This list highlights representative work by research theme and is not frequently updated (last update: October 2026). A complete list is available on Google Scholar. Names of students and postdocs I supervised are marked with an asterisk (*).

Representative Publications by Research Theme

Inverse problems in transportation: inferring what cannot be observed

• Chen, Xiaoxu, Zhanhong Cheng, and Lijun Sun. “Bayesian inference of time-varying origin-destination matrices from boarding and alighting counts for transit services.” Transportation Research Part B: Methodological 199 (2025): 103278.
• Chen, Xiaoxu*, Yafeng Yin, and Lijun Sun. “Inductive transportation origin-destination demand forecasting via Bayesian destination-choice modeling.” Transportation Science (2026), in press.
• Chen, Xiaoxu*, Alexandra M. Schmidt, Zhenliang Ma, and Lijun Sun. “Bayesian spatiotemporal modeling of passenger trip assignment in metro networks.” arXiv preprint arXiv:2507.22403 (2025).
• Sun, Lijun, Yang Lu, Jian Gang Jin, Der-Horng Lee, and Kay W. Axhausen. “An integrated Bayesian approach for passenger flow assignment in metro networks.” Transportation Research Part C: Emerging Technologies 52 (2015): 116–131.

Bayesian spatiotemporal modeling: kernelized tensor models and Gaussian processes

• Lei, Mengying*, Aurelie Labbe, and Lijun Sun. “Scalable spatiotemporally varying coefficient modeling with Bayesian kernelized tensor regression.” Bayesian Analysis 20, no. 3 (2025): 919–947.
• Lei, Mengying*, and Lijun Sun. “Generalized least squares kernelized tensor factorization.” Pattern Recognition 180 (2026): 114342.
• Lei, Mengying*, Aurelie Labbe, and Lijun Sun. “Bayesian complementary kernelized learning for multidimensional spatiotemporal data.” arXiv preprint arXiv:2208.09978 (2022).
• Lei, Mengying, Aurelie Labbe, Yuankai Wu, and Lijun Sun. “Bayesian kernelized matrix factorization for spatiotemporal traffic data imputation and kriging.” IEEE Transactions on Intelligent Transportation Systems 23, no. 10 (2022): 18962–18974.
• Chen, Xinyu*, and Lijun Sun. “Bayesian temporal factorization for multidimensional time series prediction.” IEEE Transactions on Pattern Analysis and Machine Intelligence 44, no. 9 (2022): 4659–4673.
• Wu, Fan, Zhanhong Cheng*, Huiyu Chen, Tony Z. Qiu, and Lijun Sun. “Traffic state estimation with anisotropic Gaussian processes from vehicle trajectories.” Transportation Research Part C: Emerging Technologies 163 (2024): 104646.

Low-rank models, tensor completion and dynamic modes

• Lei, Mengying*, and Lijun Sun. “Low-rank tensor learning for spatiotemporal traffic and mobility data: A survey.” Artificial Intelligence for Transportation 3–4 (2025): 100035.
• Chen, Xinyu, Chengyuan Zhang, Xi-Le Zhao, Nicolas Saunier, and Lijun Sun. “Forecasting sparse movement speed of urban road networks with nonstationary temporal matrix factorization.” Transportation Science 59, no. 3 (2025): 670–687.
• Chen, Xinyu, Zhanhong Cheng, HanQin Cai, Nicolas Saunier, and Lijun Sun. “Laplacian convolutional representation for traffic time series imputation.” IEEE Transactions on Knowledge and Data Engineering 36, no. 11 (2024): 6490–6502.
• Chen, Xinyu, Chengyuan Zhang, Xiaoxu Chen*, Nicolas Saunier, and Lijun Sun. “Discovering dynamic patterns from spatiotemporal data with time-varying low-rank autoregression.” IEEE Transactions on Knowledge and Data Engineering 36, no. 2 (2023): 504–517.
• Wang, Xudong*, H. Shu, Luis Miranda-Moreno, and Lijun Sun. “Robust matrix completion for spatiotemporal traffic data in time-frequency domain.” IEEE Transactions on Intelligent Transportation Systems (2026).
• Wang, Xudong*, and Lijun Sun. “Anti-circulant dynamic mode decomposition with sparsity-promoting for highway traffic dynamics analysis.” Transportation Research Part C: Emerging Technologies 153 (2023): 104178.
• Wang, Xudong, Yuankai Wu, Dingyi Zhuang*, and Lijun Sun. “Low-rank Hankel tensor completion for traffic speed estimation.” IEEE Transactions on Intelligent Transportation Systems 24, no. 5 (2023): 4862–4871.
• Cheng, Zhanhong*, Martin Trépanier, and Lijun Sun. “Real-time forecasting of metro origin-destination matrices with high-order weighted dynamic mode decomposition.” Transportation Science 56, no. 4 (2022): 904–918.

