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On-line Access: 2023-11-13

Received: 2023-02-01

Revision Accepted: 2023-03-19

Crosschecked: 2023-11-14

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Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Yilin SUN

https://orcid.org/0000-0002-8757-7261

Yinan DONG

https://orcid.org/0000-0002-6275-6175

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Journal of Zhejiang University SCIENCE A 2023 Vol.24 No.11 P.1003-1016

http://doi.org/10.1631/jzus.A2300057


Correlation between travel experiences and post-COVID outbound tourism intention: a case study from China


Author(s):  Yilin SUN, Yinan DONG, Dianhai WANG, E. Owen D. WAYGOOD, Hamed NASERI, Kazuo NISHII

Affiliation(s):  College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China; more

Corresponding email(s):   dannydong@zju.edu.cn

Key Words:  Outbound tourism, Touring behavior, Travel behavior, COVID-19, Ensemble learner


Yilin SUN, Yinan DONG, Dianhai WANG, E. Owen D. WAYGOOD, Hamed NASERI, Kazuo NISHII. Correlation between travel experiences and post-COVID outbound tourism intention: a case study from China[J]. Journal of Zhejiang University Science A, 2023, 24(11): 1003-1016.

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year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2300057"
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Abstract: 
The COVID-19 pandemic has devastated global tourism and recovery is proceeding very slowly. For many countries, tourism served as a major economic sector, so investigating how to recover is essential. As China was the largest source of outbound travelers before the outbreak, study of the factors influencing Chinese intentions to travel overseas in the post-COVID era is revealing. In Apr. 2022, among seven provinces (or cities) with the most outbound tourists from 2019 to 2021, 2450 individuals responded to a questionnaire on daily mobility, tourism experiences, and the shifts due to the pandemic. Light gradient boosting machine (LightGBM), a robust ensemble learning method, was adopted to quantify and visualize the impact of explanatory factors on outbound travel intention. In addition, the Optuna mechanism and Shapley additive explanation (SHAP) instruments were employed for tuning hyperparameters and interpreting results, respectively. Findings suggest neither one-day nor multi-day tours have resumed to pre-COVID levels. Higher frequency of multi-day tours with further destinations, less car utilization in daily shopping trips, and moderate pandemic restrictions can boost the intention to travel abroad. The concerns and desires of different age groups for overseas travel need different responses. This study reveals the factors affecting Chinese outbound travel intentions and provides suggestions for the recovery of tourism in the post-COVID period.

出行经历与后疫情时代出境旅游意向之间的相关性--来自中国的案例研究

作者:孙轶琳1,2,3,4,董轶男1,5,王殿海1,2,E. Owen D. WAYGOOD6,Hamed NASERI6,Kazuo NISHII7
机构:1浙江大学,建筑工程学院,中国杭州,310058;2浙江大学建筑设计研究院,中国杭州,310028;3阿里巴巴-浙江大学前沿技术联合研究中心,中国杭州,310058;4浙江大学,工程师学院,中国杭州,310015;5浙江大学平衡建筑研究中心,中国杭州,310028;6蒙特利尔工程学院,土木、岩土和采矿工程系,加拿大蒙特利尔,H3T 1J4;7流通科学大学,政策研究系,日本神户,651-2188
目的:全球旅游业在新冠肺炎疫情期间遭受了严重冲击,并与经济发展密切关联,因此,探求如何推进其相关产业的复兴十分重要。由于中国是疫情前最大的出境游客来源国,对中国人在后疫情时代的出境旅游意向及其影响因素进行研究,有利于促进国际旅游市场的复苏。
创新点:1.构建LightGBM模型,参数化各类变量及组合与后疫情时期出境旅游意向之间的关系;2.通过SHAP算法,可视化各类因素对后疫情时期出境旅游意向的影响。
方法:1.通过网络调查收集2450份有效问卷,并统计疫情期间受访者单日、多日出游次数以及出行方式等信息(图2);2.通过模型构建及参数调优,以后疫情时期的出境游意向为因变量,基于所收集数据训练LightGBM模型并评估其效能(图3);3.运用SHAP算法,对各个自变量的影响进行排序及部分依赖图分析(图4和5)。
结论:1.疫情期间的日常出行及旅游经历对后疫情时期的出境旅游意向的影响程度最大;2.多日游频率的上升和日常购物出行中小汽车利用率的增加,显著促进出境旅游意向的增强;3.后疫情时期,不同年龄段的人群在出境旅游意向及出行方式选择上的差异愈加明显。

关键词:出境旅游;旅游行为;出行行为;新冠肺炎;集成学习

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]AgrawalT, 2021. Optuna and autoML. In: Agrawal T (Ed.), Hyperparameter Optimization in Machine Learning. Springer, Berkeley, USA, p.109-129.

