(完整成果请参考https://scholar.google.co.jp/citations?user=-uhcC6EAAAAJ&hl=zh-CN&oi=sra)
1. 城市路网韧性管控
(1) Su, Z., Chow, A.H.F., Fang, C., Liang, E., & Zhong, R. (2023). Hierarchical control for stochastic network traffic with reinforcement learning. Transportation Research Part B, 167, 196-216.(双层优化)
(2) Su, Z., Chow, A.H.F., & Zhong, R. (2021). Adaptive network traffic control with an integrated model-based and data-driven approach and a decentralised solution method. Transportation Research Part C, 128, 103154.(信号控制,交通理论顶会ISTTT大会报告)
(3) Su, Z., Chow, A.H.F., Zheng, N., Huang, Y., Liang, E., & Zhong, R. (2020). Neuro-dynamic programming for optimal control of macroscopic fundamental diagram systems. Transportation Research Part C, 116, 102628.(边界控制)
2. 出行服务与体验评价
(1) Guo, Y., Su, Z.*, Yang, H., Liang, E., Zhong, C., & Ma, W. (2026). A smart predict-then-optimize framework for vehicle rebalancing problem. Transportation Research Part B, 206, 103411.(车辆调度,部署于滴滴平台)
(2) Yang, J., Chen, L., Su, Z.*, Ma, W., Zou, Z., & An, K. (2025). Decision-focused learning for optimal subsidy allocation in ride-hailing services. Transportation Research Part C, 180, 105301.(补贴分配,部署于滴滴平台)
(3) Li, M., Fan, C., Yan, H., Wu, P., Su, Z.*, & Ma, W. (2026). Urban traffic evaluation with social media data: A consensus-based LLM negotiation paradigm. Transportation Research Part A, 208, 104980.(大模型评测,指导本科生以第一作者发表)
(4) Gao, S., Ran, Q., Su, Z.*, Wang, L., Ma, W., & Hao, R. (2024). Evaluation system for urban traffic intelligence based on travel experiences: A sentiment analysis approach. Transportation Research Part A, 187, 104170.(体验评价,本科生毕业论文成果)
3. 人工智能与机器学习理论
(1) Yang, J., Su, Z.*, Zou, Z., Zhen, P., Ma, W., & An, K. (2026). Optimal treatment assignment from observational data: A decision-focused learning approach via pseudo labels. In ICLR 2026 Workshop on AI for Mechanism Design and Strategic Decision Making.(CCF-A会议,因果推断)
(2) Yang, J., Liang, E., Su, Z.*, Zou, Z., Zhen, P., Guo, J., ... & An, K. (2025). DFF: decision-focused fine-tuning for smarter predict-then-optimize with limited data. In AAAI 2025(oral), Vol. 39, No. 25, pp. 26868-26876.(CCF-A会议, 决策导向学习)
(3) Liang, E., Su, Z., Fang, C., & Zhong, R. (2022). OAM: An option-action reinforcement learning framework for universal multi-intersection control. In AAAI 2022 (oral), Vol. 36, No. 4, pp. 4550-4558.(CCF-A会议,可泛化强化学习)