Perceived Responses of International Tourists to Transportation and Tourism Services During Typhoons Faxai and Hagibis in Japan
Abstract
:1. Introduction
2. Literature Review
2.1. Tourism Crisis Management and Crisis Communication
2.2. Social Media Use in Disasters
2.3. Text Mining Approaches
3. Materials and Methods
3.1. Methodological Framework Using Topic Modeling and Sentiment Analysis
3.2. Data Extraction
3.3. Data Pre-Processing
3.4. Embedding BERT Topic Modeling
3.5. Topic-Based Sentiment Analysis
4. Case Study
4.1. Typhoons Faxai and Hagibis in Japan
4.2. Data Collection and Pre-Processing
4.3. Topics Generated by Public and Official Tweets
4.4. Temporal Changes of Public Sentiments
4.5. Discussion
4.6. Theoretical and Practical Implications
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Mileti, D.S.; O’Brien, P.W. Warnings during Disaster: Normalizing Communicated Risk. Soc. Probl. 1992, 39, 40–57. [Google Scholar] [CrossRef]
- Spence, P.R.; Westerman, D.; Skalski, P. Proxemic Effects on Information Seeking Following the 9/11 Attacks. Commun. Res. Rep. 2005, 22, 39–46. [Google Scholar] [CrossRef]
- Drabek, T.E. Disaster Responses within the Tourism Industry. Int. J. Mass Emerg. Disasters 1995, 13, 7–23. [Google Scholar] [CrossRef]
- Cahyanto, I.; Pennington-Gray, L.; Thapa, B.; Srinivasan, S.; Villegas, J.; Matyas, C.; Kiousis, S. Predicting Information Seeking Regarding Hurricane Evacuation in the Destination. Tour. Manag. 2016, 52, 264–275. [Google Scholar] [CrossRef]
- Kawasaki, A.; Henry, M.; Meguro, K. Media Preference, Information Needs, and the Language Proficiency of Foreigners in Japan after the 2011 Great East Japan Earthquake. Int. J. Disaster Risk Sci. 2018, 9, 116–128. [Google Scholar] [CrossRef]
- Choi, S. Comparative Study on Disaster Prevention Consciousness and Evacuation Intention of Inbound Tourists and Japanese. In Proceedings of the Travel and Tourism Research Association: Advancing Tourism Research Globally 2020 (2020 TTRA), Victoria, BC, Canada, 16–18 June 2020; p. 72. [Google Scholar]
- Choi, S.; Yoshida, K.; Yamaji, H.; Shindo, H.; Maharjan, R. Factors Influencing Disaster Response Behavior of International Tourists under the Tokyo Metropolitan Earthquake Scenario. Front. Sustain. Tour. 2023, 2, 1205084. [Google Scholar] [CrossRef]
- Hatayama, M.; Kosaka, T.; Hernandez, A.H. Analysis on Tsunami Evacuation Options with Agent-Based Simulation in Tourist Area. In Proceedings of 2018 5th International Conference on Information and Communication Technologies for Disaster Management (ICT-DM), Sendai, Japan, 4–7 December 2018; IEEE: New York, NY, USA, 2018; pp. 1–8. [Google Scholar]
- Takabatake, T.; Shibayama, T.; Esteban, M.; Ishii, H. Simulated tsunami evacuation behavior of local residents and visitors in Kamakura, Japan. Int. J. Disaster Risk Reduct. 2017, 23, 1–14. [Google Scholar] [CrossRef]
- Choi, S.; Maharjan, R.; Hong, T.T.N.; Hanaoka, S. Impact of Information Provision on Tsunami Evacuation Behavior of Residents and International Tourists in Japan. Transp. Policy 2024, 155, 264–273. [Google Scholar] [CrossRef]
- Ritchie, B.W. Chaos, Crises and Disasters: A Strategic Approach to Crisis Management in the Tourism Industry. Tour. Manag. 2004, 25, 669–683. [Google Scholar] [CrossRef]
