Ph.D.
Peking University Shenzhen Graduate School
School of Urban Planning and Design
Dr. Jian Yuan is a postdoctoral researcher at the School of Urban Planning and Design, Peking University Shenzhen Graduate School. He received his Ph.D. in Transportation Engineering from Tongji University and was a visiting scholar at McGill University. His research spans transportation modeling, spatiotemporal analytics, transport–energy systems and AI in transportation. He has co-authored 15 journal articles, including work in TR-Part C/D/E, Applied Energy, Journal of Transport Geography, Nexus, China Journal of Highway and Transport, and Journal of Transportation Systems Engineering and Information Technology. He leads an NSFC Young Scientists Fund project, participates in an NSFC General Program project, and is a co-inventor of five granted patents. He contributes to TransBigData and has co-authored a book published by Tsinghua University Press. He also leads Qunachong, a platform for sharing private EV chargers connecting research with practical applications.
Complex network modeling and mechanisms of traffic congestion
Data fusion, trajectory mining and spatial computing
Electric mobility, charging networks and urban energy
Agent-based simulation and data-driven decisions
Based at Peking University Shenzhen Graduate School, the laboratory brings together engineering and social sciences to study energy geography, energy system optimization, social perception and behavioral modeling, and sustainable governance. Combining multi-source data analysis with simulation, its research addresses risk identification, system resilience and collaborative governance in energy transitions, supporting low-carbon urban and regional development.
Transportation Research Part D · 2026
A data-driven agent-based model examining resilience patterns and the propagation of cascading failures in highway charging networks.
交通运输系统工程与信息 · 2024
Tracing lane-level traffic origins under low trajectory-data penetration by combining traffic shockwave features with machine learning.
Transportation Research Part C · 2022
Estimating route flows by fusing probe vehicle trajectories and automated vehicle identification data, evaluated using real-world pNEUMA trajectories.
A platform connecting shared private chargers with EV drivers, supporting research and practical applications in shared charging.
Visit project website ↗
A Python toolkit for vehicle trajectory processing, mobility analysis and spatial visualization.
GitHub ↗
A practical guide to vehicle trajectory processing, mining and visualization with Python. Published in Chinese.
I welcome collaborations in transportation modeling, spatiotemporal analytics and transport–energy systems. Through the Qunachong platform for sharing private EV chargers, I welcome collaborations on city-scale charging resource coordination, platform integration, system development and scheduling optimization. Prospective interns and research assistants interested in research or development are welcome to get in touch. Please briefly introduce your background, interests or collaboration ideas.