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 University of Washington advised by Prof. Yinhai Wang. I received my Master degree in Software Engineering from Peking University advised by Dr. Ying Huang and Prof. Tong Mo and earned a Bachelor degree in Software Engineering from Beihang University. During my Master study, I worked as a visiting student in the College of Electrical Engineering and Computer Science at National Taiwan University advised by Prof. Hsin-Mu (Michael) Tsai.

Curriculum Vitae: [CV]

News

  • 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 will give a presentation 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]
  • New! May 2019. I’m honoured to be a member of Transportation Research Board (TRB) Standing Committee on Intelligent Transportation Systems – AHB15
  • New! Apr. 2019. I’m honoured to be a member of Transportation Research Board (TRB) Standing Committee on Geospatial Data Acquisition Technologies - AFB80
  • New! Jan. 2019. Three papers are accepted by TRB Aunual Meeting 2019. I will give two oral presentations [1,2]

Research Focus

  • Basic and Applied Research on Machine Learning and Deep Learning
  • Urban Computing
  • Spatiotemporal (Traffic State) Data Modeling/Prediction
  • Transportation Data Science
  • Connected Vehicles and Autonomous Driving
  • Intelligent Transportation Systems

Recent Papers

  • Cui Z, Lin L, Pu Z, Wang Y. (2020) Graph Markov Network for Traffic Forecasting with Missing Data. Transportation Research Board 99th Annual Meeting (accepted)
  • Cui Z, Fu M, Zhu M, Ban X, Wang Y. (2020) Transportation Artificial Intelligence Platform for Traffic Forecasting. Transportation Research Board 99th Annual Meeting (accepted)
  • 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 (accepted) [arXiv][code]
  • Cui Z, Long Y*. (2019) Perspectives on Stability and Mobility of Transit Passenger’s Travel Behaviour through Smart Card Data. IET Intelligent Transport Systems (in press). (doi: 10.1049/iet-its.2019.0212)
  • Cui Z, Henrickson K, Biancardo S, Pu Z, Wang Y*. (2019) Establishing a Multi-Source Data Integration Framework for Transportation Data Analytics. Journal of Transportation Engineering, Part A: Systems (accepted). (doi: 10.1061/JTEPBS.0000331)

Selected Research on Deep Learning based Traffic Prediction and Data Imputation

  • 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]