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Identification of Enablers and Barriers for Public Bike Share System Adoption using Social Media and Statistical Models

Author

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  • Ainhoa Serna

    (Computer Science and Artificial Intelligence Department, University of the Basque Country UPV/EHU, 20018 Donostia-San Sebastián, Spain)

  • Tomas Ruiz

    (Transport Department, School of Civil Engineering, Universitat Politècnica de Valencia, Camino de Vera s/n, 46022, Valencia, Spain)

  • Jon Kepa Gerrikagoitia

    (IDEKO, ICT and Automation Research Group, Arriaga 2, 20870 Elgoibar, Spain)

  • Rosa Arroyo

    (Transport Department, School of Civil Engineering, Universitat Politècnica de Valencia, Camino de Vera s/n, 46022, Valencia, Spain)

Abstract

Public bike share (PBS) systems are meant to be a sustainable urban mobility solution in areas where different travel options and the practice of active transport modes can diminish the need on the vehicle and decrease greenhouse gas emission. Although PBS systems have been included in transportation plans in the last decades experiencing an important development and growth, it is crucial to know the main enablers and barriers that PBS systems are facing to reach their goals. In this paper, first, sentiment analysis techniques are applied to user generated content (UGC) in social media comments (Facebook, Twitter and TripAdvisor) to identify these enablers and barriers. This analysis provides a set of explanatory variables that are combined with data from official statistics and the PBS observatory in Spain. As a result, a statistical model that assesses the connection between PBS use and certain characteristics of the PBS systems, utilizing sociodemographic, climate, and positive and negative opinion data extracted from social media is developed. The outcomes of the research work show that the identification of the main enablers and barriers of PBS systems can be effectively achieved following the research method and tools presented in the paper. The findings of the research can contribute to transportation planners to uncover the main factors related to the adoption and use of PBS systems, by taking advantage of publicly available data sources.

Suggested Citation

  • Ainhoa Serna & Tomas Ruiz & Jon Kepa Gerrikagoitia & Rosa Arroyo, 2019. "Identification of Enablers and Barriers for Public Bike Share System Adoption using Social Media and Statistical Models," Sustainability, MDPI, vol. 11(22), pages 1-21, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:22:p:6259-:d:284676
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    References listed on IDEAS

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    1. Ainhoa Serna & Elena Marchiori & Jon Kepa Gerrikagoitia & Aurkene Alzua-Sorzabal & Lorenzo Cantoni, 2015. "An Auto-Coding Process for Testing the Cognitive-Affective and Conative Model of Destination Image," Springer Books, in: Iis Tussyadiah & Alessandro Inversini (ed.), Information and Communication Technologies in Tourism 2015, edition 127, pages 111-123, Springer.
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    5. Shaheen, Susan A & Guzman, Stacey & Zhang, Hua, 2010. "Bikesharing in Europe, the Americas, and Asia: Past, Present and Future," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6qg8q6ft, Institute of Transportation Studies, UC Berkeley.
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    Cited by:

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    3. Susan Hull Grasso & Philip Barnes & Celeste Chavis, 2020. "Bike Share Equity for Underrepresented Groups: Analyzing Barriers to System Usage in Baltimore, Maryland," Sustainability, MDPI, vol. 12(18), pages 1-17, September.
    4. 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.
    5. Martin Zajac & Jiří Horák & Joaquín Osorio-Arjona & Pavel Kukuliač & James Haworth, 2022. "Public Transport Tweets in London, Madrid and Prague in the COVID-19 Period—Temporal and Spatial Differences in Activity Topics," Sustainability, MDPI, vol. 14(24), pages 1-25, December.
    6. Wang, Yacan & Douglas, Matthew & Hazen, Benjamin, 2021. "Diffusion of public bicycle systems: Investigating influences of users’ perceived risk and switching intention," Transportation Research Part A: Policy and Practice, Elsevier, vol. 143(C), pages 1-13.
    7. Ainhoa Serna & Aitor Soroa & Rodrigo Agerri, 2021. "Applying Deep Learning Techniques for Sentiment Analysis to Assess Sustainable Transport," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    8. Marta Christina SUCIU & Marco SAVASTANO & Gheorghe Alexandru STATIVA & Irina GORELOVA, 2020. "Smart mobility: a comparison between the social media strategies for the public urban mobility services of Rome and Bucharest," Smart Cities International Conference (SCIC) Proceedings, Smart-EDU Hub, Faculty of Public Administration, National University of Political Studies & Public Administration, vol. 8, pages 303-320, November.

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