Urban Transportation Data Research Overview: A Bibliometric Analysis Based on CiteSpace

Yanni Liang, Jianxin You (), Ran Wang, Bo Qin and Shuo Han
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Yanni Liang: School of Economics and Management, Tongji University, Shanghai 200092, China
Jianxin You: School of Economics and Management, Tongji University, Shanghai 200092, China
Ran Wang: State Cloud Technology Company, Guangzhou 510308, China
Bo Qin: School of Economics and Management, Beibu Gulf University, Qinzhou 535011, China
Shuo Han: School of Economics and Management, Beibu Gulf University, Qinzhou 535011, China

Sustainability, 2024, vol. 16, issue 22, 1-45

Abstract: Urban transportation data are crucial for smart city development, enhancing traffic management’s intelligence, accuracy, and efficiency. This paper conducts a comprehensive investigation encompassing policy analysis, a literature review, concept definition, and quantitative analysis using CiteSpace from both domestic and international perspectives. Urban transportation data comprise multiple dimensions, such as infrastructure status, real-time monitoring, policy planning, and environmental assessment, which originate from various sources and stakeholders. Highly influential authors and active institutions, particularly in the USA, China, Canada, and England, contribute significantly to extensive and collaborative research. Key areas include intelligent transportation, traffic flow prediction, data fusion, and deep learning. Domestic research focuses on practical applications, while international studies delve into interdisciplinary research areas. With advancements in intelligent systems and big data technology, research has evolved from basic data collection to sophisticated methodologies, such as deep learning and spatiotemporal analysis, driving substantial progress. This paper concludes by recommending enhanced data integration, improved privacy and security, fostering big data and AI applications, facilitating policy formulation, and exploring innovative transportation modes, thereby underscoring the importance of urban transportation data in shaping the future of smart cities. The findings provide theoretical and practical guidance for the future intelligence, efficiency, and sustainability of urban transportation systems.

Keywords: urban transportation data; intelligent transportation system; concept definition; CiteSpace; sustainable transportation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
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