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Mining Public Opinion on Transportation Systems Based on Social Media Data

Author

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  • Dawei Li

    (Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 210096, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic, Southeast University, Nanjing 210096, China)

  • Yujia Zhang

    (Jiangsu Key Laboratory of Urban ITS, School of Transportation, Southeast University, Nanjing 210096, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic, Southeast University, Nanjing 210096, China)

  • Cheng Li

    (China Academy of Transportation Sciences, No.240, Huixinli, Chaoyang District, Beijing 100029, China)

Abstract

Public participation plays an important role of traffic planning and management, but it is a great challenge to collect and analyze public opinions for traffic problems on a large scale under traditional methods. Traffic management departments should appropriately adopt public opinions in order to formulate scientific and reasonable regulations and policies. At present, while increasing degree of public participation, data collection and processing should be accelerated to make up for the shortcomings of traditional planning. This paper focuses on text analysis using large data with temporal and spatial attributes of social network platform. Web crawler technology is used to obtain traffic-related text in mainstream social platforms. After basic treatment, the emotional tendency of the text is analyzed. Then, based on the probabilistic topic modeling (latent Dirichlet allocation model), the main opinions of the public are extracted, and the spatial and temporal characteristics of the data are summarized. Taking Nanjing Metro as an example, the existing problems are summarized from the public opinions and improvement measures are put forward, which proves the feasibility of providing technical support for public participation in public transport with social media big data.

Suggested Citation

  • Dawei Li & Yujia Zhang & Cheng Li, 2019. "Mining Public Opinion on Transportation Systems Based on Social Media Data," Sustainability, MDPI, vol. 11(15), pages 1-15, July.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:15:p:4016-:d:251414
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    References listed on IDEAS

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    Cited by:

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    2. Paula Vasquez-Henriquez & Eduardo Graells-Garrido & Diego Caro, 2020. "Tweets on the Go: Gender Differences in Transport Perception and Its Discussion on Social Media," Sustainability, MDPI, vol. 12(13), pages 1-21, July.
    3. Armah, Abdul Karim & Li, Jinfa, 2023. "Generational cohorts’ social media acceptance as a delivery tool in sub-Sahara Africa motorcycle industry: The role of cohort technical know-how in technology acceptance," Technology in Society, Elsevier, vol. 75(C).
    4. Hansu Hwang & SeJin An & Eunchang Lee & Suhyeon Han & Cheon-hwan Lee, 2021. "Cross-Societal Analysis of Climate Change Awareness and Its Relation to SDG 13: A Knowledge Synthesis from Text Mining," Sustainability, MDPI, vol. 13(10), pages 1-21, May.
    5. Zha, Wenbin & Ye, Qian & Li, Jian & Ozbay, Kaan, 2023. "A social media Data-Driven analysis for transport policy response to the COVID-19 pandemic outbreak in Wuhan, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 172(C).
    6. Sharon Shoshany-Tavory & Tamar Trop & Yoram Shiftan, 2024. "A cross-cultural study of nonprofit self-organized ridesharing," Transportation, Springer, vol. 51(2), pages 717-757, April.
    7. Shaojie Liu & Jing Teng & Yue Gong, 2020. "Extraction Method and Integration Framework for Perception Features of Public Opinion in Transportation," Sustainability, MDPI, vol. 13(1), pages 1-17, December.
    8. Zhu, Chunxiao & Shou, Minghuan & Zhou, Yitong & Li, Wenrui, 2023. "Modeling the effect of social media on older adults’ usage intention of public transport," Economic Analysis and Policy, Elsevier, vol. 77(C), pages 239-250.

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