- Cahyanto, I.; Pennington-Gray, L. Communicating Hurricane Evacuation to Tourists: Gender, Past Experience with Hurricanes, and Place of Residence. J. Travel Res. 2015, 54, 329–343. [Google Scholar] [CrossRef]
- Arce, R.S.C.; Onuki, M.; Esteban, M.; Shibayama, T. Risk Awareness and Intended Tsunami Evacuation Behavior of Tourists in Kamakura City, Japan. Int. J. Disaster Risk Reduct. 2017, 23, 178–192. [Google Scholar] [CrossRef]
- Aliperti, G.; Nagai, H.; Cruz, A.M. Communicating Risk to Tourists: A Mental Models Approach to Identifying Gaps and Misperceptions. Tour. Manag. Perspect. 2020, 33, 100615. [Google Scholar] [CrossRef]
- Alrawad, M.; Lutfi, A.; Almaiah, M.A.; Alsyouf, A.; Arafa, H.M.; Soliman, Y.; Elshaer, I.A. A Novel Framework of Public Risk Assessment Using an Integrated Approach Based on AHP and Psychometric Paradigm. Sustainability 2023, 15, 9965. [Google Scholar] [CrossRef]
- Seneviratne, K.; Nadeeshani, M.; Senaratne, S.; Perera, S. Use of Social Media in Disaster Management: Challenges and Strategies. Sustainability 2024, 16, 4824. [Google Scholar] [CrossRef]
- Chung, N.; Koo, C. The Use of Social Media in Travel Information Search. Telemat. Inform. 2015, 32, 215–229. [Google Scholar] [CrossRef]
- Kwak, S.Y.; Shin, M.; Lee, M.; Back, K.J. Integrating the Reviewers’ and Readers’ Perceptions of Negative Online Reviews for Customer Decision-Making: A Mixed-Method Approach. Int. J. Contemp. Hosp. Manag. 2023, 35, 4191–4216. [Google Scholar] [CrossRef]
- Sousa, N.; Alén, E.; Losada, N.; Melo, M. Breaking Barriers: Unveiling Challenges of Tourism and Hospitality Management. Tour. Hosp. Manag. 2024, 30, 269–282. [Google Scholar] [CrossRef]
- Sousa, N.; Alén, E.; Losada, N.; Melo, M. Influencing Wine Tourists’ Decision-Making with VR: The Impact of Immersive Experiences on Their Behavioural Intentions. Tour. Manag. Perspect. 2024, 51, 101235. [Google Scholar] [CrossRef]
- Liu-Lastres, B. Beyond Simple Messaging: A Review of Crisis Communication Research in Hospitality and Tourism. Int. J. Contemp. Hosp. Manag. 2022, 34, 1959–1983. [Google Scholar] [CrossRef]
- Vera-Burgos, C.M.; Griffin Padgett, D.R. Using Twitter for Crisis Communications in a Natural Disaster: Hurricane Harvey. Heliyon 2020, 6, e04804. [Google Scholar] [CrossRef]
- Pennington-Gray, L.; Schroeder, A. International Tourists’ Perceptions of Safety & Security: The Role of Social Media. Matkailututkimus 2013, 9, 7–20. [Google Scholar]
- Panagiotopoulos, P.; Barnett, J.; Bigdeli, A.Z.; Sams, S. Social Media in Emergency Management: Twitter as a Tool for Communicating Risks to the Public. Technol. Forecast. Soc. Chang. 2016, 111, 86–96. [Google Scholar] [CrossRef]
- Schultz, F.; Utz, S.; Göritz, A. Is the medium the message? Perceptions of and reactions to crisis communication via twitter, blogs and traditional media. Public Relat. Rev. Rev. Commun. 2011, 37, 20–27. [Google Scholar] [CrossRef]
- Cabinet Office Japan. White Paper on Disaster Management. 2022. Available online: https://www.bousai.go.jp/en/documentation/white_paper/pdf/2022/R4_hakusho_english.pdf (accessed on 13 October 2024).
- Japan Tourism Agency. Useful Tools for Foreign Tourists Visiting Japan in Case of a Disaster (In Japanese). 2023. Available online: https://www.mlit.go.jp/kankocho/seisaku_seido/kihonkeikaku/jizoku_kankochi/anzenkakuho/inbound/tool.html (accessed on 13 October 2024).
