Taijie Chen陈泰劼

Ph.D. Candidate, The University of Hong Kong
Research Intern, Noah's Ark Lab, Huawei

Hong Kong SAR, China HKU Smart Mobility Lab
Portrait of Taijie Chen

I am a Ph.D. candidate at the University of Hong Kong, advised by Prof. Jintao Ke. In 2026 I spent six months at EPFL as a visiting Ph.D. student, hosted by Prof. Nicolas Geroliminis at the Urban Transport Systems Laboratory. I hold an M.S. in Computer Science from HKU and a B.E. in Software Engineering from Nankai University.

My research develops artificial intelligence methodologies for next-generation mobility systems. I work on three connected questions: how to make learning-based decisions on large-scale transportation platforms, how to capture human mobility intelligence with machine learning and foundation models, and how to build AI-enabled simulation and digital twins that let those methods be analysed and optimised before deployment. Much of this work is done with industry partners and evaluated on real ride-hailing operations.

  • Artificial Intelligence for Transportation Systems
  • Learning-based Sequential Decision Making
  • Human Mobility Intelligence and Foundation Models
  • Transportation Digital Twins and Simulation
Email CV Google Scholar GitHub ResearchGate

News

  • 09/2026 🚀 Started a research internship at Noah's Ark Lab, Huawei, working on domain-specific LLM agents and LLM post-training.
  • 09/2026 🇨🇭 Completed my six-month visiting Ph.D. at EPFL with Prof. Nicolas Geroliminis at the Urban Transport Systems Laboratory.
  • 08/2026 📄 Preprint "LAB-Tab: LLM-Augmented Bayesian Network Adaptation for Few-Shot Tabular Generation" is available on arXiv.
  • 05/2026 🎉 Our paper "D3-Subsidy: Online and Sequential Driver Subsidy Decision-Making for Large-Scale Ride-Hailing Market" has been accepted by ACM SIGKDD (KDD 2026).
  • 04/2026 🎉 Our paper "Addressing the Online Incremental Transport Mode Choice Prediction Problem with an LLM-Augmented Class-Incremental Learning Approach" has been accepted by Transportation Research Part C.
  • 03/2026 ✈️ Started a visiting Ph.D. at EPFL, hosted by Prof. Nicolas Geroliminis at the Urban Transport Systems Laboratory.
  • 10/2025 🎉 Our paper "T2BR: A Hierarchical Repositioning Approach for Autonomous Mobility-on-Demand Systems" has been accepted by IEEE TITS.
  • 07/2025 🎉 Our paper "To Grab or Not to Grab? Revealing Determinants of Drivers' Willingness to Grab Orders under the Broadcasting Mode" has been accepted by Travel Behaviour and Society.
  • 11/2024 🎉 Our paper "Teaching-Inspired Integrated Prompting Framework" has been accepted by the 31st International Conference on Computational Linguistics (COLING 2025).
  • 10/2024 🎉 Our paper "Dynamic Matching Radius Decision Model for On-Demand Ride Services" has been accepted by Transportation Research Part E.
  • 09/2024 📢 Presented "A Top-to-Bottom Repositioning Method for Ride-Hailing Platforms" at the TRC-30 Symposium in Crete, Greece.
  • 12/2023 🎓 Attended the 27th International Conference of the Hong Kong Society for Transportation Studies (HKSTS) and gave a poster presentation.
  • 04/2023 🎒 Began my Ph.D. journey at the University of Hong Kong.

Education

  • Ph.D. in Transportation Engineering Apr 2023 – Apr 2027 (expected)
    The University of Hong Kong
    Advisor: Prof. Jintao Ke · HKU Smart Mobility Lab
  • Visiting Ph.D. Student Mar 2026 – Sept 2026
    École Polytechnique Fédérale de Lausanne (EPFL)
    Host: Prof. Nicolas Geroliminis · Urban Transport Systems Laboratory
  • M.S. in Computer Science Sept 2021 – Dec 2022
    The University of Hong Kong
  • B.E. in Software Engineering Sept 2017 – Jun 2021
    Nankai University

Experience

  • Research Intern Sep 2026 – Present
    Noah's Ark Lab, Huawei
    LLM agent design for domain-specific tasks and LLM post-training.
  • Research Intern Jul 2025 – Feb 2026
    Didi Chuxing
    Formulated dynamic pricing as a constrained MDP and designed a reinforcement learning algorithm to solve it.
  • Research Intern Nov 2022 – Jul 2023
    Didi Chuxing
    Time-series models for electric-vehicle anomaly detection.
  • Research Assistant Aug 2022 – Mar 2023
    The Hong Kong University of Science and Technology
    Reinforcement learning methods for vehicle dispatching and repositioning.

