iTensor II: intelligent traffic estimation and prediction for cities

In order to support intelligent traffic signal system, iTensor also developed estimation technologies for real-time traffic states and prediction algorithms since 2016. The research has led to several later publications:

J. Jin and X. Ma, "A non-parametric Bayesian framework for traffic-state estimation at signalized intersections," Information Sciences, vol. 498, pp. 21-40, 2019.

M. Sederlin, X. Ma and J. Jin, "A hybrid modelling approach for traffic state estimation at signalized intersections", the 24th IEEE Conference on Intelligent Transportation Systems, 2021. 

J. Jin, D. Rong, T. Zhang, Q. Ji, H. Guo, Y. Lv, X. Ma, F. Wang,  A GAN-based short-term link traffic prediction approach for urban road networks under a parallel learning framework, IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 9, pp. 16185-16196, Sept. 2022.

Q. Ji, J. Jin, Y. Qin, X. Ma, Y. Zhang, GraphPro: A Graph-Based Proactive Prediction Approach for Link Speeds on Signalized Urban Traffic Network,  the 2022 IEEE International Conference on System, Man and Cybernetics (IEEE SMC2022), 2022.