Travel-related Factors Research Articles

The Resilience of Travel Demand: The Case of Europeans’ Travel Sentiment During the Covid-19 Pandemic

The COVID-19 pandemic had a fundamental effect on travel and tourism in general, and travel sentiment in particular. Travel sentiment is an emotional expression that evaluates customer willingness to travel and is shaped by numerous factors. The current study explores the effects of COVID-19, vaccine rollout, and travel-related factors on the travel sentiment of Europeans from September 2020 to September 2021. Employing a quantitative approach, the study analyzed 54,000 questionnaires collected in 10 European countries, measuring Europeans??? travel sentiment. In accordance with the study’s main assertion, it has been found that travel sentiment is highly adaptive and corresponds with both the disabling factor of COVID-19 cases and the enabling factor of vaccine rollout, demonstrating the adaptive and resilient nature of travel sentiment. In this view, travel sentiment demonstrates a high level of adaptive capacity, as can be found in complex systems with high levels of resilience. The study contributes to the emerging discussion on tourism resilience and the resilience of travel demand, placing travel sentiment in the context of tourism resilience theory.

Barriers to longitudinal follow-up for hepatitis B treatment in rural Sierra Leone: A mixed methods study of retention in care.

HBV disproportionately affects resource-limited settings, and retaining patients in longitudinal care remains challenging. We conducted a mixed methods investigation to understand the causes of losses to follow-up within an HBV clinic in rural Sierra Leone. We developed a multivariable logistic regression model of baseline clinical and sociodemographic factors predicting losses to follow-up, defined as failing to present for a follow-up visit within 14 months of enrollment. We included patients enrolled between April 30, 2019 and March 1, 2020, permitting 14 months of follow-up by April 30, 2021. We then developed a survey to solicit patient perspectives on the challenges surrounding retention. We interviewed randomly selected patients absent from HBV care for at least 6 months. Among 271 patients enrolled in the Kono HBV clinic, 176 (64.9%) did not have a follow-up visit within 14 months of the study end point. Incomplete baseline workup (aOR 2.9; 95% CI: 1.6-4.8), lack of treatment at baseline (aOR 5.0; 95% CI: 1.7-14.4), and having cirrhosis at baseline (aOR 3.3; 95% CI: 0.99-10.8) were independently associated with being lost to follow-up. For the patient survey, 21 patients completed the interview (median age 34 years [IQR: 25-38]). Travel-related factors were the most frequently reported barrier to retention (57%). Almost 30% suggested improved customer care might support retention in care; 24% requested to be given medication. In our setting, factors that might reduce losses to follow-up included expanded criteria for treatment initiation, overcoming transportation barriers, reducing wait times, ensuring against stockouts, and scaling up point-of-care testing services.

How individual perceptions of transportation systems influence mode choice for mobility-challenged people: A case study in Dhaka using an integrated choice and latent variable model

This study examines how mobility-challenged persons (MCPs) navigate the limitations of transportation systems, using an integrated choice and latent variable (ICLV) model. Building on the experience of MCPs in a South Asian megacity context, that of Dhaka, Bangladesh, the empirical strategy accounts for perceptions of mode-specific challenges, which arguably vary by mobility impairment. These perceptions are posited as latent variables. Drawing on survey data collected from 400 MCPs living in Dhaka, the latent variables were constructed via a factor analysis of 18 statements about the experienced severity (ranked on a scale from 1 to 5) of mode-specific challenges. Holding socio-demographic and travel-related factors constant, we find that perceptions of mode-specific challenges significantly influence mode choices – while the degree of impairment alone, and related mobility aid needed, do not. Perceived limitations of the walking infrastructure shift MCPs' travel demand towards the bus, whereas bus fare-related issues encourage the use of non-motorized and powered three-wheelers. We recommend that mode choice models include latent variables related to MCPs’ perceptions of various modes to more accurately inform universal access policies.