[2]AkibaT, SanoS, YanaseT, et al., 2019. Optuna: a next-generation hyperparameter optimization framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining.

[3]Al DaoudE, 2019. Comparison between XGBoost, lightGBM and CatBoost using a home credit dataset. International Journal of Computer and Information Engineering, 13(1):6-10.

[4]ArbulúI, RazumovaM, Rey-MaquieiraJ, et al., 2021. Can domestic tourism relieve the COVID-19 tourist industry crisis? The case of Spain. Journal of Destination Marketing & Management, 20:100568.

[5]BayihBE, SinghA, 2020. Modeling domestic tourism: motivations, satisfaction and tourist behavioral intentions. Heliyon, 6(9):E04839.

[6]BentéjacC, CsörgőA, Martínez-MuñozG, 2021. A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54(3):1937-1967.

[7]BuehlerR, PucherJ, 2021. COVID-19 impacts on cycling, 2019‒2020. Transport Reviews, 41(4):393-400.

[8]ButlerRW, 2020. Tourism carrying capacity research: a perspective article. Tourism Review, 75(1):207-211.

[9]CaroppoE, MazzaM, SannellaA, et al., 2021. Will nothing be the same again?: Changes in lifestyle during COVID-19 pandemic and consequences on mental health. International Journal of Environmental Research and Public Health, 18(16):8433.

[10]CastanhoRA, CoutoG, PimentelP, et al., 2020. The impact of SARS-CoV-2 outbreak on the accommodation selection of Azorean tourists. A study based on the assessment of the Azores population’s attitudes. Sustainability, 12(23):9990.

[11]CastanhoRA, CoutoG, SousaÁ, et al., 2021. Assessing the impacts of the COVID-19 pandemic over the Azores region’s touristic companies. Sustainability, 13(17):9647.

[12]ChanLY, LauWL, ZouSC, et al., 2002. Exposure level of carbon monoxide and respirable suspended particulate in public transportation modes while commuting in urban area of Guangzhou, China. Atmospheric Environment, 36(38):5831-5840.

[13]CorbisieroF, MonacoS, 2021. Post-pandemic tourism resilience: changes in Italians’ travel behavior and the possible responses of tourist cities. Worldwide Hospitality and Tourism Themes, 13(3):401-417.

[14](China Tourism Academy)CTA, 2020. China Outbound Tourism Development Report 2020 (in Chinese). http://www.ctaweb.org.cn/cta/ztyj/202103/87a492a44eda4038b7fe8f6428ed3d5d.shtml

[15](China Tourism Academy)CTA, 2021a. China Domestic Tourism Development Report 2021 (in Chinese). http://www.ctaweb.org.cn/cta/gzdt/202110/357550c0eb0444d4b59534d51b590373.shtml

[16](China Tourism Academy)CTA, 2021b. China Outbound Tourism Development Report 2021 (in Chinese). http://www.ctaweb.org.cn/cta/gzdt/202111/074b098d53e24375bfebf5352f67512a.shtml

[17]de SausmarezN, 2007. The potential for tourism in post-crisis recovery: lessons from Malaysia’s experience of the Asian financial crisis. Asia Pacific Business Review, 13(2):‍277-299.

[18]DHS (United States Department of Homeland Security), 2020. Notices of Arrival Restrictions Due to Coronavirus. https://www.dhs.gov/publication/notices-arrival-restrictions-coronavirus

[19]DonaireJA, GalíN, CamprubiR, 2021. Empty summer: international tourist behavior in Spain during COVID-19. Sustainability, 13(8):4356.

[20]DongHM, MaSF, JiaN, et al., 2021. Understanding public transport satisfaction in post COVID-19 pandemic. Transport Policy, 101:81-88.

[21]DongYN, SunYL, WaygoodEOD, et al., 2022. Insight into the nonlinear effect of COVID-19 on well-being in China: commuting, a vital ingredient. Journal of Transport & Health, 27:101526.

[22]DuroJA, Perez-LabordaA, Turrion-PratsJ, et al., 2021. COVID-19 and tourism vulnerability. Tourism Management Perspectives, 38:100819.

[23]EisenmannC, NobisC, KolarovaV, et al., 2021. Transport mode use during the COVID-19 lockdown period in Germany: the car became more important, public transport lost ground. Transport Policy, 103:60-67.

[24]GillBS, JayarajVJ, SinghS, et al., 2020. Modelling the effectiveness of epidemic control measures in preventing the transmission of COVID-19 in Malaysia. International Journal of Environmental Research and Public Health, 17(15):5509.

