Travel Willingness Research Articles

Investigating the Multifaceted Impact of Urban Environment on Winter Green Travel in Cold Regions: An Empirical Study of Shenyang, China

Green travel offers significant benefits, including reducing pollution and alleviating traffic congestion. Despite these advantages, green travel is less popular in cold regions, particularly during winter, due to comfort, convenience, and safety concerns. While climate factors are immutable, enhancing the urban environment can promote winter green travel. This paper examines the impact of urban environments on residents’ willingness to engage in green travel in cities located in cold regions during winter. Using data from Shenyang, China, a comprehensive structural equation model based on the stimulus–organism–response (S-O-R) theory was constructed, revealing the causal relationships and underlying structure between environmental factors and green travel willingness. The model demonstrates that social, built, natural, and travel environments collectively shape residents’ willingness to engage in green travel, with the social environment emerging as the most impactful factor. Additionally, this study identified two crucial mediating variables, travel perception evaluation and attitude towards green travel, which indirectly influence green travel willingness. This study also identifies nine critical factors—often underappreciated in traditional analyses—that should be prioritised in urban planning. These findings advance the understanding of green travel behaviour in winter cities by illustrating the complex interplay between environmental factors and individual attitudes while providing actionable guidance for fostering supportive social environments and strategically enhancing built and travel environments to promote green travel in cold climates.

Effect of Perceived Risk and Travel Motivation on Tourists’ Decisions in a Crisis Context: A Domestic Tourist Perspective

The significance of tourists’ perceived risk and travel push motivation as need-driven factors has not been extensively examined within a crisis context. This study aims to evaluate their combined effect on tourists’ attitudes and behaviors, focusing on travel satisfaction and travel willingness. The study used Maslow's theory of needs as the underpinning theory, survey data collected from a sample of 278 domestic tourists in Vietnam, and the partial least squares structural equation modeling for data analysis. The results showed that perceived risk negatively affected tourists’ travel satisfaction during the pandemic, but it did not influence their travel willingness after the pandemic. In contrast, travel push motivation had a positive impact on tourists’ travel satisfaction and their willingness to travel postpandemic. Perceived risk had a greater effect on tourists’ satisfaction compared to travel push motivation. This result implies that domestic tourists would be more concerned about safety needs (a lower order need) while traveling in this particular pandemic context. However, travel push motivation (a higher order need) is more powerful in encouraging tourists to travel. Based on the results obtained, several implications are suggested for destination managers to attract and satisfy tourists in the context of a crisis.

Bridging the gap: Leveraging AI to enhance access to clinical trials for underrepresented and underserved populations with cancer.

e13701 Background: National guidelines emphasize the crucial role of clinical trials (CTs) participation for all cancer patients. Despite this, significant barriers hinder access, particularly for underrepresented populations. Patients encounter obstacles such as geographic disparities and limited knowledge of tumor genetics, which impact their access to innovative CTs in metropolitan areas and academic centers. Furthermore, the increasing requirement for specific genetic profiles poses an additional challenge. This study aims to assess the impact of digital health platforms on improving access to CTs, with a specific focus on barriers related to ethnicity, geographic location, and disease knowledge. Methods: Leal (formerly, Trialjectory) is an AI-based platform that matches cancer patients to CTs based on self-reported medical profiles. The study included a diverse cohort of 39,031 users who completed the questionnaire. The patients’ geographic distribution across the US was analyzed, considering proximity to medical/academic centers, willingness to travel, and disease knowledge. Additionally, we examined genetic testing and biomarker knowledge across various clinical and sociodemographic groups. Results: The digital platform significantly improved geographic and ethnic diversity among CT seekers. Compared to industry standards of as low as 3% CT diversity, nearly a quarter of platform users were non-whites. Users outside major metropolitan areas, where CT enrollment is traditionally lower, were also better represented. Markedly, non-whites reported a lower willingness to travel outside their residence for CT participation compared to their white counterparts (p < 0.0001). A gap in disease knowledge was more evident in minority groups (p < 0.0001), with African Americans having a lower probability of knowing their mutation status compared to other ethnic groups. Conclusions: Access to CTs is hindered by barriers such as ethnicity, location, and knowledge. AI-based platforms swiftly identify and resolve these issues, empowering healthcare and support organizations to address individual patient concerns and boost diversity in CTs. AI analysis pinpoints underrepresented populations and regions with limited trial access, paving the way for tailored education and support initiatives to encourage informed decision-making. Strategically establishing trial sites in relevant areas addresses limited travel willingness due to cultural or socioeconomic constraints. AI technologies offer a promising approach to mitigate disparities, improve disease knowledge, ensuring equitable CT access, and enhancing healthcare outcomes for underrepresented populations.

