intelligent Transportation Systems* - New! Feb. 2020. Our paper “An Advanced Framework for Microscopic and Lane-level Macroscopic Traffic Parameters Estimation from UAV Video” is accepted by
*IET Intelligent Transport Systems* - New! Feb. 2020. Our paper “Two-Stream Multi-Channel Convolutional Neural Network (TM-CNN) for Multi-Lane Traffic Speed Prediction Considering Traffic Volume Impact” is accepted by
*Transportation Research Record* - New! Jan. 2020. I’m honoured to win the Student Paper Award from the Transportation Statistics Interest Group of American Statistical Association (ASA). [Paper]
- New! Oct. 2019. Two first-author and two co-authored papers are accepted for presentation at TRB 2020.
- New! Oct. 2019. I win the Second Prize in the 2019 PacTrans Annual Conference best poster contest.
- New! Oct. 2019. I give a talk at INFORMS 2019 entitled: “Learning Traffic as a Graph: Graph based Neural networks for Network-scale Traffic Prediction” [link]
- New! Sept. 2019. My first-author paper “Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting” is accepted by
*IEEE Transaction on Intelligent Transportation Systems*[arXiv][code] - New! Sept. 2019. My first-author paper “Establishing a Multi-Source Data Integration Framework for Transportation Data Analytics” is accepted by
*Journal of Transportation Engineering, Part A: Systems* - New! Aug. 2019. My first-author paper “Perspectives on Stability and Mobility of Transit Passenger’s Travel Behavior through Smart Card Data” is accepted by
*IET Intelligent Transport Systems*[doi] - New! June 2019. Our invited review article “The Development of Smart Transportation in Urgent Need of Transportation Data Science (in Chinese)” is publised at
*Urban Transport of China*[doi]

## Selected Research on Deep Learning based Traffic Prediction and Data Imputation

**Cui Z**, Lin L, Pu Z, Wang Y. (2020) Graph Markov Network for Traffic Forecasting with Missing Data.*Transportation Research Board 99th Annual Meeting*[arXiv] (**ASA TSIG Student Paper Award**)**Cui Z**, Ke R, Pu Z, Ma X, Wang Y*. (2020) Learning Traffic as a Graph: A Gated Graph Wavelet Recurrent Neural Network for Network-scale Traffic Prediction.*Transportation Research Part C: Emerging Technologies***Cui, Z.**, Henrickson, K., Ke, R., & Wang, Y.* (2019). Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting.*IEEE Transaction on Intelligent Transportation Systems*[arXiv][code][data]

**Cui, Z.**, Ke, R., & Wang, Y.* (2018). Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction. (submitted to*IEEE Transaction on Intelligent Transportation Systems*; under review). [arXiv][code][data]Liang, Y.,

**Cui, Z.**, Tian, Y., Chen, H., & Wang, Y.* (2018). A Deep Generative Adversarial Architecture for Network-Wide Spatial-Temporal Traffic State Estimation.*Transportation Research Record*. [arXiv]