[25]GkiotsalitisK, CatsO, 2021. Public transport planning adaption under the COVID-19 pandemic crisis: literature review of research needs and directions. Transport Reviews, 41(3):374-392.

[26]GrépinKA, HoTL, LiuZH, et al., 2021. Evidence of the effectiveness of travel-related measures during the early phase of the COVID-19 pandemic: a rapid systematic review. BMJ Global Health, 6(3):e004537.

[27]HensherDA, WeiE, BeckM, et al., 2021. The impact of COVID-19 on cost outlays for car and public transport commuting-the case of the Greater Sydney Metropolitan Area after three months of restrictions. Transport Policy, 101:71-80.

[28]Higgins-DesbiollesF, 2021. The “war over tourism”: challenges to sustainable tourism in the tourism academy after COVID-19. Journal of Sustainable Tourism, 29(4):551-569.

[29]HocineA, KouaissahN, LozzaSO, 2023. XOR-analytic network process and assessing the impact of COVID-19 by sector. Computers & Industrial Engineering, 177:109017.

[30]HungK, RenLP, QiuHQ, 2021. Luxury shopping abroad: what do Chinese tourists look for? Tourism Management, 82:104182.

[31]IATA (International Air Transport Association), 2023.Passenger Demand Recovery Continued in December 2022 & for the Full Year. https://www.iata.org/en/pressroom/2023-releases/2023-02-06-02/

[32](International Monetary Fund)IMF, 2020. World Economic Outlook Update, June 2020. A Crisis Like No Other, an Uncertain Recovery. IMF.

[33]KeGL, MengQ, FinleyT, et al., 2017. LightGBM: a highly efficient gradient boosting decision tree. Proceedings of the 31st International Conference on Neural Information Processing Systems, p.3149-3157.

[34]KhaddarS, FatmiMR, 2021. COVID-19: are you satisfied with traveling during the pandemic? Transportation Research Interdisciplinary Perspectives, 9:100292.

[35]KimM, SohnJ, 2022. Passenger, airline, and policy responses to the COVID-19 crisis: the case of South Korea. Journal of Air Transport Management, 98:102144.

[36]LuJ, LinAR, JiangCM, et al., 2021. Influence of transportation network on transmission heterogeneity of COVID-19 in China. Transportation Research Part C: Emerging Technologies, 129:103231.

[37]LundbergSM, LeeSI, 2017. A unified approach to interpreting model predictions. Proceedings of the 31st International Conference on Neural Information Processing Systems, p.4768-4777.

[38]MiaoL, ImJ, FuXX, et al., 2021. Proximal and distal post-COVID travel behavior. Annals of Tourism Research, 88:103159.

[39]MolloyJ, SchatzmannT, SchoemanB, et al., 2021. Observed impacts of the COVID-19 first wave on travel behaviour in Switzerland based on a large GPS panel. Transport Policy, 104:43-51.

[40]MorenoT, PacittoA, FernándezA, et al., 2019. Vehicle interior air quality conditions when travelling by taxi. Environmental Research, 172:529-542.

[41]NBSC (National Bureau of Statistics of China), 2020. China’s Seventh Population Census Data (in Chinese). http://www.stats.gov.cn/sj/pcsj/rkpc/7rp/indexch.htm

[42]NBSC (National Bureau of Statistics of China), 2023. Statistical Bulletin on National Economic and Social Development of the People’s Republic of China 2022 (in Chinese). http://www.‍stats.‍gov.‍cn/xxgk/sjfb/zxfb2020/202302/t20230228_1919001.html

[43]ParadyG, TaniguchiA, TakamiK, 2020. Travel behavior changes during the COVID-19 pandemic in Japan: analyzing the effects of risk perception and social influence on going-out self-restriction. Transportation Research Interdisciplinary Perspectives, 7:100181.

[44]PaulT, ChakrabortyR, AnwariN, 2022. Impact of COVID-19 on daily travel behaviour: a literature review. Transportation Safety and Environment, 4(2):tdac013.

[45]PedregosaF, VaroquauxG, GramfortA, et al., 2011. Scikit-learn: machine learning in Python. The Journal of Machine Learning Research, 12:2825-2830.

[46]PelusoAM, PichierriM, 2021. Vacation preferences in the COVID-19 era: an investigation of age-related effects. Current Issues in Tourism, 24(19):2710-2715.

[47]Persson-FischerU, LiuSQ, 2021. The impact of a global crisis on areas and topics of tourism research. Sustainability, 13(2):906.

[48]PolitisI, GeorgiadisG, PapadopoulosE, et al., 2021. COVID-19 lockdown measures and travel behavior: the case of Thessaloniki, Greece. Transportation Research Interdisciplinary Perspectives, 10:100345.

