top of page
Journal Publications

​

  1. Dawid, H., Di, X., Kort, P.M., Muehlheusser, G., 2023. Autonomous vehicles policy and safety investment: an equilibrium analysis with endogenous demand. [ssrn]

  2. Di, X., Shi, R.Y., Mo, Z.B., Fu, Y.J., 2023. Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook, Algorithms, 16(6), p.305. [Link][arXiv]

  3. Di, X., Yin, Y.Q., Mo, Z.B., Fu, Y.J., Shaw-Hwa Lo, DiGuiseppi, C., Eby, D.W., Hill, L.L., Mielenz, T.J., Molnar, L.J., Strogatz, D., Andrews, H.F., Goldberg, T.E. and Lang, B.H., Kim, M., Li, G.H., 2023. Predicting Mild Cognitive Impairment and Dementia in Older Drivers from Naturalistic Driving Data Using Influence Score, Artificial Intelligence in Medicine. [AIM] 

  4. Chen, X., Wang, Z.H., Di, X., 2023. Sentiment Analysis on Multimodal Transportation During the COVID-19 Using Social Media Data, Information, 14 (2), 113.

  5. Mo, Z.B., Di, X., Shi, R.Y., 2023. Robust Data Sampling in Machine Learning: A Game-Theoretic Framework For Training and Validation Data Selection, Games, 14(1):13. 

  6. Gless, S., Di, X., Silverman, E., 2022. Ca(r)veat Emptor: Crowdsourcing Data to Challenge the Testimony of In-Car Technology. Jurimetrics Journal of Law, Science and Technology, 62(3). [ABA]

  7. Ozer, E., Malekloo, A., Ramadan, W., Tran, T., and Di, X., 2022. Systemic Reliability of Bridge Networks with Mobile Sensing-Based Model Updating for Post-Event Transportation Decisions. Computer-Aided Civil and Infrastructure Engineering, DOI:10.1111/mice.12892. [Link]

  8. Mo, Z.B., Li, W.Z., Fu, Y.J., Ruan, K.R., Di, X., 2022. CVLight: Decentralized Learning for Adaptive Traffic Signal Control with Connected Vehicles, Transportation Research Part C, 141: 103728. [TRC][arXiv]

  9. Bautista, R., Aguilera, R.G., Ruan, K.R., Fu, Y.J., Di, X., 2022. Autonomous Navigation at Unsignalized Intersections: A Coupled Reinforcement Learning and Model Predictive Control Approach, Transportation Research Part C, 139: 103662. [TRC]

  10. Chen, X., Di, X., 2022. A Unified Network Equilibrium for E-Hailing Platform Operation and Customer Mode Choice. [arXiv]

  11. Chen, X., Di, X., 2021. Ridesharing User Equilibrium with Nodal Matching Cost and Its Implications for Network Design Problems. Transportation Research Part C, 129: 103233. [TRC]

  12. Di, X., Shi, R., DiGuiseppi, C., Eby, D.W., Hill, L.L., Mielenz, T.J., Molnar, L.J., Strogatz, D., Andrews, H.F., Goldberg, T.E. and Lang, B.H., 2021. Using Naturalistic Driving Data to Predict Mild Cognitive Impairment and Dementia: Preliminary Findings from the Longitudinal Research on Aging Drivers (LongROAD) Study. Geriatrics, 6(2): 45. [Link]

  13. Mo, Z.B., Shi, R.Y., Di, X., 2021. A Physics-Informed Deep Learning Paradigm for Car-Following Models, Transportation Research Part C, 130: 103240. [TRC]​

  14. Shi, R.Y., Mo, Z.B., Huang, K., Di, X., Du, Q., 2021. A Physics-Informed Deep Learning Paradigm for Traffic State and Fundamental Diagram Estimation. IEEE Transactions on Intelligent Transportation Systems, 23(8): 11688-11698. [arXiv_v1] [arXiv_v2][TITS]

  15. Huang, K., Chen, X., Di, X., Du, Q., 2021. Dynamic Driving and Routing Games for Autonomous Vehicles on Networks: A Mean Field Game Approach. Transportation Research Part C, 128: 103189. [arXiv][TRC]