Multi-Objective Planning of Commuter Carpooling under Time-Varying Road Network

Aiming at the problem of urban traffic congestion in morning and evening rush hours, taking commuter carpool path planning as the research object, the spatial correlation of traffic flow at adjacent intersections is mined using convolutional neural networks (CNN), and the temporal features of traffic flow are mined using long short-term memory (LSTM) model. The extracted temporal and spatial features are fused to achieve short-term prediction. Considering the travel willingness of drivers and passengers, a multi-objective optimization model with minimum driver and passenger loss time and total travel time is established under the constraints of vehicle capacity, time windows and detour distances. An Improved Non-dominated Sorted Genetic Algorithm-II (INSGA-II) is proposed to solve it. The open-loop saving algorithm is used to generate an initial population with better quality, and the 2-opt local search strategy is adopted in the mutation operation to improve search efficiency. The influence of vehicle speed on the matching scheme is analyzed. The research results show that under the same demand conditions, the total travel distance of the carpool scheme is reduced by about 56.19% and total travel time is reduced by about 65.52% compared with the non-carpool scheme. Research on carpool matching under time-varying road networks will help with urban commuting efficiency and environmental quality, and play a positive role in alleviating traffic congestion and promoting carpool services.

Study on the Influence of Peoples Travel Willingness Based on the Factors of Airline Ticket Price Change

This essay takes the popular traveling destination: Dali, Yunnan, China, as an example and studies how the price changes of air tickets would affect the willingness of people to travel. A questionnaire survey was carried out and 88 responses were received, the focus group was people aged between 19-25 because this group is becoming the main consumption group of the tourism industry. The result shows with only a 30% discount on flight tickets between Shenzhen and Dali, it could bring 408,720 potential travelers every year, contributing to 252,852 hotel nights every year, CNY 138,229,128 income every year only for the hotel industry as well as CNY 8,293,748 tax income for the local government every single year. Later, this paper also analyzes the potential advantages and disadvantages after a price discount has been given to the tourists. It also gives recommendations to the tourism industry and the local government. This research is meaningful in helping the government and the tourism industry understand better to what extent the price of the air ticket influences peoples traveling willingness, as well as have better predictions on the consumption preferences of future tourists.

Travel behavior of visually impaired people before and during COVID-19

COVID-19 has imposed significant changes to the daily routines of urban residents, significantly impacting travel behaviors. Visually impaired people are a disadvantaged group in transportation, and may be more vulnerable to disproportionate impacts due to COVID-19. This study explores the travel behavior of visually impaired people before and during COVID-19. It applies the ordered logistic regression model and a structural equation model to investigate whether satisfaction with travel condition (including bus voice prompts, blind guidance systems, intersection navigability, and urban blind-tracks) and whether willingness to travel (related to using blind-tracks, traveling by bus, and going to active places), respectively. It also uses a correlation analysis to compare the effects of travel condition satisfaction and psychological state on travel willingness. A multiform questionnaire survey was conducted to collect travel data from 218 visually impaired people in Taiyuan, China. The results indicate that the extent of impacts of related variables on travel condition satisfaction and travel willingness of visually impaired people did differ before and during COVID-19. Psychological state plays an important role in analyzing the travel willingness of visually impaired people, particularly during COVID-19. Our findings provide a reference for developing travel policies that can positively impact the travel behavior of visually impaired people, particularly during public health crisis.

Acceptability toward Policy Mix: Impact of Low-Carbon Travel Intention, Fairness, and Effectiveness

Policymakers are implementing a combination of various travel demand management (TDM) policies to effectively reduce carbon emissions. Widespread acceptance of these policy mixes by residents plays a pivotal role in ensuring their successful implementation. Drawing upon the value–belief–norm (VBN) theory and policy-specific beliefs (perceived freedom, perceived fairness, and perceived effectiveness), this study evaluates the impact of low-carbon travel psychology and policy characteristics on the acceptability of a policy mix formed by the combination of mandatory measures and guiding measures within TDM. The empirical analysis leverages survey data obtained from 839 participants and employs a structural equation model. The results indicate that low-carbon travel willingness, perceived fairness, and perceived effectiveness significantly influence acceptability. Consequently, fostering low-carbon travel willingness among residents is essential for enhancing overall policy acceptability. Moreover, policy mixes that encompass incentives for the development of new energy vehicles (NEVs) are generally considered the most acceptable among residents due to their non-restrictive impact on private car usage. Given this analysis, policymakers should consider designing policy mixes that balance acceptability and effectiveness, such as incorporating policies for the development of NEVs, to address traffic pollution.