[49]PolyzosS, SamitasA, SpyridouAE, 2021. Tourism demand and the COVID-19 pandemic: an LSTM approach. Tourism Recreation Research, 46(2):175-187.

[50]PrayagG, 2020. Time for reset? COVID-19 and tourism resilience. Tourism Review International, 24(2-3):179-184.

[51]RaveendranAV, JayadevanR, SashidharanS, 2021. Long COVID: an overview. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 15(3):869-875.

[52]RawatD, DixitV, GulatiS, et al., 2021. Impact of COVID-19 outbreak on lifestyle behaviour: a review of studies published in India. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 15(1):331-336.

[53]RogersonCM, BaumT, 2020. COVID-19 and African tourism research agendas. Development Southern Africa, 37(5):727-741.

[54]RogersonCM, RogersonJM, 2021. COVID-19 and changing tourism demand: research review and policy implications for South Africa. African Journal of Hospitality, Tourism and Leisure, 10(1):1-21.

[55]SharmaD, ZhongC, WongH, 2023. Lockdown lifted: measuring spatial resilience from London’s public transport demand recovery. Geo-spatial Information Science, in press.

[56]ShibayamaT, SandholzerF, LaaB, et al., 2021. Impact of COVID-19 lockdown on commuting: a multi-country perspective. European Journal of Transport and Infrastructure Research, 21(1):70-93.

[57]ShortallR, MouterN, van WeeB, 2022. COVID-19 passenger transport measures and their impacts. Transport Reviews, 42(4):441-466.

[58]SönmezSF, GraefeAR, 1998. Determining future travel behavior from past travel experience and perceptions of risk and safety. Journal of Travel Research, 37(2):171-177.

[59]SousaÁ, CastanhoRA, CoutoG, et al., 2022. Post-COVID tourism planning: based on the Azores residents’ perceptions about the development of regional tourism. European Planning Studies, in press.

[60]SunXL, LiuMX, SimaZQ, 2020. A novel cryptocurrency price trend forecasting model based on LightGBM. Finance Research Letters, 32:101084.

[61]SunXQ, WandeltS, ZhangAM, 2021. On the degree of synchronization between air transport connectivity and COVID-19 cases at worldwide level. Transport Policy, 105:115-123.

[62]SunYL, DongYN, WaygoodEOD, et al., 2023. Machine-learning approaches to identify travel modes using smartphone-assisted survey and map application programming interface. Transportation Research Record: Journal of the Transportation Research Board, 2677(2):‍‍385-400.

[63]TanrıverdiG, BakırM, MerkertR, 2020. What can we learn from the JATM literature for the future of aviation post COVID-19?–‍A bibliometric and visualization analysis. Journal of Air Transport Management, 89:101916.

[64]UNICEF (United Nations International Children’s Emergency Fund), 2021. How COVID-19 is Changing the World: a Statistical Perspective-Volume III. https://data.unicef.org/resources/how-covid-19-is-changing-the-world-a-statistical-perspective-volume-iii/

[65]UNWTO (United Nations World Tourism Organization), 2021. Tourism Grows 4% in 2021 but Remains Far Below Pre-pandemic Levels. https://www.unwto.org/news/tourism-grows-4-in-2021-but-remains-far-below-pre-pandemic-levels

[66]VolggerM, TaplinR, AebliA, 2021. Recovery of domestic tourism during the COVID-19 pandemic: an experimental comparison of interventions. Journal of Hospitality and Tourism Management, 48:428-440.

[67]WangBJ, WangYL, QinK, et al., 2018. Detecting transportation modes based on LightGBM classifier from GPS trajectory data. The 26th International Conference on Geoinformatics.

[68]WenJ, KozakM, YangSH, et al., 2021. COVID-19: potential effects on Chinese citizens’ lifestyle and travel. Tourism Review, 76(1):74-87.

[69]WHO (World Health Organization), 2020. Management of Ill Travellers at Points of Entry‍–‍International Airports, Ports and Ground Crossings‍–‍in the Context of COVID-19 Outbreak: Interim Guidance, 16 February 2020. WHO, Geneva, Switzerland.

[70]WuMY, WallG, 2016. Chinese research on family tourism: review and research implications. Journal of China Tourism Research, 12(3-4):274-290.

[71]YeQ, WangBL, MaoJH, et al., 2020. Epidemiological analysis of COVID-19 and practical experience from China. Journal of Medical Virology, 92(7):755-769.

[72]ZhangHY, SongHY, WenL, et al., 2021. Forecasting tourism recovery amid COVID-19. Annals of Tourism Research, 87:103149.

[73]ZhangJY, HayashiY, FrankLD, 2021. COVID-19 and transport: findings from a world-wide expert survey. Transport Policy, 103:68-85.

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