  16. Shou, Z.Y., Chen, X., Fu, Y.J., Di, X., 2021. Multi-Agent Reinforcement Learning for Markov Routing Games: A New Modeling Paradigm For Dynamic Traffic Assignment, Transportation Research Part C, 137: 103560. [arXiv][TRC]

  17. Di, X., Shi, R.Y., 2021. A Survey on Autonomous Vehicle Control in the Era of Mixed-Autonomy: From Physics-Based to AI-Guided Driving Policy Learning. Transportation Research Part C, 125: 103008. [TRC][download]

  18. Shou, Z.Y., Di, X., 2020. Reward Design for Driver Repositioning Using Multi-Agent Reinforcement Learning, Transportation Research Part C, 119: 102738. [Link]​.

  19. Li, Z.H., Gu, Z.C., Di, X., Shi, R.Y., 2020. An LSTM-Based Autonomous Driving Model Using Waymo Open Dataset, Applied Sciences - Intelligent Transportation Systems: Beyond Intelligent Vehicles, 10(6), 2046, DOI: 10.3390/app10062046. ​[Link]​

  20. Di, X., Chen, X., Talley, E., 2020. Liability Design for Autonomous Vehicles and Human-Driven Vehicles: A Hierarchical Game-Theoretic Approach. Transportation Research Part C, 118: 102710. [Link]​[arXiv]​[Video]

  21. Meinrenken, C.J., Shou, Z.Y., Di, X., 2020. Using GPS-data to determine optimum electric vehicle ranges: A Michigan cases study, Transportation Research Part D, 78: 102203. [Link]

  22. Luo, Q., Dou, X., Di, X., and Hampshire, R. Multimodal Connections between Micro-Mobility and Micro-transit: Conceptual Foundations and Empirical Evidence. IEEE Intelligent Transportation Systems Magazine, forthcoming.

  23. Shou, Z.Y., Di, X., Ye, J.P., Zhu, H.T., Zhang, H., Hampshire, R., 2020. Optimal Passenger-Seeking Policies on E-hailing Platforms Using Markov Decision Process and Imitation Learning, Transportation Research Part C, 111: 91-113. [Link]

  24. Huang, K., Di, X., Du, Q., Chen, X., 2020. Scalable Traffic Stability Analysis in Mixed-Autonomy Using Continuum Models, Transportation Research Part C, 111: 616-630. [Link]

  25. Huang, K., Di, X., Du, Q., Chen, X., 2019. A Game-Theoretic Framework for Autonomous Vehicles Velocity Control: Bridging Microscopic Differential Games and Macroscopic Mean Field Games, Discrete and Continuous Dynamical Systems - Series B, 25(12): 4869-4903. [DCDS-B]​[arXiv]​

  26. Di, X., Ban, X., 2019. A Unified Equilibrium Framework of New Shared Mobility Systems, Transportation Research Part B, 129: 50-78.​ [Link]

  27. Li, M., Di, X., Liu, H.X., Huang, H-J., 2019. A Restricted Path-Based Ridesharing User Equilibrium, Journal of Intelligent Transportation Systems, DOI: 10.1080/15472450.2019.1658525. [Link]

  28. Shou, Z.Y., Di, X.. Similarity Analysis of Frequent Sequential Activity Pattern Mining, Transportation Research Part C, 96: 122-143.​ [Link]

  29. Di, X., Zhao, Y., Huang, S.H., Liu, H.X., 2019. A Similitude Theory for Modeling Traffic Flow Dynamics, IEEE Transactions on Intelligent Transportation Systems, 20(3): 900-911. [Link]

  30. Di, X., Fabusuyi, T., Simek, C., Chen, X., Hampshire, R., 2019. Inferred Switching Behavior in Response to Re-Entry of Uber and Lyft: A Revealed Study in Austin, TX, Transport Findings, DOI: 10.32866/7568. [Link]

  31. Hampshire, R.C, Simek, C., Fabusuyi, T., Di, X., Chen, X.. Measuring the Impact of an Unanticipated Disruption of On-Demand Ride Services in Austin, Texas. [Link]

  32. Di, X., Ma, R., Liu, H.X., Ban, X., 2018. A Link-Node Reformulation of Ridesharing User Equilibrium With Network Design, Transportation Research Part B, 112: 230-255. [Link]

  33. Di, X., Liu, H.X., Ban, X., Yang, H., 2017. Ridesharing User Equilibrium and Its Implications for High-Occupancy Toll Lane Pricing, Transportation Research Record, 2667: 39-50.

