Public Opinion on Dockless Bike Sharing: A Machine Learning Approach
Abstract
Dockless bike sharing is an emerging paradigm. Like many other technologies, it brings advantages and disadvantages to communities. Further investigation into public opinion will shed light on the impact of this technology on communities and provide input to city authorities for transportation planning. Transportation planning processes can be enhanced by engaging the community through social media technologies. Social media like Twitter, Facebook, and other microblogging media have been used for planning, but have not been extensively evaluated for that purpose. This study examined approximately 32,000 posts on Twitter to assess public opinion on dockless bike-sharing systems. Using a mix of text mining and statistical techniques, we examined relevant posts to determine the sentiment polarity of tweets, the underlying topics in the tweets, and the extent of engagement and impact on the decision-making process. Results given by two different sentiment algorithms show that there is more positive than negative polarity across the algorithms. Also, the findings show that the underlying topics in tweets include electric scooters, private e-hailing companies, and blockage of sidewalks, among others. The results indicate that the dockless shared mobility models are potentially useful in generating participation, but faced substantial technical, analytical, and communication barriers to influencing decision-making.
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ML has been applied to monitor public sentiment about bike shares via tweets.
“ML has been applied to monitor public sentiment about such bike shares via tweets [775].”
From Tackling climate change with machine learning (2023) · cites this paper as [775]
ML can distill information on urban issues related to climate change through web-scraping and text-mining.
“ML can also distill information on urban issues related to climate change through web-scraping and text-mining, e.g., [775].”
From Tackling climate change with machine learning (2023) · cites this paper as [775]
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- Authors
- Rahim Taleqani, A., Hough, J., & Nygard, K. E.
- Affiliations
- Transportation, Logistics and Finance Department, North Dakota State University, Fargo, ND
- North Dakota State University (US)
- ORCID
- Hough, Jill
- Venue
- Transportation Research Record: Journal of the Transportation Research Board
- Publisher
- SAGE Publications
- Published
- 2019-04
- Volume
- 2673
- Issue
- 4
- Pages
- 195-204
- ISSN
- 0361-1981 (ISSN-L)
- 2169-4052
- OpenAlex
- W2933112115
- Cited by
- 47
- References
- 18
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 4 · 2023: 7 · 2022: 6 · 2021: 10 · 2020: 7 · 2019: 5
- FWCI
- 7.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Transportation Research Record Journal of the Transportation Research Board (publishedVersion)
- Topics
- Social Sciences › Transportation › Urban Transport and Accessibility
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Engineering › Automotive Engineering › Transportation and Mobility Innovations
- Keywords
- dockless bike-sharing, public opinion, Twitter, sentiment analysis, shared mobility, electric scooters, transportation planning, community participation
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