Guangyu Zhu | Intelligent Transportation System | Best Researcher Award

Mr Guangyu Zhu | Intelligent transportation system | Best Researcher Award

Ph.D. candidate, Tsinghua University, China

Guangyu Zhu is a dedicated researcher and PhD candidate in Automotive Engineering at Tsinghua University, specializing in intelligent connected vehicles and vehicle-road collaborative systems. With a strong academic foundation from Shanghai Jiaotong University (Master’s) and Northeastern University (Bachelor’s), Guangyu has consistently excelled in his field, earning numerous awards and scholarships. His research focuses on the development strategy, cost-benefit analysis, and social value of intelligent automotive technologies. Guangyu has led significant consulting projects for the China Academy of Engineering and top international automobile companies, contributing to the advancement of China’s automotive industry. He has published multiple high-impact papers in SCI and EI-indexed journals, showcasing his expertise in intelligent transportation systems, steer-by-wire technologies, and blockchain applications in automotive fields. Guangyu’s work bridges academic research and industry innovation, aiming to shape the future of intelligent transportation.

Professional Profile

Orcid

Education 🎓

  • PhD in Automotive Engineering, Tsinghua University (Aug 2020–Jun 2025)
    Research: Intelligent connected vehicles, vehicle-road collaborative systems, social and user value.
    Awards: SAECCE Young Professional Excellent Paper, University/Institute scholarships.
  • Master of Engineering in Automotive Engineering, Shanghai Jiaotong University (Sep 2017–Mar 2020)
    Research: Steer-by-wire systems, lane-changing decision-making.
    Awards: Huawei Cup Mathematical Contest in Modeling (Second Prize).
  • Bachelor of Science in Applied Physics, Northeastern University (Sep 2013–Jun 2017)
    GPA: 3.7/4.0 (Rank: 1/88).
    Awards: Outstanding Graduate of Liaoning Province, National Scholarship, Huawei Scholarship.

Experience 💼

  • Lead Researcher, Consulting Project, China Academy of Engineering (Jun 2020–Apr 2021)
    Defined development strategies for intelligent automotive technologies and identified key bottlenecks.
  • Lead Researcher, Consulting Project for Top International Automobile Company (Aug 2020–Feb 2022)
    Analyzed drivers of China’s automotive industry, including pandemic impacts and digital transformation.
  • Lead Researcher, University-Industry Joint Research Project on Steer-by-Wire Systems (Feb 2019–Feb 2020)
    Developed a steer-by-wire test bench and designed an internal model controller for precise steering.

Awards and Honors �

  • SAECCE Young Professional Excellent Paper Award
  • University-level Second-Class Scholarship (Tsinghua University)
  • Institute-level First-Class Scholarship (Tsinghua University)
  • Second Prize, Huawei Cup Mathematical Contest in Modeling (Shanghai Jiaotong University)
  • Outstanding Graduate of Liaoning Province (Northeastern University)
  • National Scholarship (Northeastern University)
  • Huawei Scholarship (Northeastern University)
  • University-level First-Class Scholarship (Northeastern University)
  • Second Prize, Mathematical Contest in Modeling (MCM)

Research Focus 🔍

Guangyu Zhu’s research focuses on intelligent connected vehicles and vehicle-road collaborative systems, emphasizing development strategies, cost-benefit analysis, and social value. He explores the integration of advanced technologies like blockchain and steer-by-wire systems in the automotive industry. His work also addresses traffic efficiencyenergy consumption, and user-centric design in high-level autonomous driving scenarios. Guangyu’s research bridges theoretical innovation and practical applications, contributing to the sustainable growth of intelligent transportation systems in China and globally.

Publication Top Notes📚

  1. Cost Analysis of Vehicle‐Road Cooperative Intelligence Solutions for High‐Level Autonomous Driving: A Beijing Case Study
  2. Research on Vehicle-Road Intelligent Capacity Redistribution and Cost Sharing in the Context of Collaborative Intelligence
  3. Research on Social Values of Vehicle-Road Collaborative Intelligent Systems: A Beijing Case Study
  4. Blockchain Technology and Its Application in Automotive Field
  5. Controller Design for an Automobile Steer-by-Wire System
  6. Intelligent Vehicle Technology Innovation Development Strategy Supporting the Upgrading of Traffic
  7. Study on Front Wheel Angle Tracking Strategy of Steering-by-Wire System
  8. Impacts of Connected and Autonomous Vehicles with Level 2 Automation on Traffic Efficiency and Energy Consumption
  9. Evaluation of Traffic Efficiency and Energy-Saving Benefits of L3 Smart Vehicles under the Urban Expressway Scenario
  10. The Expansion of Value Engineering Theory and Its Application in the Intelligent Automotive Industry