- Morelli, S.; Pazzi, V.; Nardini, O.; Bonati, S. Framing Disaster Risk Perception and Vulnerability in Social Media Communication: A Literature Review. Sustainability 2022, 14, 9148. [Google Scholar] [CrossRef]
- Martín, Y.; Cutter, S.L.; Li, Z.; Emrich, C.; Mitchell, J.T. Using geotagged tweets to track population movements to and from Puerto Rico after Hurricane Maria. Popul. Environ. 2020, 42, 4–27. [Google Scholar] [CrossRef]
- Bonta, V.; Kumaresh, N.; Janardhan, N. A Comprehensive Study on Lexicon Based Approaches for Sentiment Analysis. Asian J. Comput. Sci. Technol. 2019, 8, 1–6. [Google Scholar] [CrossRef]
- Terracciano, E.; Han, A.T. Twitter Communication During Winter Storm Uri in San Antonio, Texas: Implications for Climate Resiliency Planning. Cities 2023, 136, 104407. [Google Scholar] [CrossRef]
- Devlin, J.; Chang, M.W.; Lee, K.; Toutanova, K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv 2018, arXiv:1810.04805. [Google Scholar]
- Hutto, C.; Gilbert, E. VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. In Proceedings of the International AAAI Conference on Web and Social Media, Ann Arbor, MI, USA, 1–4 June 2014; pp. 216–225. [Google Scholar]
- Ritchie, B.W.; Jiang, W. A Review of Research on Tourism Risk, Crisis and Disaster Management: Launching the Annals of Tourism Research Curated Collection on Tourism Risk, Crisis and Disaster Management. Ann. Tour. Res. 2019, 79, 102812. [Google Scholar] [CrossRef]
- Mair, J.; Ritchie, B.W.; Walters, G. Towards a Research Agenda for Post-Disaster and Post-Crisis Recovery Strategies for Tourist Destinations: A Narrative Review. Curr. Issues Tour. 2016, 19, 1–26. [Google Scholar] [CrossRef]
- Faulkner, B. Towards a Framework for Tourism Disaster Management. Tour. Manag. 2001, 22, 135–147. [Google Scholar] [CrossRef]
- Möller, C.; Wang, J.; Nguyen, H.T. #Strongerthanwinston: Tourism and Crisis Communication through Facebook following Tropical Cyclones in Fiji. Tour. Manag. 2018, 68, 272–284. [Google Scholar] [CrossRef]
- Aliperti, G.; Cruz, A.M. Risk Communication to Tourists: Towards the Definition of a Research Agenda for a More Effective Disaster Preparedness in Japan. Almatourism 2018, 9, 1–12. [Google Scholar] [CrossRef]
- Martinez-Rojas, M.; del Carmen Pardo-Ferreira, M.; Rubio-Romero, J.C. Twitter as a Tool for the Management and Analysis of Emergency Situations: A Systematic Literature Review. Int. J. Inf. Manag. 2018, 43, 196–208. [Google Scholar] [CrossRef]
- Aladwani, A.M.; Dwivedi, Y.K. Towards a Theory of Socio-Citizenry: Quality Anticipation, Trust Configuration, and Approved Adaptation of Governmental Social Media. Int. J. Inf. Manag. 2018, 43, 261–272. [Google Scholar] [CrossRef]
- Lindsay, B.R. Social Media and Disasters: Current Uses, Future Options, and Policy Considerations; Congressional Research Service: Washington, DC, USA, 2011.
- Osborne, M.; Moran, S.; McCreadie, R.; Von Lunen, A.; Sykora, M.; Cano, E.; O’Brien, A. Real-Time Detection, Tracking, and Monitoring of Automatically Discovered Events in Social Media. In Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, Baltimore, MD, USA, 23–24 June 2014; pp. 37–42. [Google Scholar] [CrossRef]
- Zhao, S.; Gao, Y.; Ding, G.; Chua, T.S. Real-Time Multimedia Social Event Detection in Microblog. IEEE Trans. Cybern. 2017, 48, 3218–3231. [Google Scholar] [CrossRef] [PubMed]
- Gal-Tzur, A.; Grant-Muller, S.M.; Kuflik, T.; Minkov, E.; Nocera, S.; Shoor, I. The Potential of Social Media in Delivering Transport Policy Goals. Transp. Policy 2014, 32, 115–123. [Google Scholar] [CrossRef]
- Asghari, M.; Sierra-Sosa, D.; Elmaghraby, A.S. A Topic Modeling Framework for Spatio-Temporal Information Management. Inf. Process. Manag. 2020, 57, 102340. [Google Scholar] [CrossRef]
