Social media analysis of the public perception of urban vehicle access regulations

• Autorzy: Ogunkunbi Gabriel , Meszaros Ferenc

Identyfikatory

DOI

10.20858/tp.2023.18.1.13

• Języki publikacji: EN

Abstrakty

EN

Heavy motorisation in the wake of increasing urbanisation is one of the significant transport problems cities face today. There are practical measures under the panoply of urban vehicle access regulations (UVARs) used to stimulate sustainable mobility behaviour changes in the urban population and reduce reliance on passenger car travel. However, the adoption and implementation of such measures are often riddled with challenges, particularly building public acceptability and preserving social justice. Overcoming these challenges will also require cities to understand how the mobility needs of residents change over time. Considering the limitations of conventional data-collection and monitoring approaches, this study explored and analysed the public perception of UVARs over 12 years through natural language processing techniques using social media as a data source. The results show that UVARs are a prominent topic in public discussion and that the average sentiment expressed in tweets tended to be more positive than negative, with a gradual increase observed over the 12-year study period. In addition, the patterns observed in the data and the topics modelled were consistent with the events and talking points in society related to UVARs. Hence, this study demonstrates that social media data can help policymakers assess public sentiments during the ideation, design, implementation, and operational phases of UVARs and other transport policy measures.

Słowa kluczowe

EN

UVAR; congestion charging; emission zone; sentiment analysis; social network data; Twitter

PL

UVAR; opłaty za wjazd; strefa emisji; analiza nastrojów; dane sieci społecznościowych; Twitter

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Transport Problems
• Rocznik: 2023
• Tom: T. 18, z. 1
• Strony: 157--168
• Opis fizyczny: Bibliogr. 33 poz.

Twórcy

autor

Ogunkunbi Gabriel

• Budapest University of Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering; Stoczek str 2, H-1111 Budapest, Hungary

• https://orcid.org/0000-0002-1327-6394

autor

Meszaros Ferenc

• Budapest University of Technology and Economics, Faculty of Transportation Engineering and Vehicle Engineering; Stoczek str 2, H-1111 Budapest, Hungary

