教育经历
2015.09-2021.12同济大学，交通运输工程，博士
2018.09-2019.09美国密歇根大学安娜堡分校，土木与环境工程系，联合培养博士
2011.09-2015.07西南交通大学，交通运输规划与管理，学士
工作经历
2026.08至今同济大学，交通学院，助理教授
2022.03-2026.07同济大学，交通运输工程学院，博士后
2021.12-2022.03滴滴出行，自动驾驶技术研发部门，大数据研发实习生
研究方向
时空交通数据治理，复杂交通网络分析、建模与优化，人工智能赋能交通（AI for transportation）
主持项目
1.国家自然科学基金委员会青年科学基金项目（52302413）：城市路网拥堵渗流生成机理与路径管控策略，2024.01-2026.12
2.博士后创新人才支持计划（BX20220231）：基于多模式共享出行的网约车供需匹配动态优化研究，2022.01-2024.12
3.中国博士后科学基金面上资助（2022M712409）：不同中心化程度的需求响应出行系统中的随机搜索策略挖掘与优化，2022.01-2024.12
4.上海市“超级博士后”激励计划（2022753）：面向隐私保护的多模式共享出行系统设计，2022.01-2024.12
5.国家重点研发计划子课题（2023YFB4301903）：异智交通流路车协同计算与级联控制，2023.12-2026.11
国家重点研发计划子课题（2022YFB2602105）：城市交通基础设施数字孪生原型系统及应用验证，2022.12-2025.11
获奖情况
1.2018.06国家建设高水平大学公派研究生项目（LIMOS at the University of Michigan）
2.2022.01 2021年美国交通运输研究委员会（TRB）Kikuchi-Karlaftis最佳论文奖（Guoyang Qin, Qi Luo, Yafeng Yin*, Jian Sun, Jieping Ye）
3.2023.09同济大学2022年优秀博士学位论文（学位论文题目：多模式共享出行系统建模与优化，导师：孙剑教授）
2024.08广东省科技进步一等奖（城市交通智能体建模与多模式融合演化计算关键技术，排13）
发表刊物
最新发表情况见Google Scholar:
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