The Impact of Socioeconomic and Travel-Related Aspects on the Allocation of Expenditures by Tourists Traveling to Taiwan

This paper analyzes the spending patterns of foreign tourists visiting Taiwan in 2019. Then, the study examines the impact of socioeconomic and travel-related factors on tourists' allocation of expenditures for five essential trip products. By comparing the behaviors of tourists from different nations, the study investigates their priorities for travel expenses such as food, accommodation, transportation, and shopping. The study's OLS regression results show that distance, visit times, travel modes, duration of stays, number of visitors, gender, income, age, and education significantly affect tourists' travel expenditures in Taiwan. The findings reveal that longer-haul tourists spend more on travel products due to their experience. In addition, tourists traveling with companions spend more than those traveling alone, and males spend more on accommodation while females prefer shopping. The study also highlights the positive impact of the number of nights spent on the purchase level of travel goods and services. These insights provide valuable information on the travel-related variables that influence international tourists' spending behavior in Taiwan.

Identify travel and health factors influencing well-being of the older adults-a case study in China.

With the increase in aging populations worldwide, the travel well-being of the elders has gained attention. The objective of this study is to examine the nonlinear relationships between the well-being of the older people in China and factors associated with travel and health. Based on the data collected in China, combined embedded feature selection and decision tree built by Gini index were utilized to screen for influential factors and to determine the importance of the features selected. Tamhane's T2 was used to study the differences in the important factors among older people with different levels of travel well-being. This study found that the travel well-being of older adults depends mainly on accessibility to public places, such as schools and medical facilities, and the availability of bus services. Out of expectation, the most important influential factor of travel well-being of older people is the distance from home to high school. This is related to the traditional Chinese concept of education. In addition, it was found that the body mass index is more important than self-perceived health as an influence factor of travel well-being of the elders in China. Social skills are important factors too. This study investigated various health-related and travel-related factors and their impacts on the travel well-being of older adults Chinese with the overall goal to improve the quality of life of the elders in China. The findings may provide a theoretical basis for the implementation of various transportation management and urban planning and design -related policies to improve the travel well-being of older adults in China.

An exploration of the preferences and mode choice behavior between autonomous demand-responsive transit and traditional buses

With the advancement in autonomous driving techniques, autonomous demand-responsive transit (ADRT) is a newly emerging sustainable transport mode for the future, which will provide more flexible services to public users. ADRT offers benefits such as flexible stops and routes and comfortable seats, but it also involves risks due to the vehicles being driverless. This paper particularly investigates users’ preferences and attitudes towards ADRT, and mode choice behavior between ADRT buses and traditional buses. A survey with Likert scale statements and stated preference (SP) choice scenarios is designed and conducted to explore users’ attitudes towards the safety risks of autonomous vehicles (AVs), social concerns, service flexibility concerns when using AVs, interest in new things, and shuttle mode choices. An integrated choice and latent variable (ICLV) model is adopted to explore users’ psychological factors through latent variables and to integrate them into mode choice behavioral modeling. Estimated results indicate that users’ attitudes towards AV safety risks, their social concerns, and their flexibility concerns with ADRT strongly influence their mode choices and are strongly related to sociodemographic and travel-related factors such as age, gender, income, education, number of family members. In general, a young age, a high education level, a higher income, private car ownership, and better knowledge of AVs are positively related to attitudes towards ADRT. Females, users from large families, and users with driving licenses or long commuting times are less willing to adopt ADRT. The study's outcomes highlight significant heterogeneities among users and can be highly valuable for policymakers, such as government authorities, in providing social support and designing policies targeting specific population groups. This will be beneficial in attracting more users to this emerging mobility service and contributing to sustainable urban development.

Effects of mode of transportation on PrEP persistence among urban men who have sex with men

ABSTRACT Little is known about the effect of travel-related factors, such as mode of transportation, on retention in PrEP care, or PrEP persistence. We used data from the 2020 American Men’s Internet Survey and conducted multilevel logistic regression to estimate the association between mode of transportation used for healthcare access and PrEP persistence among urban gay, bisexual, and other men who have sex with men (MSM) in the U.S. MSM using public transportation were less likely to report PrEP persistence (aOR: 0.51; 95% CI: 0.28–0.95) than MSM using private transportation. There were no significant associations between PrEP persistence and using active transportation (aOR: 0.67; 95% CI: 0.35–1.29) or multimodal transportation (aOR: 0.85; 95% CI: 0.51–1.43) compared to using private transportation. Transportation-related interventions and policies are needed to address structural barriers to accessing PrEP services and to improve PrEP persistence in urban areas.

Study of travellers' preferences towards travel offer categories and incentives in the journey planning context.