- Romero, C.; Ventura, S. Educational Data Mining: A Review of the State of the Art. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 2010, 40, 601–618. [Google Scholar] [CrossRef]
- Zeng, L.; Li, L.; Duan, L.; Lu, K.; Shi, Z.; Wang, M.; Wu, W.; Luo, P. Distributed Data Mining: A Survey. Inf. Technol. Manag. 2012, 13, 403–409. [Google Scholar] [CrossRef]
- He, W. Improving User Experience with Case-Based Reasoning Systems Using Text Mining and Web 2.0. Expert Syst. Appl. 2013, 40, 500–507. [Google Scholar] [CrossRef]
- Ukkusuri, S.V.; Zhan, X.; Sadri, A.M.; Ye, Q. Use of Social Media Data to Explore Crisis Informatics: Study of 2013 Oklahoma Tornado. Transp. Res. Rec. 2014, 2459, 110–118. [Google Scholar] [CrossRef]
- Vayansky, I.; Kumar, S.A.P. A Review of Topic Modeling Methods. Inf. Syst. 2020, 94, 101582. [Google Scholar] [CrossRef]
- Blei, D.M.; Ng, A.Y.; Jordan, M.I. Latent Dirichlet Allocation. J. Mach. Learn. Res. 2003, 3, 993–1022. [Google Scholar]
- Méndez, J.T.; Lobel, H.; Parra, D.; Herrera, J.C. Using Twitter to Infer User Satisfaction with Public Transport: The Case of Santiago, Chile. IEEE Access 2019, 7, 60255–60263. [Google Scholar] [CrossRef]
- Ali, F.; Kwak, D.; Khan, P.; Islam, S.R.; Kim, K.H.; Kwak, K.S. Fuzzy Ontology-Based Sentiment Analysis of Transportation and City Feature Reviews for Safe Traveling. Transp. Res. Part C Emerg. Technol. 2017, 77, 33–48. [Google Scholar] [CrossRef]
- Hu, M.; Liu, B. Mining and Summarizing Customer Reviews. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, USA, 22–25 August 2004; ACM: New York, NY, USA, 2004; pp. 168–177. [Google Scholar]
- Ma, E.; Cheng, M.; Hsiao, A. Sentiment Analysis—A Review and Agenda for Future Research in Hospitality Contexts. Int. J. Contemp. Hosp. Manag. 2018, 30, 3287–3308. [Google Scholar] [CrossRef]
- Philander, K.; Zhong, Y. Twitter Sentiment Analysis: Capturing Sentiment from Integrated Resort Tweets. Int. J. Hosp. Manag. 2016, 55, 16–24. [Google Scholar] [CrossRef]
- Kirilenko, A.P.; Stepchenkova, S.O.; Kim, H.; Li, X. Automated Sentiment Analysis in Tourism: Comparison of Approaches. J. Travel Res. 2018, 57, 1012–1025. [Google Scholar] [CrossRef]
- Paolanti, M.; Mancini, A.; Frontoni, E.; Felicetti, A.; Marinelli, L.; Marcheggiani, E.; Pierdicca, R. Tourism Destination Management Using Sentiment Analysis and Geo-Location Information: A Deep Learning Approach. Inf. Technol. Tour. 2021, 23, 241–264. [Google Scholar] [CrossRef]
- Tokarchuk, O.; Barr, J.C.; Cozzio, C. Estimating Destination Carrying Capacity: The Big Data Approach. In Proceedings of the 51st Travel and Tourism Research Association (TTRA) International Conference, Vitual, 14–16 June 2021. [Google Scholar]
- Norris, F.H.; Stevens, S.P.; Pfefferbaum, B.; Wyche, K.F.; Pfefferbaum, R.L. Community Resilience as a Metaphor, Theory, Set of Capacities, and Strategy for Disaster Readiness. Am. J. Community Psychol. 2008, 41, 127–150. [Google Scholar] [CrossRef] [PubMed]
- Irons, M.; Paton, D. Social Media and Emergent Groups: The Impact of High Functionality of Community Resilience. In Disaster Resilience: An Integrated Approach, 2nd ed.; Paton, D., Moore, D., Eds.; Charles C Thomas Publisher: Springfield, IL, USA, 2017. [Google Scholar]
- Obembe, D.; Kolade, O.; Obembe, F.; Owoseni, A.; Mafimisebi, O. COVID-19 and Tourism Industry: An Early Stage Sentiment Analysis of the Impact of Social Media and Stakeholder Communication. Int. J. Inf. Manag. Data Insights 2021, 1, 100040. [Google Scholar] [CrossRef]
