Dr. Jin Liu | Algorithm Development – University of Leeds | United Kingdom
Dr. Jin Liu is a dedicated and forward-thinking researcher in the field of railway traffic management and intelligent transportation systems. Currently serving as a Research Fellow at the Institute for Transport Studies, University of Leeds, Dr. Liu has established himself as a key contributor to the development of next-generation transport technologies. His work bridges theory and practice, especially in areas of train rescheduling, real-time traffic control, and AI-driven optimisation methods for complex railway systems. With a solid foundation in electrical engineering and deep expertise in rail operation control systems, Dr. Liu continues to push boundaries in transport innovation through multi-agent systems, digital twins, and AI applications.
Professional Profile
Education
Dr. Liu began his academic journey with a Bachelor of Engineering in Electrical Engineering and Automation from the Harbin Institute of Technology, China. He then pursued his Master’s degree in Electronic and Electrical Engineering at the University of Birmingham, UK, where he completed a significant final-year project on onboard wheel slip protection systems for high-speed rail. Building on his growing interest in rail systems, Dr. Liu remained at Birmingham to complete his Ph.D., focusing on a multi-agent-based approach for resolving real-time train rescheduling problems in large-scale railway networks. His doctoral work laid the groundwork for much of his subsequent research on optimising railway traffic management using intelligent algorithms.
Professional Experience
Dr. Liu’s academic career commenced at Newcastle University as a Research Assistant and later as a Research Associate in the School of Engineering. He transitioned to the University of Leeds in March 2022, where he now contributes to multiple EU and UK-funded transportation projects. His role in these initiatives often involves the development of advanced algorithms, decision-support tools, and data models for improving operational efficiency in rail and road transport. Dr. Liu also provides leadership in technical project deliverables, industrial collaboration, and conference participation. He has built a reputation for integrating academic research with real-world applications, especially in projects related to electric vehicles, nano-emission tracking, and rail timetable optimisation.
Research Interests
Dr. Liu’s research revolves around modelling, analysis, and optimisation of rail traffic operations, including both passenger and freight services. He is particularly interested in developing energy-efficient train operations, train speed profile optimisation, and multi-agent-based modelling for traffic management. His algorithmic expertise covers both exact methods and meta-heuristic approaches, and he is deeply engaged in applying artificial intelligence—especially reinforcement learning—to real-world transportation challenges. Furthermore, his research encompasses railway system requirements analysis, focusing on RAM (Reliability, Availability, Maintainability), safety and security standards, and the verification and validation of novel railway traffic management applications.
Awards and Professional Engagements
Dr. Liu’s contributions to transport research have been recognised with several prestigious awards and professional memberships. Notably, he is the recipient of the Michael Beverley Innovation Fellowship, the International Researcher Mobility Fund, and has served as a guest speaker at the Birmingham Decarbonisation Summer School. His academic impact is evidenced by his selection as a Program Committee Member for the AI4RAILS 2023 conference and as a session chair at the IEEE ITSC 2023. He also plays a significant role in collaborative transportation initiatives, serving as the Early Career Researcher representative for the Network for Innovative Sustainable Transportation Co-Simulation. Furthermore, his work has been acknowledged among the top ten most downloaded papers of 2023 in his field.
Dr. Liu is an Associate Fellow of the UK’s Higher Education Academy and maintains an active publishing profile, with his research appearing in prestigious journals such as Applied Sciences, IET Intelligent Transport Systems, and Journal of Rail Transport Planning & Management. His research output includes notable work on reinforcement learning applications in train scheduling, system validation in virtual environments, and the assessment of railway operation impacts under hazardous conditions. Through his ongoing projects with European and UK stakeholders—such as Shift2Rail, Network Rail, and the EPSRC—Dr. Liu continues to contribute meaningfully to shaping the future of sustainable, intelligent, and resilient transportation systems.
Publications
A Multi-agent Based Approach for Railway Traffic Management Problems
Authors: J. Liu, L. Chen, C. Roberts, Z. Li, T. Wen
Journal/Conference: 2018 International Conference on Intelligent Rail Transportation (ICIRT)
Year: 2019
Algorithm and peer-to-peer negotiation strategies for train dispatching problems in railway bottleneck sections
Authors: J. Liu, L. Chen, C. Roberts, G. Nicholson, B. Ai
Journal: IET Intelligent Transport Systems
Year: 2019
Use cases for obstacle detection and track intrusion detection systems in the context of new generation of railway traffic management systems
Authors: P. Hyde, C. Ulianov, J. Liu, M. Banic, M. Simonovic, D. Ristic-Durrant
Journal: Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit
Year: 2021
Novel approach for validation of innovative modules for railway traffic management systems in a virtual environment
Authors: J. Liu, C. Ulianov, P. Hyde, A. L. Ruscelli, G. Cecchetti
Journal: Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit
Year: 2022
Formalization and Processing of Data Requirements for the Development of Next Generation Railway Traffic Management Systems
Authors: A. Magnien, G. Cecchetti, A. L. Ruscelli, P. Hyde, J. Liu, S. Wegele
Journal: Reliability, Safety, and Security of Railway Systems. Modelling, Analysis, Verification, and Certification (Book/Conference Proceedings)
Year: 2022