Nowadays, efforts to encourage changes in travel behaviour towards eco-friendly and active modes of transport are intensifying. A promising solution is to increase the use of sustainable public transport modes. Currently, a significant challenge related to this solution is the implementation of journey planners that will inform travellers about available travel solutions and facilitate decision-making by using personalisation techniques. This paper provides some valuable hints to journey planner developers on how to define and prioritise the travel offer categories and incentives to meet the travellers' expectations. The analysed data were obtained from a survey conducted in several European countries as part of the H2020 RIDE2RAIL project. The results confirm that travellers prefer to minimise travel time and stay on time. Also, incentives such as price discounts or class upgrades may play a crucial role in influencing the choices among travel solutions. By applying the regression analysis, it was found that preferences of travel offer categories and incentives are correlated with some demographic or travel-related factors. The results also show that subsets of significant factors strongly differ for particular travel offer categories and incentives, what underlines the importance of personalised recommendations in journey planners.

Contextualized Recommendation of Aviation Ancillary Services Based on Passenger Portraits

Ancillary services are crucial to Chinese domestic airlines’ profits. Personalized recommendations for ancillary aviation services must consider massive amounts of contextualized online and offline data. The issue of sparsity is more serious in massive data, which makes traditional collaborative filtering (CF) algorithms less accurate. This article adds instant contextualized travel-related factors to the modeling of air passengers’ ancillary services, thus achieving dynamic recommendations. It also proposes a four-tuple to construct a contextual ontology as a conceptual model of ancillary aviation services. The article establishes a context-aware air passenger portrait model based on four tag sets, including passenger attribute tags, ancillary service attribute tags, passenger interaction behavior tags, and contextual attribute tags. Further, it proposes a multidimensional method for calculating contextual portrait similarity; the method recommends a list of the top- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> items to the target passenger according to the similarity calculation. Compared with the traditional CF recommendation method, the proposed algorithm has better performance, improving the accuracy rate by 4.3% and the recall rate by 2.62%. The final experimental result demonstrates that the contextual recommendation method achieves higher passenger satisfaction, which can provide consolidated data and decision support for the management of aviation ancillary services.

The Effects of Three-Factor Exhibition Attributes on Tourist-Exhibition Organizer Value Co-Creation and Tourists’ Satisfaction: A Case Study of China

Exhibitions attract an increasing number of visitors, which, in turn, greatly influence the destination’s economy. Past studies paid little attention to exploring both internal and external attributes of the exhibition. The purpose of this study is to identify the multi-level attributes that motivate visitors to attend the exhibition, and explore their effects on value co-creation, satisfaction and re-exhibit intentions. A total of 395 valid questionnaires from the 26th Guangdong-Hong Kong-Macau Greater Bay Area International Auto show at Shenzhen Convention and Exhibition Center were collected. By using Partial least squares structural equation modeling (PLS-SEM), the results revealed that three dimensions of exhibition attributes (exhibition, travel and destination) are all significantly related to satisfaction and value co-creation, while travel-related factors have the greatest impact on satisfaction and value-co-creation. Furthermore, satisfaction and value co-creation all positively influence re-exhibit intention. This study helps exhibition organizers, tourism destination marketers and government agencies to establish strategies by considering travel purposes as a priority to attract potential tourists. It also helps future scholars to deeply understand tourists’ co-creation experience with an exhibition.

Spatial–Temporal Convolutional Model for Urban Crowd Density Prediction Based on Mobile-Phone Signaling Data

Urban crowd density prediction is essential for transport demand management and public safety monitoring. Existing studies for crowd density prediction only focus on a few transport modes which do not cover all urban crowds. Moreover, most existing models rely on grid-based divisions, which cannot accommodate unevenly distributed populations. This study proposes a novel model for predicting crowd density in urban areas with mobile-phone signaling data (MPSD). MPSD contain diversified travel and activity information that is not limited to specific transport modes and are practical for activity-travel research. We introduce methods for preprocessing MPSD and extracting information on crowd density and activity choice behaviors (such as activity location, activity type, and activity duration). A novel spatial-temporal convolutional model is proposed for urban crowd density prediction. We design an attention-based feature fusion block, enabling the model to comprehensively consider historical crowd density, activity choices, and external factors (such as weather conditions and holidays). With tailored spatial-temporal convolution blocks, our proposed model successfully captures spatial-temporal information related to crowd movement in different scenes. Unlike existing grid-based models, the proposed model can predict crowd density in irregular-shaped regions. Experimental results show that our model outperforms baseline models that do not apply activity information when selecting different prediction offsets, especially for long-term predictions. The correlation between prediction error and the regularity of crowd activities is also analyzed. The subsequent parameter analysis shows that the influence of external factors on urban crowd movement varies according to activity choices and other travel-related factors like commuting distance.