Probabilistic forecasting with correlated errors

• Zheng, Vincent Zhihao, Seongjin Choi, and Lijun Sun. “Probabilistic traffic forecasting with dynamic regression.” Transportation Science 59, no. 4 (2025): 689–707.
• Choi, Seongjin, Nicolas Saunier, Vincent Zhihao Zheng, Martin Trépanier, and Lijun Sun. “Scalable dynamic mixture model with full covariance for probabilistic traffic forecasting.” Transportation Science 59, no. 4 (2025): 708–720.
• Zheng, Vincent Zhihao*, and Lijun Sun. “Multivariate probabilistic time series forecasting with correlated errors.” Advances in Neural Information Processing Systems (NeurIPS) (2024).
• Zheng, Vincent Zhihao*, and Lijun Sun. “MVG-CRPS: A robust loss function for multivariate probabilistic forecasting.” arXiv preprint arXiv:2410.09133 (2024).
• Zheng, Vincent Zhihao, Seongjin Choi, and Lijun Sun. “Better batch for deep probabilistic time series forecasting.” Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 238 (2024).
• Kong, Menglin, Vincent Zhihao Zheng, Xudong Wang*, and Lijun Sun. “Dynamic modes as time representation for spatiotemporal forecasting.” arXiv preprint arXiv:2506.01212 (2025).
• Kong, Menglin, Vincent Zhihao Zheng, and Lijun Sun. “Frequency-constrained learning for long-term forecasting.” arXiv preprint arXiv:2508.01508 (2025).
• Xu, Chen*, Qiang Wang, and Lijun Sun. “Link representation learning for probabilistic travel time estimation.” IEEE Transactions on Intelligent Transportation Systems 26, no. 11 (2025): 21149–21161.

Public transport demand and operations

• Chen, Xiaoxu, Zhanhong Cheng, Alexandra M. Schmidt, and Lijun Sun. “Conditional forecasting of bus travel time and passenger occupancy with Bayesian Markov regime-switching vector autoregression.” Transportation Research Part B: Methodological 192 (2025): 103147.
• Chen, Xiaoxu, Zhanhong Cheng, Jian Gang Jin, Martin Trépanier, and Lijun Sun. “Probabilistic forecasting of bus travel time with a Bayesian Gaussian mixture model.” Transportation Science 57, no. 6 (2023): 1516–1535.
• Zhao, Yangyang, Zhenliang Ma, Xiaoxu Chen, and Lijun Sun. “Deep structured Gaussian modeling for real-time probabilistic bus travel time prediction.” Transportation Research Part C: Emerging Technologies 186 (2026): 105617.
• Cheng, Zhanhong, Jiawei Wang, Martin Trépanier, and Lijun Sun. “Abnormal metro passenger demand is predictable from alighting and boarding correlations.” Transportation Research Part C: Emerging Technologies 178 (2025): 105239.
• Cheng, Zhanhong*, V. J. Rodrigues de Sousa, K. Perez Martinez, T. Barbier, and Lijun Sun. “Railway booking demand forecasting for revenue management: A deep probabilistic approach.” Transportmetrica A: Transport Science (2025).
• Wang, Jiawei*, and Lijun Sun. “Robust dynamic bus control: A distributional multi-agent reinforcement learning approach.” IEEE Transactions on Intelligent Transportation Systems 24, no. 4 (2023): 4075–4088.
• Wang, Jiawei*, and Lijun Sun. “Multi-objective multi-agent deep reinforcement learning to reduce bus bunching for multiline services with a shared corridor.” Transportation Research Part C: Emerging Technologies 155 (2023): 104309.
• Wang, Jiawei*, and Lijun Sun. “Reducing bus bunching with asynchronous multi-agent reinforcement learning.” Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI) (2021).

Stochastic traffic simulation and driving behavior

• Zhang, Chengyuan*, Zhengbing He, Cathy Wu, and Lijun Sun. “When context is not enough: Modeling unexplained variability in car-following behavior.” Transportation Research Part B: Methodological 214 (2026): 103588.
• Kong, Menglin, Chengyuan Zhang, and Lijun Sun. “Active simulation-based inference for scalable car-following model calibration.” arXiv preprint arXiv:2602.05246 (2026).
• C. Hickert, M. Samaei, A. Wang, Chengyuan Zhang*, Lijun Sun, Y. Wang, M. Ameli, and Cathy Wu. “The calibration illusion in traffic microsimulation.” arXiv preprint arXiv:2608.19642 (2026).
• Zhang, Chengyuan, Wenshuo Wang, and Lijun Sun. “Calibrating car-following models via Bayesian dynamic regression.” Transportation Research Part C: Emerging Technologies 168 (2024): 104719.
• Zhang, Chengyuan*, and Lijun Sun. “Bayesian calibration of the intelligent driver model.” IEEE Transactions on Intelligent Transportation Systems 25, no. 8 (2024): 9308–9320.
• Chen, Kehua*, Meixin Zhu, Lijun Sun, and Hai Yang. “Combining time dependency and behavioral game: A deep Markov cognitive hierarchical model for human-like discretionary lane changing modeling.” Transportation Research Part B: Methodological 189 (2024): 102980.
• Wang, Wenshuo, Letian Wang, Chengyuan Zhang, Changliu Liu, and Lijun Sun. “Social interactions for autonomous driving: A review and perspectives.” Foundations and Trends® in Robotics 10, no. 3–4 (2022): 198–376.

