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

EARLY ACADEMIC PURSUITS:

Zhe Li has demonstrated a commitment to academic excellence through his educational journey. Currently enrolled as a Sponsored Scholar at the University of Birmingham (UK) pursuing a Ph.D. in Railway and Highway Engineering, he is expected to complete this program by February 2024. Simultaneously, Zhe Li is also engaged in a Ph.D. program in Railway and Highway Engineering at Chang'an University, China, showcasing his dedication to advancing knowledge in his field.

PROFESSIONAL ENDEAVORS:

Zhe Li's professional journey reflects a diverse and impactful engagement with the field of transportation and pavement engineering. His roles as an Assistant Editor for the China Journal of Highway and Transport and a Student Assistant at Chang'an University's Training and Teaching Office underscore his involvement in both academic publication processes and administrative aspects of teaching and training.

CONTRIBUTIONS AND RESEARCH FOCUS:

Zhe Li has specialized in asphalt pavement research, with a focus on pavement condition assessment, 3D modeling using machine learning, and non-destructive testing methods. His research contributions are evident in published works, including articles in reputable journals such as 'Automation in Construction' and 'Construction and Building Materials.' These publications cover topics ranging from crack width measurement algorithms to predictive modeling of asphalt pavement performance.

IMPACT AND INFLUENCE:

Through his research endeavors and roles as an editor and reviewer for esteemed international journals, Zhe Li has contributed significantly to the academic community. His impact extends beyond research, influencing the quality and dissemination of knowledge in the field of transportation engineering.

ACADEMIC CITES:

Zhe Li's work is substantiated by several publications, demonstrating his influence and recognition within the academic community. Notable articles include those on crack width measurement algorithms, suitability evaluation methods for preventive maintenance, and asphalt pavement performance prediction.

LEGACY AND FUTURE CONTRIBUTIONS:

Zhe Li's legacy is characterized by his dedication to advancing pavement engineering knowledge and his active involvement in academic publishing. His future contributions are anticipated to further enrich the field, building on his research experiences, editorial roles, and the completion of dual Ph.D. programs.

Force system conversion mechanisms of retaining structures for subway excavation in soft soil. 2023 (1) Suitability evaluation method for preventive maintenance of asphalt pavement based on interval-entropy weight-TOPSIS. 2023 Improved inverse analysis methods and modified apparent earth pressure for braced excavations in soft clay. 2023 (3)