Interaction Between Information and Communication Technologies and Travel Behavior: Using Behavioral Data to Explore Correlates of the COVID-19 Pandemic

The COVID-19 pandemic has highlighted the importance of information and communication technologies (ICTs) in providing virtual engagement. Planners and engineers must determine whether cities will see reductions in travel demand, given the increasing use of ICTs. Notably, ICTs facilitate online shopping and working from home (WFH). Generally, online shopping may lead to fewer shopping trips; similarly, WFH may reduce work-related trips. However, more WFH has the potential to generate other non-work trips, including shopping trips. To find answers and explore interdependencies, this study integrates pre-pandemic behavioral data with during-pandemic travel data. In our framework, WFH and online shopping are considered together. By harnessing the pre-pandemic 2017 National Household Travel Survey data, this study jointly analyzes the relationships between shopping trips, online shopping, and WFH with a conditional mixed process model that can address unobserved endogeneity and selection bias. The results suggest that, before the pandemic, online shopping was associated with fewer in-person shopping trips while WFH was associated with more shopping trips. The role of socio-demographic, locational, and travel-related factors is also explored. The during-pandemic data and analysis capture how COVID-19 affected travel behavior. Results show that the relationships among the key variables found in the pre-pandemic data are similar but differ in magnitude from during the pandemic. WFH increased from 12% to 61% during COVID-19, admittedly an unusual situation. In the next “new normal,” planners may improve travel demand models by treating WFH explicitly as an alternative to traveling to work in the trip generation and time of day models.

Associations of circadian change, travel distance, and their interaction with basketball performance: a retrospective analysis of 2014–2018 National Basketball Association data

ABSTRACT This investigation aimed to clarify the influence of circadian change and travel distance on National Basketball Association (NBA) team performance using a dataset from the 2014–2018 seasons. Data from 9,840 games were acquired from an open-access source. Game point differential and team free-throw percentage served as outcome variables. Time zone change (TZΔ) captured raw circadian delay/advance based on travel for a game and adjusted TZΔ (AdjTZΔ) evolved TZΔ by allowing acclimation to a novel TZ. We also further categorized AdjTZΔ into AdjTZΔ_A, which assumed travel the day before each game and AdjTZΔ_B, which assumed teams spent as many days in their home city as possible. Travel distance for each game was calculated. Linear mixed-effects modeling estimated associations, with games nested within team and year. Adjusted associations accounted for differences in team ability, whether the game was home or away, and whether the game occurred on the second half of a back-to-back game sequence. Greater circadian misalignment, regardless of delay or advance, and increasing travel distance negatively influenced NBA game performance. Yet, results suggest that performance outcomes may be more influenced by travel distance than circadian misalignment. Moreover, circadian misalignment and travel distance interacted to significantly influence game point differential. Furthermore, differences in results across analyses were observed between AdjTZΔ_A and AdjTZΔ_B, which suggests that subtle differences in constructed travel schedules can have notable impact on NBA performance outcomes. Lastly, playing on the second half of a back-to-back sequence emerged as a robust predictor of performance disadvantage, which corroborates the existing literature and provides further support for NBA schedule changes purposed to enhance competitive equity by reducing the number of back-to-back games across a season. These findings can help guide NBA teams on key strategies for reducing travel-related disadvantages and inform schedule makers on critical factors to prioritize across future schedules to attenuate competitive inequity from travel. Furthermore, they can help direct teams towards scenarios that are best to target for load management purposes due to the cumulative disadvantage arising from travel-related factors, opponent quality, game location, and game sequence.

Drivers and forecasts of multiple waves of the coronavirus disease 2019 pandemic: A systematic analysis based on an interpretable machine learning framework.

