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Using Twitter data for transit performance assessment: a framework for evaluating transit riders’ opinions about quality of service

Author

Listed:
  • N. Nima Haghighi

    (University of Utah)

  • Xiaoyue Cathy Liu

    (University of Utah)

  • Ran Wei

    (University of California at Riverside)

  • Wenwen Li

    (Arizona State University)

  • Hu Shao

    (Arizona State University)

Abstract

Social media platforms such as Facebook, Instagram, and Twitter have drastically altered the way information is generated and disseminated. These platforms allow their users to report events and express their opinions toward these events. The profusion of data generated through social media has proved to have the potential for improving the efficiency of existing traffic management systems and transportation analytics. This study complements existing literature by proposing a framework to evaluate transit riders’ opinion about quality of transit service using Twitter data. Although previous studies used keyword search to extract transit-related tweets, the extracted tweets can still be noisy and might not be relevant to transit quality of service at all. In this study, we leverage topic modeling, an unsupervised machine learning technique, to sift tweets that are relevant to the actual user experience of the transit system. Sentiment analysis is further performed based on the tweet-per-topic index we developed, to gauge transit riders’ feedback and explore the underlying reasons causing their dissatisfaction on the service. This framework can be potentially quite useful to transit agencies for user-oriented analysis and to assist with investment decision making.

Suggested Citation

  • N. Nima Haghighi & Xiaoyue Cathy Liu & Ran Wei & Wenwen Li & Hu Shao, 2018. "Using Twitter data for transit performance assessment: a framework for evaluating transit riders’ opinions about quality of service," Public Transport, Springer, vol. 10(2), pages 363-377, August.
    • Handle: RePEc:spr:pubtra:v:10:y:2018:i:2:d:10.1007_s12469-018-0184-4
    DOI: 10.1007/s12469-018-0184-4
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Irina Wedel & Michael Palk & Stefan Voß, 2022. "A Bilingual Comparison of Sentiment and Topics for a Product Event on Twitter," Information Systems Frontiers, Springer, vol. 24(5), pages 1635-1646, October.
    2. Shuli Luo & Sylvia Y He, 2021. "Using data mining to explore the spatial and temporal dynamics of perceptions of metro services in China: The case of Shenzhen," Environment and Planning B, , vol. 48(3), pages 449-466, March.
    3. Mohammad Masoud Rahimi & Elham Naghizade & Mark Stevenson & Stephan Winter, 2023. "SentiHawkes: a sentiment-aware Hawkes point process to model service quality of public transport using Twitter data," Public Transport, Springer, vol. 15(2), pages 343-376, June.
    4. Amirali Soltanpour & Mahmoud Mesbah & Meeghat Habibian, 2020. "Customer satisfaction in urban rail: a study on transferability of structural equation models," Public Transport, Springer, vol. 12(1), pages 123-146, March.
    5. Tasnim M. A. Zayet & Maizatul Akmar Ismail & Kasturi Dewi Varathan & Rafidah M. D. Noor & Hui Na Chua & Angela Lee & Yeh Ching Low & Sheena Kaur Jaswant Singh, 2021. "Investigating transportation research based on social media analysis: a systematic mapping review," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6383-6421, August.
    6. 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.
    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. Wenwen Zhang & Camille Barchers & Janille Smith-Colin, 2023. "Transit communication via Twitter during the COVID-19 pandemic," Environment and Planning B, , vol. 50(5), pages 1244-1261, June.
    9. Liping Ge & Stefan Voß & Lin Xie, 2022. "Robustness and disturbances in public transport," Public Transport, Springer, vol. 14(1), pages 191-261, March.
    10. Liping Ge & Malek Sarhani & Stefan Voß & Lin Xie, 2021. "Review of Transit Data Sources: Potentials, Challenges and Complementarity," Sustainability, MDPI, vol. 13(20), pages 1-37, October.
    11. Luo, Shuli & He, Sylvia Y. & Grant-Muller, Susan & Song, Linqi, 2023. "Influential factors in customer satisfaction of transit services: Using crowdsourced data to capture the heterogeneity across individuals, space and time," Transport Policy, Elsevier, vol. 131(C), pages 173-183.
    12. Dibya Nandan Mishra & Rajeev Kumar Panda, 2023. "Decoding customer experiences in rail transport service: application of hybrid sentiment analysis," Public Transport, Springer, vol. 15(1), pages 31-60, March.
    13. Eldeeb, Gamal & Sears, Sean & Mohamed, Moataz, 2023. "What do users want from transit? Qualitative analysis of current and potential users' perceptions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 171(C).

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