  34. Danczyk, A., Di, X., Liu, H.X., Levinson, D.M., 2017. Unexpected versus Expected Network Disruption: Effects on Travel Behavior, Transport Policy, 57:68-78.  [Link].

  35. Di, X., Liu, H.X., Zhu, S.J., Levinson, D.M, 2017. Indifference Bands for Boundedly Rational Route Switching, Transportation, 44(5): 1169-1194. [Link]

  36. Danczyk, A., Di, X., Liu, H.X., 2016. A Probabilistic Optimization Model for Allocating Freeway Sensors, Transportation Research Part C, 67, 378-398. [Link]

  37. Di, X., Liu, H.X., 2016. Boundedly Rational Travel Behavior: A Review of Models and Methodologies, Transportation Research Part B, 85: 142-179. [Link]

  38. Di, X., Liu, H.X., Ban, X., 2016. Second Best Toll Pricing Within the Framework of Bounded Rationality, Transportation Research Part B, 83: 74-90. [Link]

  39. Di, X., Liu, H.X., Ban, X., Yu, J.W., 2015. On the Stability of a Boundedly Rational Day-to-day Dynamic, Networks and Spatial Economics, 15 (3): 537-557. [Link]

  40. Di, X., Liu, H.X., Levinson, D.M., 2015. Multi-Agent Route Choice Game for Transportation Engineering, Transportation Research Record, 2480: 55-63. [Link]

  41. Di, X., Liu, H.X., He, X.Z., Guo, X.L., 2014. Braess Paradox under the Boundedly Rational User Equilibria, Transportation Research Part B, 67: 86–108. [Link]

  42. Di, X., Liu, H.X., Pang, J.S., Ban, X., 2013. Boundedly Rational User Equilibria (BRUE): Mathematical Formulation and Solution Sets, Transportation Research Part B, 57: 300–313. [Link]

  43. Di, X., Liu, H. X., Davis, G. A., 2010. Hybrid Extended Kalman Filtering Approach for Traffic Density Estimation Along Signalized Arterials. Transportation Research Record, 2188 (1), 165-173. [Link]

  44. Di, X., Zhang, X.N., Zhang, M.H., 2008. Cellular Automata based Expressway Weaving Section Modeling and Simulation (in Chinese), Transportation and Computer, 26 (2), 23-26.

Conference Proceedings

  1. Ruan, K.R., Zhang, J.Z., Di, X., Bareinboim, E, 2023. Causal Imitation learning via Inverse Reinforcement Learning, the 11th International Conference on Learning Representations [Techical Report]

  2. Chen, X., Liu, S., Di, X., 2023. A Hybrid Framework of Reinforcement Learning and Physics-Informed Deep Learning for Spatiotemporal Mean Field Games, In Proceedings of the 20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)

  3. Mo, Z.B., Di, X., 2022. Physics-Informed Generative Adversarial Networks for Uncertainty Quantification of Human Car-Following Behaviors, the 28th ACM SIGKDD in conjunction with the 11th International Workshop on Urban Computing (UrbComp2022). [UrbComp22] (Best paper award)

  4. Mo, Z.B., Fu, Y.J., Di, X., 2022. TrafficFlowGAN: Physics-informed Flow based GAN for Uncertainty Quantification, European Conference on Machine Learning and Data Mining (ECML PKDD) [arxiv]. 

  5. Chen, X., Di, X., 2022. How the COVID-19 Pandemic Influences Human Mobility? Similarity Analysis Leveraging Social Media Data, the 25th IEEE International Conference on Intelligent Transportation Systems. 