Medical tourism index and travel willingness via travel anxiety: PLS-NCA approach

ABSTRACT This empirical research developed a conceptual model to predict medical tourists’ willingness to travel in the post-pandemic era. The paper employed complexity theory to assess the complex role of medical tourism index (MTI) dimensions on tourist willingness through travel anxiety. Data were gathered using a questionnaire-based survey and analysed using partial least squares structural equation modelling (PLS-SEM) and necessary condition analysis (NCA). The findings proved that travel anxiety partially mediated the association between the MTI dimensions and travel willingness. Besides, findings showed that perceived susceptibility and severity increase travel anxiety among medical tourists. According to the NCA findings, MTI dimensions are necessary to reduce tourists’ fear of travel, increasing their willingness to travel to a country with unique potential for medical tourism. As such, policymakers should try to improve the medical tourism environment and create a favourable environment to reduce travel anxiety.

Understanding Travel Behavior of Electric Car-Sharing Users under Impact of COVID-19

The outbreak of the COVID-19 pandemic has raised concerns about the use of public transport, with a surge in people considering personal car usage. However, owning private cars is costly and wasteful of resources. Electric car-sharing (ECS) is considered a safer and more private mode of transportation compared with public transportation. The COVID-19 pandemic has affected transport on transportation policies and travel willingness. What is the effect of the COVID-19 pandemic on CS travel, especially considering the safety issues during the COVID-19 pandemic? This study analyses the differences in the travel characteristics of private car owners and nonowners while using CS under the influence of the COVID-19 pandemic. Quantitative analysis during four months before and four months after the outbreak of the COVID-19 pandemic is conducted based on CS order data in Lanzhou, China. It was found that the number of CS orders fell by 55.8% during the COVID-19 pandemic. Travel behavior during the pandemic is different from that before the outbreak of the pandemic. Additionally, both private car owners and nonowners use CS while having differences in travel characteristics. Based on the results, business suggestions are introduced on the distribution of vehicles to help improve the profit of CS operators.

Antecedents of Booster Vaccine Intention for Domestic and International Travel

Herd immunity through vaccination has been a major technique for long-term COVID-19 infection management, with significant consequences for travel willingness and the recovery of the hospitality and tourism industries. However, indications that vaccine-induced immunity declines over time imply the need for booster vaccines. This could minimize the perceived health hazards of travel while enhancing travel propensity. This study integrated the theory of basic human values, the norm activation model, and the theory of planned behavior to investigate the role of cognitive aspects of individuals’ booster vaccine intention on domestic and international travel intention. More importantly, the study examined the role of value in activating moral responsibility and individuals’ beliefs to take the booster vaccine before traveling. A total of 315 Korean samples were collected to test the proposed conceptual model using structural equation modeling. In general, the results supported the proposed hypotheses. Notably, the intention to take the booster vaccine has a substantial impact on the intention to travel internationally. Furthermore, the communal values accept benevolence have an influence on personal morals and beliefs about receiving booster vaccines before international traveling.

What drives willingness to travel in the context of COVID-19?—A measurement of eco-environmental values

The outbreak of COVID-19 has had tremendous impacts on human health and the world economy. Studies have focused on the impact of COVID-19 on potential tourists and tourism destinations from the perspectives of individuals, industries and organizations, and they have provided some measures for tourism recovery. However, under the situation of individual restriction, research has not systematically explained residents’ desire for trips of different distances and factors or the similarities and differences in the factors affecting tourism willingness for trips of different distances. In this context, a measurement of eco-environmental values is used to investigate these issues to help the tourism economy recover. Using online questionnaires covering all provinces in mainland China, this paper investigates residents’ travel willingness to make trips of different distances, and it utilizes binary logistic regression analysis to examine the factors that help predict tourists’ travel intentions. In addition, the patterns of willingness to travel different distances are displayed in maps generated by ArcGIS software. The results suggest that the objective COVID-19 confirmed case distribution follows distance decay theory; however, the distribution patterns of travel willingness are not in accordance with distance decay. The factors that have a significant impact on predicting travel willingness regarding the three kinds of trip distances are educational background, cognition of COVID-19, and geographical division factors. Income and the severity of the pandemic situation play different roles in predicting travel willingness in this study. Overall, the findings of this study extend the application of distance decay theory, which contributes to tourism studies in the COVID-19 context. The findings are also beneficial for tourism recovery and crisis management against the backdrop of pandemic normalization.