- Schulz, A.; Thanh, T.D.; Paulheim, H.; Schweizer, I. A Fine-Grained Sentiment Analysis Approach for Detecting Crisis Related Microposts. In Proceedings of the 10th International ISCRAM Conference, Baden, Germany, 1 May 2013; pp. 846–851. [Google Scholar]
- Neppalli, V.K.; Caragea, C.; Squicciarini, A.; Tapia, A.; Stehle, S. Sentiment Analysis during Hurricane Sandy in Emergency Response. Int. J. Disaster Risk Reduct. 2017, 21, 213–222. [Google Scholar] [CrossRef]
- Sano, K.; Sano, H. The effect of different crisis communication channels. Ann. Tour. Res. 2019, 79, 102804. [Google Scholar] [CrossRef]
- Dahal, B.; Kumar, S.A.P.; Li, Z. Topic Modeling and Sentiment Analysis of Global Climate Change Tweets. Soc. Netw. Anal. Min. 2019, 9, 24. [Google Scholar] [CrossRef]
- Zheng, Y.; Su, L.; Luo, Q.; Liu-Lastres, B. Public Risk Perception of Cruise Travel on Social Media: A Collective Sensemaking Perspective. J. Hosp. Tour. Manag. 2023, 56, 473–481. [Google Scholar] [CrossRef]
- Laureate, C.D.P.; Buntine, W.; Linger, H. A systematic review of the use of topic models for short text social media analysis. Artif. Intell. Rev. 2023, 56, 14223–14225. [Google Scholar] [CrossRef]
- Birunda, S.S.; Devi, R.K. A Review on Word Embedding Techniques for Text Classification. In Innovative Data Communication Technologies and Application; Raj, J.S., Ed.; Lecture Notes on Data Engineering and Communications Technologies; Springer: Singapore, 2021; Volume 59, pp. 271–282. [Google Scholar] [CrossRef]
- Çılgın, C.; Baş, M.; Bilgehan, H.; Ünal, C. Twitter Sentiment Analysis during COVID-19 Outbreak with VADER. Am. J. Inf. Technol. Educ. 2022, 13, 49. [Google Scholar] [CrossRef]
- Sakurai, M.; Adu-Gyamfi, B. Disaster-Resilient Communication Ecosystem in an Inclusive Society—A Case of Foreigners in Japan. Int. J. Disaster Risk Reduct. 2020, 51, 101804. [Google Scholar] [CrossRef]
- Twitter. Twitter Advanced Search. Available online: https://help.x.com/en/using-x/x-advanced-search (accessed on 1 December 2022).
- Grootendorst, M. BERTopic: Leveraging BERT and c-TF-IDF to Create Easily Interpretable Topics. Zenodo 2020. [CrossRef]
- McInnes, L.; Healy, J.; Melville, J. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. ArXiv 2018, arXiv:1802.03426. [Google Scholar] [CrossRef]
- Campello, R.J.; Moulavi, D.; Sander, J. Density-Based Clustering Based on Hierarchical Density Estimates. In Pacific-Asia Conference on Knowledge Discovery and Data Mining; Springer: Berlin/Heidelberg, Germany, 2013; pp. 160–172. [Google Scholar]
- Pavlopoulos, I. Aspect Based Sentiment Analysis. Ph.D. Thesis, Athens University of Economics and Business, Athens, Greece, 2014. [Google Scholar]
- Japan Meteorological Agency. Damage Caused by Typhoons in Japan (In Japanese). Available online: https://www.jma.go.jp/jma/kishou/know/typhoon/6-1.html (accessed on 2 December 2022).
- Suzuki, T.; Tajima, Y.; Watanabe, M.; Tsuruta, N.; Takagi, H.; Takabatake, T. Post-Event Survey of Locally Concentrated Disaster due to 2019 Typhoon Faxai along the Western Shore of Tokyo Bay, Japan. Coast. Eng. J. 2020, 62, 146–158. [Google Scholar] [CrossRef]
- Murata, A.; Teraoka, A. Exodus Begins for over 10,000 Stranded at Narita Airport. 10 September 2019. Available online: https://asia.nikkei.com/Economy/Natural-disasters/Exodus-begins-for-over-10-000-stranded-at-Narita-airport (accessed on 2 December 2022).
- Deng, Q.; Gao, Y.; Wang, C.; Zhang, H. Detecting Information Requirements for Crisis Communication from Social Media Data: An Interactive Topic Modeling Approach. Int. J. Disaster Risk Reduct. 2020, 50, 101692. [Google Scholar] [CrossRef]
- Narita International Airport Corporation. Survey on Current State of Transportation Modes of Access to Narita Airport. Available online: https://www.naa.jp/jp/ (accessed on 2 December 2022).