Coronavirus disease 2019 (COVID-19) has become a global pandemic and continues to prevail with multiple rebound waves in many countries. The driving factors for the spread of COVID-19 and their quantitative contributions, especially to rebound waves, are not well studied. Multidimensional time-series data, including policy, travel, medical, socioeconomic, environmental, mutant and vaccine-related data, were collected from 39 countries up to 30 June 2021, and an interpretable machine learning framework (XGBoost model with Shapley Additive explanation interpretation) was used to systematically analyze the effect of multiple factors on the spread of COVID-19, using the daily effective reproduction number as an indicator. Based on a model of the pre-vaccine era, policy-related factors were shown to be the main drivers of the spread of COVID-19, with a contribution of 60.81%. In the post-vaccine era, the contribution of policy-related factors decreased to 28.34%, accompanied by an increase in the contribution of travel-related factors, such as domestic flights, and contributions emerged for mutant-related (16.49%) and vaccine-related (7.06%) factors. For single-peak countries, the dominant ones were policy-related factors during both the rising and fading stages, with overall contributions of 33.7% and 37.7%, respectively. For double-peak countries, factors from the rebound stage contributed 45.8% and policy-related factors showed the greatest contribution in both the rebound (32.6%) and fading (25.0%) stages. For multiple-peak countries, the Delta variant, domestic flights (current month) and the daily vaccination population are the three greatest contributors (8.12%, 7.59% and 7.26%, respectively). Forecasting models to predict the rebound risk were built based on these findings, with accuracies of 0.78 and 0.81 for the pre- and post-vaccine eras, respectively. These findings quantitatively demonstrate the systematic drivers of the spread of COVID-19, and the framework proposed in this study will facilitate the targeted prevention and control of the ongoing COVID-19 pandemic.

Car sharing review analysis using topic modeling social network analysis

본 논문에서는 카셰어링의 사용자 리뷰를 이용하여 긍정/부정 요소를 분석하였다. 긍정 리뷰 분석 결과에서는 가격과 서비스 이용 측면에서 높은 만족도를 가지고 있고, 부정 리뷰 분석 결과에서는 앱 이용에서 발생하는 오류에 대한 불만족이 높은 것으로 나타났다. 사회연결망분석을 이용한 긍정적인 리뷰에서는 여행과 관련된 요소가 영향력이 높았고, 부정적인 리뷰에서는 계약서와 관련된 요소가 높은 영향력을 가졌다. 리뷰 분석을 통하여 고객 서비스를 제고시킬 수 있는 구체적인 방안을 설계할 수 있을 것으로 기대된다.In this paper, the positive factors and the negative factors were analyzed using user reviews on car sharing. The positive review analysis results showed high satisfaction in terms of price and service, and the negative review analysis results showed high dissatisfaction with errors occurring in App use. In the analysis for the positive reviews using social network analysis, the travel-related factors had a high influence, and in the analysis for the negative reviews, the contract-related factors had a high influence. It is expected that it would be possible to design specific measures to improve customer service through review analysis in car sharing.

Tourism and vaccine hesitancy

• Trust in vaccination safety/efficacy dominant drivers of vaccination willingness • Promise of unrestricted international travel does not reduce vaccine hesitancy • Past travel frequency and current travel desire associated with willingness • Sociodemographic and travel-related characteristics associated with willingness • A clear link of travel-related factors and vaccine willingness established

소셜미디어를 통한 여행경험 공유에 관한 연구 : 사회연결망 분석을 중심으로

The purpose of this study is to analysis of find components that affect travelers' sharing of travel experiences expressed in social media environments. The majority of existing studies have been from big data using surveys or online reviews, where we conduct social network analysis by setting up the following research problems to analyze what travelers want to share about their travel experiences by extracting unstructured data generated directly from social media. A total of 25,959 words were collected, and the collected travel experience sharing data were extracted to the top 50 to conduct the analysis. looking at the simultaneous appearance frequency analysis and connection centricity results of noun data in this study, first, the highest ranking of noun data connection centricity was in the order of travel, people, thoughts, time, city, weather, landscape, accommodation, and village. They were found to have both high frequency and high degree of connectivity, and were linked to all texts, which can be seen as highly influential between texts. looking at the results of the study, photos and images among travel-related factors in the analysis of the frequency of simultaneous appearance have great connectivity, and travelers prefer places where they can share and spread their content on social media when choosing a destination.

