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Investigating the Impacting Factors on the Public’s Attitudes towards Autonomous Vehicles Using Sentiment Analysis from Social Media Data

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  • Shengzhao Wang

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, 4800 Cao’an Highway, Shanghai 201804, China)

  • Meitang Li

    (Human Factors Group, University of Michigan Transportation Research Institute, 2901 Baxter Rd., Ann Arbor, MI 48109, USA)

  • Bo Yu

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, 4800 Cao’an Highway, Shanghai 201804, China)

  • Shan Bao

    (Human Factors Group, University of Michigan Transportation Research Institute, 2901 Baxter Rd., Ann Arbor, MI 48109, USA
    Industrial and Manufacturing Systems Engineering Department, University of Michigan—Dearborn, 4901 Evergreen Rd., Dearborn, MI 48128, USA)

  • Yuren Chen

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, 4800 Cao’an Highway, Shanghai 201804, China)

Abstract

The attitudes of the public play a critical role in the acceptance, purchase, utilization, and research and development of autonomous vehicles (AVs). Currently, the attitudes of the public toward AVs have been mostly estimated through traditional survey data, which bears a low quantity of samples with high labor costs. It is probably also one of the reasons why the critical factors on the attitudes of the public toward AVs have not been studied from a comprehensive perspective yet. To address the issue, this study aims to propose a method by using large-scale social media data to investigate key factors that affect the attitudes of the public toward AVs. A total of 954,151 Twitter data related to AVs and 53 candidate independent variables from seven categories were extracted using the web scraping method. Then, sentiment analysis was used to measure the public attitudes towards AVs by calculating sentiment scores. Random forests algorithm was employed to preliminarily select candidate independent variables according to their importance and a linear mixed model was utilized to explore the impacting factors, considering the unobserved heterogeneities caused by the subjectivity level of tweets. The results showed that the attitudes of the public toward AVs were slightly optimistic. Factors, such as “drunk”, “blind spot”, and “mobility”, had the largest impacts on public attitudes. In addition, people were more likely to express positive feelings when talking about words, such as “lidar” and “Tesla”, related to high technologies. Conversely, factors, such as “COVID-19”, “pedestrian”, “sleepy”, and “highway”, were found to have significantly negative effects on the attitudes of the public. The findings of this study are beneficial for the development of AV technologies, the guidelines for AV-related policy formulation, and the understanding and acceptance of the public toward AVs.

Suggested Citation

  • Shengzhao Wang & Meitang Li & Bo Yu & Shan Bao & Yuren Chen, 2022. "Investigating the Impacting Factors on the Public’s Attitudes towards Autonomous Vehicles Using Sentiment Analysis from Social Media Data," Sustainability, MDPI, vol. 14(19), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12186-:d:925544
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    References listed on IDEAS

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    1. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
    2. Nielsen, Thomas Alexander Sick & Haustein, Sonja, 2018. "On sceptics and enthusiasts: What are the expectations towards self-driving cars?," Transport Policy, Elsevier, vol. 66(C), pages 49-55.
    3. Kapser, Sebastian & Abdelrahman, Mahmoud & Bernecker, Tobias, 2021. "Autonomous delivery vehicles to fight the spread of Covid-19 – How do men and women differ in their acceptance?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 183-198.
    4. Bennett, Roger & Vijaygopal, Rohini & Kottasz, Rita, 2019. "Attitudes towards autonomous vehicles among people with physical disabilities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 127(C), pages 1-17.
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