Conclusion 🌟

Guangyu Zhu is a highly accomplished researcher and PhD candidate whose work in intelligent connected vehicles and vehicle-road collaborative systems is shaping the future of transportation. With a strong academic background, extensive research experience, and numerous awards, he is a leading voice in the automotive industry. His publications and projects demonstrate a commitment to innovation, sustainability, and practical applications, making him a valuable contributor to the field of intelligent transportation.

 

Fang Yang – Transportation Engineering – Best Researcher Award

Fang Yang - Transportation Engineering - Best Researcher Award

Kunming University of Science and Technology - China

AUTHOR PROFILE

SCOPUS

EXPERT IN ELECTRIC VEHICLE CHARGING SAFETY

Fang Yang is a leading researcher in the field of electric vehicle technology, with a focus on enhancing the safety and efficiency of electric bike charging systems. His work explores innovative methods for detecting charging anomalies and promoting safe charging practices through advanced data analysis and machine learning techniques.

PROLIFIC AUTHOR IN ENGINEERING AND TRANSPORTATION

Fang has contributed significantly to academic literature with several high-impact publications. Notably, his paper on electric bike charging anomaly detection was published in Engineering Applications of Artificial Intelligence, highlighting his expertise in big data applications for transportation systems.

MAJOR PROJECT CONTRIBUTOR

Fang has played a pivotal role in various major projects, including evaluating traffic impacts and organizing traffic during the construction of Guiyang Rail Transit Line S2. His contributions extend to optimizing safety operations for new energy vehicle charging piles and researching big data public services for Kunming mobile signaling.

ADVANCING MACHINE LEARNING IN TRANSPORTATION

His research also includes leveraging machine learning to enhance the safety of electric bicycle charging systems. His work in this area has been featured in iScience, reflecting his commitment to applying cutting-edge technology to real-world transportation challenges.

RESEARCH IN URBAN RAIL TRANSIT DEMANDS

Fang's research extends to the predictability of passenger demands in urban rail transit. His study, published in Transportation, delves into short-term predictions for passenger origins and destinations, showcasing his expertise in optimizing urban transit systems.

FOCUS ON DATA-DRIVEN FORECASTING

His paper on battery swapping demands for electric bicycles, published in the Journal of Transportation Systems Engineering and Information Technology, underscores his proficiency in data-driven forecasting and its applications in improving transportation infrastructure.

DIVERSE RESEARCH EXPERIENCE

With extensive experience across multiple research projects, Fang Yang's work spans from safety analysis of new energy vehicle infrastructure to public service optimization using big data. His diverse expertise reflects a broad commitment to advancing transportation systems through innovative research.

NOTABLE PUBLICATION

Predictability of Short-Term Passengers’ Origin and Destination Demands in Urban Rail Transit.
Authors: F. Yang, C. Shuai, Q. Qian, M. He, J. Lee
Year: 2023
Journal: Transportation, 50(6), pp. 2375–2401

Online Car-Hailing Origin-Destination Forecast Based on a Temporal Graph Convolutional Network.
Authors: C. Shuai, X. Zhang, Y. Wang, F. Yang, G. Xu
Year: 2023
Journal: IEEE Intelligent Transportation Systems Magazine, 15(4), pp. 121–136

Intelligent Diagnosis of Abnormal Charging for Electric Bicycles Based on Improved Dynamic Time Warping.
Authors: C. Shuai, Y. Sun, X. Zhang, X. Ouyang, Z. Chen
Year: 2023
Journal: IEEE Transactions on Industrial Electronics, 70(7), pp. 7280–7289

Promoting Charging Safety of Electric Bicycles via Machine Learning.
Authors: C. Shuai, F. Yang, W. Wang, Z. Chen, X. Ouyang
Year: 2023
Journal: iScience, 26(1), 105786

Battery Swapping Demands Forecast for Electric Bicycles Based on Data-Driven.
Authors: C.-Y. Shuai, F. Yang, X. Ouyang, G. Xu
Year: 2021
Journal: Jiaotong Yunshu Xitong Gongcheng Yu Xinxi/Journal of Transportation Systems Engineering and Information Technology, 21(2), pp. 173–179