Bayesian optimization and decision-making under uncertainty

• Lei, Mengying*, and Lijun Sun. “Bayesian kernelized tensor factorization as surrogate for Bayesian optimization.” arXiv preprint arXiv:2302.14510 (2023).
• Z. Tang, Weihua Gu, Edward Chung, and Lijun Sun. “Surrogate-based dynamic network traffic signal optimization via a data-driven link dynamics model.” Transportation Research Part C: Emerging Technologies 193 (2026): 105965.

Generative AI and large language models for mobility and urban science

• Tang, Yihong, Menglin Kong, J. He, Tong Nie, Wei Ma, and Lijun Sun. “LLMSynthor: Macro-aligned micro-records synthesis with large language models.” Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (2026).
• Tang, Yihong*, Ao Qu, X. Yu, W. Deng, J. Ma, Jinhua Zhao, and Lijun Sun. “From street views to urban science: Discovering road safety factors with multimodal large language models.” Transportation Research Part C: Emerging Technologies 188 (2026): 105692.
• J. He, Yihong Tang*, Tong Nie, Ao Qu, Yuebing Liang, Hamzeh Alizadeh, B. Liu, Wei Ma, and Lijun Sun. “MobEvolve: An agentic self-evolving heuristic system for interpretable human mobility generation.” arXiv preprint arXiv:2606.01640 (2026).
• Tang, Yihong, Tong Nie, J. He, Q. Huang, Dingyi Zhuang, and Lijun Sun. “Bridge: Retrieval-augmented spatiotemporal modeling for urban delivery demand.” arXiv preprint arXiv:2605.19172 (2026).
• Xia, Yutong, Ao Qu, Yunhan Zheng, Yihong Tang, Dingyi Zhuang, Yuebing Liang, Shenhao Wang, Cathy Wu, Lijun Sun, Roger Zimmermann, and Jinhua Zhao. “Reimagining urban science: Scaling causal inference with large language models.” arXiv preprint arXiv:2504.12345 (2025).
• Choi, Seongjin*, Zhixiong Jin, Seung Woo Ham, Jiwon Kim, and Lijun Sun. “A gentle introduction and tutorial on deep generative models in transportation research.” Transportation Research Part C: Emerging Technologies 176 (2025): 105145.
• Xu, Chen*, Qiang Wang, and Lijun Sun. “Likelihood-free variational autoencoders.” arXiv preprint arXiv:2504.17622 (2025).
• Liu, Fuqiang, Sicong Jiang, Luis Miranda-Moreno, Seongjin Choi*, and Lijun Sun. “Adversarial vulnerabilities in large language models for time series forecasting.” Proceedings of the 28th International Conference on Artificial Intelligence and Statistics (AISTATS), PMLR 258 (2025): 4672–4680.
• Sun, Lijun, Alexander Erath, and Ming Cai. “A hierarchical mixture modeling framework for population synthesis.” Transportation Research Part B: Methodological 114 (2018): 199–212.
• Sun, Lijun, and Alexander Erath. “A Bayesian network approach for population synthesis.” Transportation Research Part C: Emerging Technologies 61 (2015): 49–62.

Shared mobility, travel behavior and climate

• Zhu, Xing-hang, Zhanhong Cheng, Luis Miranda-Moreno, and Lijun Sun. “ProST-CP: Probabilistic spatiotemporal CP decomposition for shared micro-mobility origin-destination demand modeling.” Transportation Research Part C: Emerging Technologies 192 (2026): 105874.
• Zhu, Xing-hang*, Lijun Sun, and Luis Miranda-Moreno. “Global assessment of bike-sharing sensitivity and resilience to extreme weather.” Transportation Research Part D: Transport and Environment 157 (2026): 105413.
• Wang, Xudong*, E. A. Valenzuela, C. You, Lijun Sun, and Luis Miranda-Moreno. “The future of urban cycling: A predictive framework for climate change.” Transportation Research Part D: Transport and Environment 143 (2025): 104722.
• Chang, Annie Y. J., Xudong Wang, Mojdeh Sharafi*, Luis Miranda-Moreno, and Lijun Sun. “Headwind or tailwind? The evolution of bike-sharing and ride-hailing demand during the COVID-19 pandemic.” Journal of Transport Geography 118 (2024): 103944.
• Wang, Xudong, Zhanhong Cheng, Martin Trépanier, and Lijun Sun. “Modeling bike-sharing demand using a regression model with spatially varying coefficients.” Journal of Transport Geography 93 (2021): 103059.
• Sun, Lijun, Xinyu Chen*, Zhaocheng He, and Luis Miranda-Moreno. “Routine pattern discovery and anomaly detection in individual travel behavior.” Networks and Spatial Economics 23 (2023): 407–428.
• Sun, Lijun, Kay W. Axhausen, Der-Horng Lee, and Xianfeng Huang. “Understanding metropolitan patterns of daily encounters.” Proceedings of the National Academy of Sciences 110, no. 34 (2013): 13774–13779.