Dr. Guoyang Qin is an Assistant Professor in the College of Transportation at Tongji University. He earned his Ph.D. in Transportation Engineering at Tongji University, where he also completed a postdoc, and he was a visiting Ph.D. student at the University of Michigan, Ann Arbor.
His research sits at the intersection of transportation data science and network modeling. Specifically, he works on spatiotemporal transportation data governance, the analysis, modeling, and optimization of complex transportation networks, and AI for transportation. His goal is to use models to understand what makes transportation systems complex—and what rules govern them—and then to design and optimize those systems accordingly.
He has led or served as technical lead on more than 10 funded projects. He has led an NSFC Young Scientist grant and two subprojects under China's National Key R&D Program. As technical lead, he has managed several sponsored projects, including ones funded by the Shanghai Science and Technology Program and an industry-academia partnership with SenseTime.
He has published 16 papers inTransportation Research Part CandIEEE Transactions on Intelligent Transportation Systems— both top journals in the field — plus one paper each at KDD and CIKM, two top-tier data mining conferences. His cumulative impact factor exceeds 152.8. His work has been recognized with the 2021 TRB Kikuchi-Karlaftis Best Paper Award—given to a single paper worldwide each year for research that is both theoretically novel and practically useful—as well as Tongji University's 2022 Outstanding Doctoral Dissertation Award and a First Prize in the Guangdong Provincial Science and Technology Progress Award.
He was selected for the 2022 Postdoctoral Innovative Talent Support Program (roughly four awards per year nationwide in transportation), the Shanghai "Super Postdoctoral" Program, and the 71st round of general funding from the China Postdoctoral Science Foundation. He is also affiliated with the Shanghai Postdoctoral Innovation Base, where he works to move research into practice through a government–university–industry partnership.
He reviews for more than 20 journals, including Transportation Research Parts B,C, and E; IEEE T-ITS, IoTJ, and TVT; Scientific Reports; the Journal of Transport Geography; Transportation Research Part A; Expert Systems with Applications; and the China Journal of Highway and Transport. He serves as an Associate Editor for the IEEE Intelligent Transportation Systems Conference (ITSC) for three consecutive terms (2024–2026), is Secretary of the Organizing Committee for ISTTT25, and sits on the editorial board of TTRB.
Educational background
2015–2021 Ph.D., Transportation Engineering, Tongji University
2018–2019 Visiting Ph.D. Student, Civil and Environmental Engineering, University of Michigan, Ann Arbor
2011–2015 B.S., Transportation Planning and Management, Southwest Jiaotong University
Work experience
2026–present Assistant Professor, School of Transportation, Tongji University
2022–2026 Postdoctoral Researcher, College of Transportation Engineering, Tongji University
2021–2022 Big Data R&D Intern, Autonomous Driving R&D Division, DiDi
Research interests
Spatiotemporal transportation data governance, the analysis, modeling, and optimization of complex transportation networks, and AI for transportation
Research Grants (Principal Investigator)
·National Natural Science Foundation of China (NSFC) Young Scientists Fund, "Congestion Percolation Mechanisms and Route Control Strategies in Urban Road Networks," No. 52302413, Jan. 2024–Dec. 2026.
·Postdoctoral Innovative Talent Support Program, "Dynamic Optimization of Ride-Hailing Supply-Demand Matching via Multimodal Shared Mobility," No. BX20220231, Jan. 2022–Dec. 2024.
·China Postdoctoral Science Foundation General Fund, "Mining and Optimizing Stochastic Search Strategies in Demand-Responsive Transit Systems with Varying Degrees of Centralization," No. 2022M712409, Jan. 2022–Dec. 2024.
·Shanghai "Super Postdoctoral" Incentive Program, "Privacy-Preserving Design of Multimodal Shared Mobility Systems," No. 2022753, Jan. 2022–Dec. 2024.
·National Key R&D Program of China, Subproject, "Collaborative Computing and Cascaded Control for Mixed-Autonomy Traffic Flows," No. 2023YFB4301903, Dec. 2023–Nov. 2026.
·National Key R&D Program of China, Subproject, "Digital Twin Prototype System for Urban Transportation Infrastructure and Its Application Validation," No. 2022YFB2602105, Dec. 2022–Nov. 2025.
