Can Pei | Transportation Engineering | Innovative Research Award

Innovative Research Award

Can Pei
Shenzhen Polytechnic University

Can Pei
Affiliation Shenzhen Polytechnic University
Country China
Scopus ID 57325854200
Documents 6
Citations 106
h-index 2
Subject Area Transportation Engineering
Event Global Civil Engineering Awards
ORCID 0000-0002-3206-8072

Can Pei is affiliated with Shenzhen Polytechnic University and is identified in the supplied research record with a specialization in Transportation Engineering. The profile records six documents, 106 citations, and an h-index of 2. These indicators provide a bibliometric context for considering the researcher for recognition through the Innovative Research Award associated with the Global Civil Engineering Awards. [1]

Abstract

Can Pei, affiliated with Shenzhen Polytechnic University, is identified in the supplied academic profile as a researcher in Transportation Engineering. The record contains six documents, 106 citations, and an h-index of 2, providing measurable indicators of scholarly activity and research visibility. This article presents a concise academic recognition profile prepared in connection with the Innovative Research Award under the Global Civil Engineering Awards. It summarizes the available bibliometric information, research orientation, documented publication activity, potential research impact, and relevance to civil engineering recognition. The profile is intended as a neutral scholarly overview based on supplied records.

Keywords

Can Pei; Shenzhen Polytechnic University; Transportation Engineering; Civil Engineering; Research Profile; Scholarly Publications; Research Impact; Innovative Research Award; Global Civil Engineering Awards.

Introduction

Transportation Engineering is a major area of civil engineering concerned with the planning, design, operation, and evaluation of transportation systems. Within this context, Can Pei’s supplied academic profile identifies Transportation Engineering as the principal subject area. The available bibliometric record offers a basis for describing the research profile and evaluating its relevance to academic recognition. [1]

Research Profile

The supplied profile associates Can Pei with Shenzhen Polytechnic University and Transportation Engineering. The Scopus author identifier is 57325854200, while the recorded publication metrics include six documents, 106 citations, and an h-index of 2. ORCID identifier 0000-0002-3206-8072 provides an additional persistent researcher identifier for distinguishing the academic record. [1] [2]

Research Contributions

The available information indicates an academic contribution centered on Transportation Engineering, a field that supports the development and assessment of transportation infrastructure and systems. Because detailed publication titles, methodologies, and individual study outcomes were not supplied, specific technical contributions cannot be independently characterized here. The documented research activity nevertheless establishes a defined disciplinary profile. [1]

Publications

The supplied Scopus information records six documents associated with the researcher. These documents constitute the available publication base for this recognition profile, while the recorded citation count indicates that the indexed work has received scholarly references. Individual publication titles, journals, publication dates, and DOI identifiers were not provided and are therefore not reproduced without verification. [1]

Research Impact

Research impact can be considered through scholarly visibility, citation activity, disciplinary relevance, and the practical significance of research outputs. The supplied record reports 106 citations across six documents and an h-index of 2, providing quantitative indicators of academic reach. These metrics should be interpreted alongside publication quality, originality, and substantive research outcomes when assessing overall impact. [1]

Award Suitability

The Innovative Research Award is presented here in relation to the Global Civil Engineering Awards. Can Pei’s identified subject area of Transportation Engineering provides a direct disciplinary connection to civil engineering. The documented research activity and bibliometric indicators support consideration for recognition, subject to the award’s formal eligibility requirements and independent evaluation of the underlying research contributions. [1] [3]

Conclusion

Can Pei’s supplied academic record presents a focused profile in Transportation Engineering at Shenzhen Polytechnic University. Six documented publications, 106 citations, and an h-index of 2 provide measurable evidence of scholarly activity. Together with the researcher’s disciplinary alignment, these details form a reasonable academic basis for consideration under the Innovative Research Award, while final recognition remains subject to formal review.

References

  1. Elsevier. (n.d.). Scopus author details: Can Pei, Author ID 57325854200. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57325854200
  2. ORCID. (n.d.). ORCID record: Can Pei, ORCID 0000-0002-3206-8072. ORCID.
    https://orcid.org/0000-0002-3206-8072
  3. Global Civil Engineering Awards. (n.d.). Official award website.
    https://civilengineeringawards.com

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