The architecture of complexity in the relationships between information and communication technologies and travel: A review of empirical studies

The use of information and communication technologies (ICT) has become an integral part of people’s lives, having complex interactions with different facets of human activity participation and travel behaviour. Studies over decades have tried to understand the relationships between ICT and travel. However, the fast evolution of ICT systems and the complex nature of these relationships have resulted in a limited understanding of the overall architecture, the sub-components, and their interactions. This study aims to achieve two objectives. First, we update the current state of knowledge on ICT-travel relationships by conducting an extensive literature review. Second, based on the findings of the review, we propose a conceptual model that identifies sub-components and their interrelationships that need to be better understood to accurately grasp the impacts of ICT on transportation. We summarized 186 empirical studies from the perspectives of survey methods, ICT devices, services, applications, direct relationships with travel behaviour related variables and higher-order or indirect impacts on other parameters such as emissions and congestion. It was observed that the nature of empirical results would depend on assumptions, objectives and type of data used. The findings of the review were then used to classify architecture in the relationship between ICT and travel into four distinct types. The merits, demerits, and challenges associated with the analysis across types were then illustrated. We then develop a conceptual model that highlighted and discussed the possible interactions between 1) ICT systems, 2) travel-related factors and 3) higher-order variables.

Risk of Trypanosoma cruzi infection among travellers visiting friends and relatives to continental Latin America.

BackgroundChagas disease (CD) is regarded as a possible risk for travellers to endemic areas of continental Latin America (LA). The aim of the study is to determine the risk of Trypanosoma cruzi (TC) infection among travellers to CD endemic areas and to identify risk factors for acquiring TC infection.Methods/Principal findingWe designed a multicenter cross-sectional study among travellers in Spain (Badalona, Barcelona and Madrid). All available adults with laboratory confirmed proof of absence of TC infection from January 2012 to December 2015 were contacted. Participants referring a trip to LA after the negative TC screening were offered to participate. We performed a standardized questionnaire of travel related factors and measurement of TC antibodies in serum. A total of 971 participants with baseline negative TC serology were selected from the microbiology records. After excluding participants not meeting inclusion criteria, eighty participants were selected. Sixty three (78.8%) were female, and the median age was 38 (IQR 34–47) years. The reason to travel was visiting friends and relatives in 98.8% of the participants. The median duration of travel was 40 (IQR 30–60) days, with 4911 participants-day of exposure. Seventy seven cases (96.25%) participants had two negative TC serology tests after the travel, two cases (2.5%) had discordant serology results (considered false positive results) and one case was infected before travelling to LA. According to our data, the upper limit of the 95% confidence interval of the incidence rate of TC acquisition in travellers is 0.8 per 1000 participant-days.Conclusions/SignificanceAmong 79 non-CD travellers to TC endemic areas, we found no cases of newly acquired TC infection. The incidence rate of TC acquisition in travellers to endemic countries is less than or equal to 0.8 per 1000 traveller-days.

Understanding the Drivers of Mobility during the COVID-19 Pandemic in Florida, USA Using a Machine Learning Approach

As of March 2021, the State of Florida, U.S.A. had accounted for approximately 6.67% of total COVID-19 (SARS-CoV-2 coronavirus disease) cases in the U.S. The main objective of this research is to analyze mobility patterns during a three month period in summer 2020, when COVID-19 case numbers were very high for three Florida counties, Miami-Dade, Broward, and Palm Beach counties. To investigate patterns, as well as drivers, related to changes in mobility across the tri-county region, a random forest regression model was built using sociodemographic, travel, and built environment factors, as well as COVID-19 positive case data. Mobility patterns declined in each county when new COVID-19 infections began to rise, beginning in mid-June 2020. While the mean number of bar and restaurant visits was lower overall due to closures, analysis showed that these visits remained a top factor that impacted mobility for all three counties, even with a rise in cases. Our modeling results suggest that there were mobility pattern differences between counties with respect to factors relating, for example, to race and ethnicity (different population groups factored differently in each county), as well as social distancing or travel-related factors (e.g., staying at home behaviors) over the two time periods prior to and after the spike of COVID-19 cases.