• https://orcid.org/0000-0001-6233-0812

Bibliografia

• 1. Mészáros, F. Strategic policy instruments in managing freight transport demand. Periodica Polytechnica Transportation Engineering. 2012. Vol. 40. No.
• 2. P. 77-80. 2. European Commission. Commission Staff Working Document - A call for smarter urban vehicle access regulations. SWD (2013)526 final. Brussels, 2013.
• 3. Lopez, O.N. Urban vehicle access regulations. In: Zeimpekis, V. & Aktas, E. & Bourlakis, M. & Minis, I. (eds.). Sustainable Freight Transport: Theory, Models, and Case Studies. 2018. P. 139- 163. Cham: Springer International Publishing. DOI: https://doi.org/10.1007/978-3-319-62917-9_9.
• 4. Morton, C. & Mattioli, G. & Anable, J. Public acceptability towards Low Emission Zones: The role of attitudes, norms, emotions, and trust. Transportation Research Part A: Policy and Practice. 2021. Vol. 150. P. 256-270.
• 5. Tomanek, R. Free-fare public transport in the concept of sustainable urban mobility. Transport Problems. 2018. Vol. 12(SE). P. 95-105.
• 6. ReVeAL. UVAR guidance: user needs and public acceptance. 2021. Available at: https://civitas-reveal.eu/wp-content/uploads/2021/06/ReVeAL-UVAR-Guidance-User-Needs-and-Public-Acceptance-2021.pdf.
• 7. Ceron, A. & Negri, F. The “social side” of public policy: Monitoring online public opinion and its mobilisation during the policy cycle. Policy & Internet. 2016. Vol. 8(2). P. 131-147.
• 8. Kuflik, T. & Minkov, E. & Nocera, S. & Grant-Muller, S. & Gal-Tzur, A. & Shoor I. Automating a framework to extract and analyse transport related social media content: The potential and the challenges. Transportation Research Part C: Emerging Technologies. 2017. Vol. 77. P. 275-291.
• 9. Yao, W. & Qian, S. From Twitter to traffic predictor: Next-day morning traffic prediction using social media data. Transportation Research Part C: Emerging Technologies. 2021. Vol. 124. No. 102938.
• 10. Xu, S. & Li, S. & Huang, W. & Wen, R. Detecting spatiotemporal traffic events using geosocial media data. Computers, Environment and Urban Systems. 2022. Vol. 94. No. 101797.
• 11. Ram, Y. & Gal-Tzur, A. & Rechavi, A. Identifying attributes of public transport services for urban tourists: A data-mining method. Journal of Transport Geography. 2021. Vol. 93. No. 103069.
• 12. Duran-Rodas, D. & Villeneuve, D. & Wulfhorst, G. Bike-sharing: the good, the bad, and the future: An analysis of the public discussion on Twitter. European Journal of Transport and Infrastructure Research. 2020. Vol. 20(4). P. 38-58.
• 13. Vallejos, S. & Alonso, D.G. & Caimmi, B. & Berdun, L. & Armentano, M.G. & Soria, Á. Mining Social Networks to Detect Traffic Incidents. Inf Syst Front. 2021. Vol. 23(1). P. 115-134.
• 14. Suat-Rojas, N. & Gutierrez-Osorio, C. & Pedraza, C. Extraction and analysis of social networks data to detect traffic accidents. Information. 2022. Vol. 13(1). No. 26. P. 1-29.
• 15. Twitter API for academic research. Products. Available at: https://developer.twitter.com/en/ products/twitter-api/academic-research.
• 16. Barrie, C. & Ho, J. academictwitteR: an R package to access the Twitter Academic Research Product Track v2 API endpoint. JOSS. 2021. Vol. 6(62). No. 3272.
• 17. Nearly 75 Million People Visited Twitter’s Site In January (comScore). TechCrunch. Available at: https://social.techcrunch.com/2010/02/16/twitter-75-million-people-january/.
• 18. Hutto, C. & Gilbert, E. VADER: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the International AAAI Conference on Web and Social Media. 2014. Vol. 8(1). P. 216-225.
• 19. vader_df function - RDocumentation. Available at: https://www.rdocumentation.org/packages/ vader/versions/0.2.1/topics/vader_df.
• 20. Jelodar, H. & Wang, Y. & Yuan, C. & Feng, X. & Jiang, X. & Li, Y. & et al. Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimed Tools Appl. 2019. Vol. 78(11). P. 15169-15211.
• 21. Negara, E.S. & Triadi, D. & Andryani, R. Topic modelling twitter data with Latent Dirichlet Allocation Method. In: 2019 International Conference on Electrical Engineering and Computer Science (ICECOS). 2019. P. 386-390.
• 22. Zola P. & Cortez P. & Carpita M. Twitter user geolocation using web country noun searches. Decision Support Systems. 2019. Vol. 120. P. 50-59.
• 23. Eliasson, J. Efficient transport pricing–why, what, and when? Communications in Transportation Research. 2021. Vol. 1. No 100006.
• 24. Nikitas, A. & Avineri, E. & Parkhurst, G. Understanding the public acceptability of road pricing and the roles of older age, social norms, pro-social values and trust for urban policy-making: The case of Bristol. Cities. 2018. Vol. 79. P. 78-91.
• 25. Shatanawi, M. & Abdelkhalek, F. & Mészáros, F. Urban congestion charging acceptability: an international comparative study. Sustainability. 2020. Vol. 12(12). No. 5044.
• 26. Ortúzar, J. de D. & Bascuñán, R. & Rizzi, L.I. & Salata, A. Assessing the potential acceptability of road pricing in Santiago. Transportation Research Part A: Policy and Practice. 2021. Vol. 144. P. 153-169.
• 27. Thorpe, N. & Hills, P. & Jaensirisak, S. Public attitudes to TDM measures: a comparative study. Transport Policy. 2000. Vol. 7(4). P. 243-257.
• 28. Wicki, M. & Huber, R.A. & Bernauer, T. Can policy-packaging increase public support for costly policies? Insights from a choice experiment on policies against vehicle emissions. Journal of Public Policy. 2020. Vol. 40(4). P. 599-625.
• 29. Eriksson, L. & Garvill, J. & Nordlund, A.M. Acceptability of travel demand management measures: The importance of problem awareness, personal norm, freedom, and fairness. Journal of environmental psychology. 2006. Vol. 26(1). P. 15-26.
• 30. Urban Access Regulations. Available at: https://urbanaccessregulations.eu/userhome/map.
• 31. Jaensirisak, S. & Wardman, M. & May, A.D. Explaining variations in public acceptability of road pricing schemes. Journal of Transport Economics and Policy (JTEP). 2005. Vol. 39(2). P. 127-154.
• 32. Schade, J. & Schlag, B. Acceptability of urban transport pricing strategies. Transportation Research Part F: Traffic Psychology and Behaviour. 2003. Vol. 6(1). P. 45-61.
• 33. Mellon, J. & Prosser, C. Twitter and Facebook are not representative of the general population: Political attitudes and demographics of British social media users. Research & Politics. 2017. Vol. 4(3). No 2053168017720008.