| A physics-informed deep learning paradigm for car-following models Z Mo, R Shi, X Di Transportation research part C: emerging technologies 130, 103240, 2021 | 99 | 2021 |
| A physics-informed deep learning paradigm for traffic state and fundamental diagram estimation R Shi, Z Mo, K Huang, X Di, Q Du IEEE Transactions on Intelligent Transportation Systems 23 (8), 11688-11698, 2021 | 72 | 2021 |
| Physics-informed deep learning for traffic state estimation: A hybrid paradigm informed by second-order traffic models R Shi, Z Mo, X Di Proceedings of the AAAI Conference on Artificial Intelligence 35 (1), 540-547, 2021 | 59 | 2021 |
| Multimedia fusion at semantic level in vehicle cooperactive perception Z Xiao, Z Mo, K Jiang, D Yang 2018 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 1-6, 2018 | 30 | 2018 |
| Cluster naturalistic driving encounters using deep unsupervised learning S Li, W Wang, Z Mo, D Zhao 2018 IEEE Intelligent Vehicles Symposium (IV), 1354-1359, 2018 | 26 | 2018 |
| CVLight: Decentralized learning for adaptive traffic signal control with connected vehicles Z Mo, W Li, Y Fu, K Ruan, X Di Transportation research part C: emerging technologies 141, 103728, 2022 | 25 | 2022 |
| Physics-informed deep learning for traffic state estimation R Shi, Z Mo, K Huang, X Di, Q Du arXiv preprint arXiv:2101.06580, 2021 | 18 | 2021 |
| Physics-informed deep learning for traffic state estimation: A survey and the outlook X Di, R Shi, Z Mo, Y Fu Algorithms 16 (6), 305, 2023 | 13 | 2023 |
| Trafficflowgan: Physics-informed flow based generative adversarial network for uncertainty quantification Z Mo, Y Fu, D Xu, X Di Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022 | 9 | 2022 |
| Uncertainty quantification of car-following behaviors: physics-informed generative adversarial networks Z Mo, X Di the 28th ACM SIGKDD in conjunction with the 11th International Workshop on …, 2022 | 8 | 2022 |
| Longitudinal control strategy for connected electric vehicle with regenerative braking in eco-approach and departure R Bautista-Montesano, R Galluzzi, Z Mo, Y Fu, R Bustamante-Bello, X Di Applied Sciences 13 (8), 5089, 2023 | 6 | 2023 |
| Quantifying uncertainty in traffic state estimation using generative adversarial networks Z Mo, Y Fu, X Di 2022 IEEE 25th International Conference on Intelligent Transportation …, 2022 | 6 | 2022 |
| Clustering of naturalistic driving encounters using unsupervised learning S Li, W Wang, Z Mo, D Zhao arXiv preprint arXiv:1802.10214, 2018 | 5 | 2018 |
| Demonstrating stability within parallel connection as a basis for building large-scale battery systems Z Li, A Zuo, Z Mo, M Lin, C Wang, J Zhang, MH Hofmann, A Jossen Cell Reports Physical Science 3 (12), 2022 | 4 | 2022 |
| Detecting mild cognitive impairment and dementia in older adults using naturalistic driving data and interaction-based classification from influence score X Di, Y Yin, Y Fu, Z Mo, SH Lo, C DiGuiseppi, DW Eby, L Hill, TJ Mielenz, ... Artificial Intelligence in Medicine 138, 102510, 2023 | 2 | 2023 |
| Extraction of V2V Encountering Scenarios from Naturalistic Driving Database Z Mo, S Li, D Yang, D Zhao arXiv preprint arXiv:1802.09917, 2018 | 2 | 2018 |
| Robust Data Sampling in Machine Learning: A Game-Theoretic Framework for Training and Validation Data Selection Z Mo, X Di, R Shi Games 14 (1), 13, 2023 | 1 | 2023 |
| Interpenetrating Cooperative Localization in Dynamic Connected Vehicle Networks H Zhao, Z Mo, M Shen, J Sun, D Zhao arXiv preprint arXiv:1804.10064, 2018 | 1 | 2018 |
| Driving Behavioral Learning Leveraging Sensing Information from Innovation Hub [Supporting Dataset] X Di, P Jin, Y Huang, Z Mo Rutgers University. Center for Advanced Infrastructure and Transportation, 2022 | | 2022 |
| Stability within Parallel Connection: A Basis for Building Large-Scale Battery Systems Z Li, A Zuo, Z Mo, M Lin, C Wang, J Zhang, MH Hofmann, A Jossen Available at SSRN 4201713, 0 | | |
Xuan Sharon DiAssociate Professor at Civil Engineering & Engineering Mechanics, Columbia UniversityVerified email at columbia.edu