  6. Mo, Z.B., Fu, Y.J., Di, X., 2022. Quantifying Uncertainty In Traffic State Estimation Using Generative Adversarial Networks, the 25th IEEE International Conference on Intelligent Transportation Systems.

  7. Shou, Z.Y., Chen, X., Di, X., 2022. Bayesian Optimization for Multi-Agent Routing in Markov Games, the 25th IEEE International Conference on Intelligent Transportation Systems.

  8. Liu, S., Wang, Y.H., Chen, X., Fu, Y.J., Di, X., 2022. SMART-eFlo: An Integrated SUMO-Gym Framework for Multi-Agent Reinforcement Learning in Electric Fleet Management Problem, the 25th IEEE International Conference on Intelligent Transportation Systems. 

  9. Chen, X., Li, Z.C., Di, X., 2022. Social Learning In Markov Games: Empowering Autonomous Driving, the IEEE Intelligent Vehicles Symposium (IEEE IV 2022). DOI: 10.1109/IV51971.2022.9827289 [Link]

  10. Ruan, K.R., Di, X., 2022. Learning Human Driving Behaviors with Sequential Causal Imitation Learning, the 36th Association for the Advancement of Artificial Intelligence (AAAI) Conference on Artificial Intelligence, 36(4):4583-4592. [AAAI]

  11. Shi, R., Mo, Z. and Di, X., 2021. Physics Informed Deep Learning for Traffic State Estimation: A hybrid paradigm informed by second-order traffic models. In Proceedings of the AAAI Conference on Artificial Intelligence, 35(1): 540-547. [AAAI]

  12. Shou, Z., Wang, Z., Han, K., Liu, Y., Tiwari, P. and Di, X., 2020. Long-term prediction of lane change maneuver through a multilayer perceptron. the 2020 IEEE Intelligent Vehicles Symposium (IEEE IV 2020). DOI: 10.1109/IV47402.2020.9304587 [Link]

  13. Shou, Z.Y., Cao, Z.H., Di, X., 2020. Similarity Analysis of Spatial-Temporal Travel Patterns for Travel Mode Prediction Using Twitter Data. the 23rd IEEE International Conference on Intelligent Transportation Systems (ITSC). DOI: 10.1109/ITSC45102.2020.9294709 [Link]

  14. Huang, K., Di, X., Du, Q., Chen, X., 2019. Stabilizing Traffic via Autonomous Vehicles: A Continuum Mean Field Game Approach, the 22nd IEEE International Conference on Intelligent Transportation Systems (ITSC), pp. 3269-3274. [Link]

  15. Luo, Q., Dou, X.C., Di, X., Hampshire, R.C, 2018. Multimodal Connections between Dockless Bikesharing and Ride-Hailing: An Empirical Study in New York City, the 21st IEEE International Conference on Intelligent Transportation Systems, DOI: 10.1109/ITSC.2018.8569896. [Link]

  16. Liao, S.Y., Zhou, L.T., Di, X., Yuan, B., Xiong, J.J., 2018. Large-scale Short-term Urban Taxi Demand Forecasting Using Deep Learning, invited paper at IEEE Conference on 23rd Asia and South Pacific Design Automation Conference (ASP-DAC). [Link]

  17. Di, X., Liu, H.X., Pang, J.S., Ban, X., 2013, Boundedly Rational User Equilibria (BRUE): Mathematical Formulation and Solution Sets, Proceedings of 20th International Symposium on Transportation and Traffic Theory, 231-248. [Proceeding pdf]

  18. Zhang, X.N., Di, X., Zhang, M.H., Simulating Traffic Spillback of the Expressway Weaving Area Based on Cellular Automata, 2009, Proceedings of the World Research Institutes (WRI) World Congress on Software Engineering (WCSE), IEEE Computer Society, 2: 137 - 141. [Link]

  19. Di, X., Zhang, X.N., Zhang, M.H., 2008, Expressway Weaving Section Simulation and Traffic Flow Optimization, Proceedings of Graduate Students of Tongji University XI, 510-516.

Doctoral Dissertation

Di, X. , 2014. Boundedly Rational User Equilibrium: Models and Applications. University of Minnesota.

bottom of page