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

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  • Yanni Liang

    (School of Economics and Management, Tongji University, Shanghai 200092, China
    School of Economics and Management, Beibu Gulf University, Qinzhou 535011, China
    Beibu Gulf Marine Development Research Center, Qinzhou 535011, 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)

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.

Suggested Citation

  • Yanni Liang & Jianxin You & Ran Wang & Bo Qin & Shuo Han, 2024. "Urban Transportation Data Research Overview: A Bibliometric Analysis Based on CiteSpace," Sustainability, MDPI, vol. 16(22), pages 1-45, November.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:22:p:9615-:d:1514083
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    References listed on IDEAS

    as
    1. Patel, Ashish Singh & Tiwari, Vivek & Ojha, Muneendra & Vyas, O.P., 2023. "Ontology-based detection and identification of complex event of illegal parking using SPARQL and description logic queries," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    2. Elżbieta Macioszek & Anna Granà & Paulo Fernandes & Margarida C. Coelho, 2022. "New Perspectives and Challenges in Traffic and Transportation Engineering Supporting Energy Saving in Smart Cities—A Multidisciplinary Approach to a Global Problem," Energies, MDPI, vol. 15(12), pages 1-8, June.
    3. Trisalyn Nelson & Colin Ferster & Karen Laberee & Daniel Fuller & Meghan Winters, 2021. "Crowdsourced data for bicycling research and practice," Transport Reviews, Taylor & Francis Journals, vol. 41(1), pages 97-114, January.
    4. Tengfei Li & Xuanrui Xiong & Guifeng Zheng & Ying Li & Amr Tolba, 2023. "A Blockchain-Based Shared Bus Service Scheduling and Management System," Sustainability, MDPI, vol. 15(16), pages 1-27, August.
    5. Hyomin Park & Minkyung Kim & Sangdon Lee, 2021. "Spatial Characteristics of Wildlife-Vehicle Collisions of Water Deer in Korea Expressway," Sustainability, MDPI, vol. 13(24), pages 1-13, December.
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