Dr. Jin Liu | Algorithm Development | Best Researcher Award

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

SCOPUS

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

Dr. Yihao Chi - Railway Engineering - Best Researcher Award

Beijing Jiaotong University - China

Author Profile

Early Academic Pursuits

Dr. Yihao Chi's academic journey commenced at Beijing Jiaotong University, where he pursued his undergraduate degree in Railway Engineering. This foundational step laid the groundwork for his subsequent academic pursuits. Subsequently, he continued his education at the same institution, earning his Ph.D. in Railway Engineering. Throughout his academic career, Chi demonstrated a strong inclination towards research, particularly focusing on ballast track within railway engineering.

Professional Endeavors

Dr. Chi's professional endeavors have been centered around addressing critical issues in railway engineering, particularly concerning ballast track. His research interests led him to explore innovative solutions for challenges such as ice and snow splash affecting traffic safety in severe cold areas. Notably, his contributions in this area led to the development of a coupling algorithm, facilitating refined simulations of ice and snow splash processes. Additionally, Chi addressed the "necklace" problem, which posed safety concerns for the Yinxi High-speed Railway under icy conditions.

Contributions and Research Focus

Dr. Chi's contributions extend beyond theoretical research, as evidenced by his innovative introduction of an elastic track sleeper structure. This structural innovation, coupled with the establishment of an elastic track sleeper bed coupling analysis model, effectively tackled the issue of ballast crushing deterioration in actual railway operations. His research efforts provide valuable technical guidance for ensuring the safe operation, maintenance, and repair of ballasted tracks, thereby enhancing railway safety and efficiency.

Accolades and Recognition

Dr. Yihao Chi's contributions to railway engineering have garnered recognition within the academic community. His research output is reflected in a notable cumulative impact factor and a significant number of publications in prestigious journals indexed in SCI, SCIE, Scopus, and Web of Science. Furthermore, Chi has received awards and recognition, including the Best Researcher Award, acknowledging the significance of his contributions to the field.

Impact and Influence

Dr. Chi's research has made a tangible impact on the field of railway engineering, with his findings offering practical solutions to pressing challenges. His work on ice and snow splash simulations and the development of innovative track sleeper structures has the potential to enhance railway safety and operational efficiency, particularly in regions prone to harsh weather conditions. Moreover, his publications and patents underscore his influence in advancing the knowledge frontier within his domain of expertise.

Legacy and Future Contributions

Looking ahead, Yihao Chi's legacy lies in his dedication to advancing railway engineering through rigorous research and innovative problem-solving. His work serves as a foundation for future researchers and practitioners in the field, providing valuable insights and methodologies for addressing ongoing challenges in railway infrastructure. Chi's commitment to excellence and his passion for innovation ensure that his contributions will continue to shape the future of railway engineering, fostering safer, more efficient transportation networks globally.

In summary, Yihao Chi's early academic pursuits, professional endeavors, contributions, accolades, and future aspirations collectively highlight his significant impact and influence in the field of railway engineering. His innovative research, coupled with his dedication to advancing the field, positions him as a distinguished figure in academia and underscores his potential to drive transformative change within the realm of transportation infrastructure.