Selected Publications 13 total

  • D<sup>3</sup>-Subsidy: Online and Sequential Driver Subsidy Decision-Making for Large-Scale Ride-Hailing Market
    Ride-HailingSequential Decision MakingSubsidy Control

    D3-Subsidy: Online and Sequential Driver Subsidy Decision-Making for Large-Scale Ride-Hailing Market

    Chen, T., Su, R., Feng, S., Zhang, L., Zhang, H., Wang, H., Ma, Z., Ke, J., & Ma, L.

    Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Vol. 2 (KDD '26), pp. 7094–7105, 2026.

    CCF-A
    Paper DOI
  • T2BR: A Hierarchical Repositioning Approach for Autonomous Mobility-on-Demand Systems
    Autonomous Mobility-on-DemandReinforcement LearningFleet Repositioning

    T2BR: A Hierarchical Repositioning Approach for Autonomous Mobility-on-Demand Systems

    Chen, T., Liu, J., Feng, S.✉, Qiu, J., & Ke, J.

    IEEE Transactions on Intelligent Transportation Systems, 26(12), 23139–23150, 2025.

    SCI-Q1 Top CCF-B IF 8.5
    Paper
  • Dynamic Matching Radius Decision Model for On-Demand Ride Services: A Deep Multi-Task Learning Approach
    Ride-HailingMulti-Task LearningOrder Matching

    Dynamic Matching Radius Decision Model for On-Demand Ride Services: A Deep Multi-Task Learning Approach

    Chen, T., Shen, Z., Feng, S.✉, Yang, L., & Ke, J.

    Transportation Research Part E: Logistics and Transportation Review, 193, 103822, 2025.

    SCI-Q1 Top IF 8.3
    Paper Code
  • Addressing the Online Incremental Transport Mode Choice Prediction Problem with an LLM-Augmented Class-Incremental Learning Approach
    LLM AugmentationTravel BehaviourContinual Learning

    Addressing the Online Incremental Transport Mode Choice Prediction Problem with an LLM-Augmented Class-Incremental Learning Approach

    Chen, T., Shen, Z., Zhou, B., Liu, Y., Wang, S., & Ke, J.

    Transportation Research Part C: Emerging Technologies, 188, 105709, 2026.

    SCI-Q1 Top IF 7.9
    Paper
  • Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language Models
    LLM ReasoningPrompt Design

    Teaching-Inspired Integrated Prompting Framework: A Novel Approach for Enhancing Reasoning in Large Language Models

    Tan, W., Chen, D., Xue, J., Wang, Z., & Chen, T.✉

    Proceedings of the 31st International Conference on Computational Linguistics: Industry Track (COLING), pp. 827–839, 2025.

    CCF-B
    Paper arXiv Code
  • To Grab or Not to Grab? Revealing Determinants of Drivers' Willingness to Grab Orders under the Broadcasting Mode
    Driver BehaviourRide-HailingBroadcasting Mode

    To Grab or Not to Grab? Revealing Determinants of Drivers' Willingness to Grab Orders under the Broadcasting Mode

    Chen, T., Liang, J., Zhao, Y.✉, & Ke, J.

    Travel Behaviour and Society, 41, 101093, 2025.

    SSCI-Q1 Top IF 5.1
    Paper
  • A Multi-Functional Simulation Platform for On-Demand Ride Service Operations
    Simulation PlatformOpen SourceRide-Sourcing

    A Multi-Functional Simulation Platform for On-Demand Ride Service Operations

    Feng, S., Chen, T., Zhang, Y., Ke, J.✉, Zheng, Z., & Yang, H.

    Communications in Transportation Research, 4, 100141, 2024.

    SCI-Q1 Top IF 12.5
    Paper Code Video
See all publications

Academic Service

  • Reviewer, Transportation Research Board (TRB) Annual Meeting
  • Student Helper, 27th International Conference of the Hong Kong Society for Transportation Studies (HKSTS) 2023
  • Student Helper, 9th International Symposium on Transport Network Resilience

Journal Reviewer

  • Transportation Research Part C: Emerging Technologies
  • Travel Behaviour and Society
  • Transportation Planning and Technology
  • Cluster Computing