- Hartman, J.; Huppertz, J.; Schamp, C.; Heitmann, M. Comparing Automated Text Classification Methods. Int. J. Res. Mark. 2019, 36, 20–38. [Google Scholar] [CrossRef]
- Hayati, S.A.; Kang, D.; Ungar, L. Does BERT Learn as Humans Perceive? Understanding Linguistic Styles through Lexica. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Virtual Event, 7–11 November 2021; pp. 6323–6331. [Google Scholar]
Account ID | Number of Tweets |
---|---|
@JapanSafeTravel | 103 |
@NHKWORLD_News | 47 |
@JR-WEST Travel Information | 46 |
@JRE_Super_Exp | 39 |
@JRC_Shinkan_en | 25 |
@Narita Airport Operation Information [Official] | 51 |
@Narita_OPC_info | 15 |
@JAL Flight Info | 32 |
@ANA Flight Info | 7 |
@haneda_airport | 8 |
Topic | Topic Name | Count | Keywords | |
---|---|---|---|---|
Public | 1 | Flight cancel (1) | 137 | flight; cancelled; get; hi; delayed; united; now |
2 | Hotel stay | 113 | hotel; room; night; rain; stay; here; day | |
3 | Train operation | 69 | suspended; train; tokyu; transportation; shinkansen; strike; yamanashi | |
4 | Flight cancel (2) | 69 | canceled; airports; flights; airlines; airways; Haneda; nrt | |
5 | Airport stranded | 68 | stranded; airport; narita; faxai; passengers; leaving; travelers | |
6 | Typhoon information | 51 | weekend; winds; rain; storm; Hagibis; expected; weather | |
7 | Bus and Taxi | 47 | Narita; airport; buses; taxi; chaos; Tokyo; passengers | |
Official | 1 | Flight cancel | 72 | flights; typhoon; Hagibis; cancelled; delayed; operations; tokyo |
2 | Train operation | 43 | suspended; train; shinkansen; operations; Monday; tokaido; september | |
3 | Emergency assistance | 28 | emergencies; assistance; tourist; hotline; call; accidents; disaster | |
4 | Typhoon information (1) | 28 | warning; rain; emergency; meteorological; yamanashi; saitama; miyagi | |
5 | Typhoon damage | 25 | Hagibis; typhoon; damage; east; causes; flooding; areas | |
6 | Airport stranded | 24 | airline; congestion; terminals; airport; narita; transporation; typhoon | |
7 | Typhoon information (2) | 24 | October; typhoon; current; notice; status; canceled; suspension |
Tweets | Compound | Negative | Positive | Neutral |
---|---|---|---|---|
I was supposed to go to Osaka this weekend, but my flight was cancelled due to the typhoon. Getting ahold of an English speaker at @JEJUair so I can either get refunded or rebook has been difficult. | −0.6956 | 0.141 | 0 | 0.859 |
For those who travel in Japan, we have a record-level typhoon approaching. Many air flights, trains, and shinkansen are scheduled to suspend the operation today across the nation. You should also stay alert | −0.0258 | 0.061 | 0.059 | 0.88 |
Flights canceled; preparations underway as super typhoon bears down on Japan | 0.5994 | 0 | 0.281 | 0.719 |
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Choi, S.; Liu, K.; Hanaoka, S. Perceived Responses of International Tourists to Transportation and Tourism Services During Typhoons Faxai and Hagibis in Japan. Sustainability 2024, 16, 9114. https://doi.org/10.3390/su16209114
Choi S, Liu K, Hanaoka S. Perceived Responses of International Tourists to Transportation and Tourism Services During Typhoons Faxai and Hagibis in Japan. Sustainability. 2024; 16(20):9114. https://doi.org/10.3390/su16209114
Chicago/Turabian StyleChoi, Sunkyung, Kexin Liu, and Shinya Hanaoka. 2024. "Perceived Responses of International Tourists to Transportation and Tourism Services During Typhoons Faxai and Hagibis in Japan" Sustainability 16, no. 20: 9114. https://doi.org/10.3390/su16209114
APA StyleChoi, S., Liu, K., & Hanaoka, S. (2024). Perceived Responses of International Tourists to Transportation and Tourism Services During Typhoons Faxai and Hagibis in Japan. Sustainability, 16(20), 9114. https://doi.org/10.3390/su16209114