Honors & Awards
1.2024 First Prize, Guangdong Provincial Science and Technology Progress Award — "Key Technologies for Urban Transportation Agent Modeling and Multimodal Fusion Evolutionary Computing" (ranked 13th)
2.2023 Outstanding Doctoral Dissertation, Tongji University — "Modeling and Optimization of Multimodal Shared Mobility Systems" (advisor: Prof. Jian Sun)
3.2022 2021 Transportation Research Board (TRB) Kikuchi-Karlaftis Best Paper Award — Guoyang Qin, Qi Luo, Yafeng Yin, Jian Sun, Jieping Ye
4. 2018 China Scholarship Council (CSC) National High-Level University Graduate Program — LIMOS, University of Michigan
Publications
For the most up-to-date list of publications, see my Google Scholar profile:
scholar.google.com/citations?user=RqB1UgYAAAAJ
1.Qin, G., Deng, S., Luo, Q., & Sun, J. (2025). Multimodal traffic assignment from privacy-protected OD data.Communications in Transportation Research, 5, Article 100223. https://doi.org/10.1016/j.commtr.2025.100223
2.Su, X., Chen, X.,Qin, G., Yin, J., & Sun, J. (2025). Generator-as-a-Matcher: Joint optimization of tracklet matching and gap filling for sparser mmWave radar placements on smart freeways.IEEE Transactions on Intelligent Transportation Systems, 26(11), 20737–20747. https://doi.org/10.1109/TITS.2025.3586924
3.Chen, X.,Qin, G., & Sun, J. (2025).Coordinated routing policy for connected vehicles to monitor city-wide traffic.Transportation Research Part C: Emerging Technologies, 176, Article 105147. https://doi.org/10.1016/j.trc.2025.105147
4.Qin, G., & Sun, J. (2022). Ride-hail to ride rail: Learning to balance supply and demand in ride-hailing services with intermodal mobility options.Transportation Research Part C: Emerging Technologies, 144, Article 103887. https://doi.org/10.1016/j.trc.2022.103887
5.Qin, G., Luo, Q., Yin, Y., Sun, J., & Ye, J. (2021). Optimizing matching time intervals for ride-hailing services using reinforcement learning.Transportation Research Part C: Emerging Technologies, 129, Article 103239. https://doi.org/10.1016/j.trc.2021.103239
6.Qin, G., Huang, Z., Xiang, Y., & Sun, J. (2019). ProbDetect: A choice probability-based taxi trip anomaly detection model considering traffic variability.Transportation Research Part C: Emerging Technologies, 98, 221–238. https://doi.org/10.1016/j.trc.2018.11.016
7.Qin, G., Li, T., Yu, B., Wang, Y., Huang, Z., & Sun, J. (2017).Mining factors affecting taxi drivers’ incomes using GPS trajectories.Transportation Research Part C: Emerging Technologies, 79, 103–118. https://doi.org/10.1016/j.trc.2017.03.013
8.Nie, T.,Qin, G., Ma, W., & Sun, J. (2024).Spatiotemporal implicit neural representation as a generalized traffic data learner.Transportation Research Part C: Emerging Technologies, 169, Article 104890. https://doi.org/10.1016/j.trc.2024.104890
9.Nie, T., Qin, G., Sun, L., Ma, W., Mei, Y., & Sun, J. (2024).Contextualizing MLP-mixers spatiotemporally for urban traffic data forecast at scale. IEEETransactions on Intelligent Transportation Systems, 26(1), 1241–1256. https://doi.org/10.1109/TITS.2024.3491754
10.Qiu, S.,Qin, G., Wong, M., & Sun, J. (2024). RoutesFormer: A sequence-based route choice Transformer for efficient path inference from sparse trajectories.Transportation Research Part C: Emerging Technologies, 162, Article 104552. https://doi.org/10.1016/j.trc.2024.104552
11.Chen, X.,Qin, G., Seo, T., Yin, J., Tian, Y., & Sun, J. (2024). A macro-micro approach to reconstructing vehicle trajectories on multi-lane freeways with lane changing.Transportation Research Part C: Emerging Technologies, 160, Article 104534. https://doi.org/10.1016/j.trc.2024.104534
12.Nie, T.,Qin, G., Wang, Y., & Sun, J. (2023).Towards better traffic volume estimation: Jointly addressing the underdetermination and nonequilibrium problems with correlation-adaptive GNNs.Transportation Research Part C: Emerging Technologies, 157, Article 104402. https://doi.org/10.1016/j.trc.2023.104402
13.Nie, T.,Qin, G., Wang, Y., & Sun, J. (2023).Correlating sparse sensing for large-scale traffic speed estimation: A Laplacian-enhanced low-rank tensor kriging approach.Transportation Research Part C: Emerging Technologies, 152, Article 104190. https://doi.org/10.1016/j.trc.2023.104190
14.Nie, T.,Qin, G., & Sun, J. (2022).Truncated tensor Schatten p-norm based approach for spatiotemporal traffic data imputation with complicated missing patterns.Transportation Research Part C: Emerging Technologies, 141, Article 103737. https://doi.org/10.1016/j.trc.2022.103737
15.Chen, X., Yin, J.,Qin, G., Tang, K., Wang, Y., & Sun, J. (2022). Integrated macro-micro modeling for individual vehicle trajectory reconstruction using fixed and mobile sensor data.Transportation Research Part C: Emerging Technologies, 145, Article 103929.https://doi.org/10.1016/j.trc.2022.103929
16.Nie, T.,Qin, G., Ma, W., Mei, Y., & Sun, J. (2024).ImputeFormer: Low rankness-induced
Transformers for generalizable spatiotemporal imputation. InKDD ’24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining(pp. 2260–2271). Association for Computing Machinery. https://doi.org/10.1145/3637528.3671751