Analyzing the Trends of COVID-19 and Human Activity Intensity in Malaysia

COVID-19 has struck the world with multiple waves. Each wave was caused by a variant and presented different peaks and baselines. This made the identification of waves with the time series of the cases a difficult task. Human activity intensities may affect the occurrence of an outbreak. We demonstrated a metric of time series, namely log-moving-average-ratio (LMAR), to identify the waves and directions of the changes in the disease cases and check-ins (MySejahtera). Based on the detected waves and changes, we explore the relationship between the two. Using the stimulus-organism-response model with our results, we presented a four-stage model: (1) government-imposed movement restrictions, (2) revenge travel, (3) self-imposed movement reduction, and (4) the new normal. The inverse patterns between check-ins and pandemic waves suggested that the self-imposed movement reduction would naturally happen and would be sufficient for a smaller epidemic wave. People may spontaneously be aware of the severity of epidemic situations and take appropriate disease prevention measures to reduce the risks of exposure and infection. In summary, LMAR is more sensitive to the waves and could be adopted to characterize the association between travel willingness and confirmed disease cases.

Changes in public travel willingness in the post-COVID-19 era: Evidence from social network data

<abstract> <p>Amid the impact of COVID-19, the public's willingness to travel has changed, which has had a fundamental impact on the ridership of urban public transport. Usually, travel willingness is mainly analyzed by questionnaire survey, but it needs to reflect the accurate psychological perception of the public entirely. Based on Weibo text data, this paper used natural language processing technology to quantify the public's willingness to travel in the post-COVID-19 era. First, web crawler technology was used to collect microblog text data, which will discuss COVID-19 and travel at the same time. Then, based on the Naive Bayes classification algorithm, travel sentiment analysis was carried out on the data, and the relationship between public travel willingness and urban public transport ridership was analyzed by Spearman correlation analysis. Finally, the LDA topic model was used to conduct content topic research on microblog text data during and after COVID-19. The results showed that the mean values of compelling travel emotion were -0.8197 and -0.0640 during and after COVID-19, respectively. The willingness of the public to travel directly affects the ridership of urban public transport. Compared with the COVID-19 period, the public's fear of travel infection in the post-COVID-19 era has significantly improved, but it still exists. The public pays more attention to the level of COVID-19 prevention and control and the length of travel time on public transport.</p> </abstract>

MODELLING THE NEW BRAND EQUITY OF DESTINATION THEORY AND TRAVEL INTENTION: AN EMPIRICAL STUDY FROM VIETNAM

Purpose – There is a lack of research on the causal relationship between a tourism destination’s new brand equity and travel intention. This study aims to identify and explore the model of tourism destination new brand equity, validate and evaluate the relationships between tourism destination new brand equity elements and travel intention in the context of tourism destinations. Design – This study collected primary data from March to May 2021 based on a questionnaire survey of 589 domestic tourists traveling to Dong Thap province. The newly proposed conceptual framework was evaluated and validated using the PLS-SEM method. Findings – The main new findings of this study were the identification of the nine constructs of destination brand equity and their association with travel likelihood. The study revealed the effect of the new constructs of destination brand equity constructs on travel willingness, which can promote domestic tourism in Dong Thap province. Originality of the research – The current study can be considered as an important contribution to the theoretical and managerial implications for local authorities to understand the new model of destination brand equity from the perspective of domestic tourists. The findings of this study may increase travel intention and promote tourism in Dong Thap province.

Impact of Emotional Contagion through Social Network Sites on Travel Willingness in the Pandemic

ABSTRACT In previous research on consumer network usage behavior, the focus was mainly on how to collect information. Few studies have delved into consumers’ psychological responses to information and whether they are also affected by emotional contagion. Due to the impact of the COVID-19 pandemic, uncertainties and risks for travel have increased. Both the positive and negative emotional performance of travel information sharers often affect receivers’ feelings. This study explores whether the sharing of travel information on social network sites will have an emotional impact. It also explores how that might influence site members’ attitudes and behavioral intentions related to travel. According to the study, people have recently been infected with negative emotions. They hope those emotions will be transformed into positive ones by sharing travel information during the pandemic. This study shows that emotional contagion can occur in both physical and virtual spaces, and it will further affect the recipient’s attitudes toward certain tourist destinations and travel willingness.

Research on the Intention of College Students' Tourism Behavior under the Epidemic: Takes Xi'an Universities as an Example

College students' tourism group has always been an indispensable market segment of the tourism market. In order to study the outbreak period of normalized college students 'tourism consumption behavior change, this paper adopts the questionnaire survey and depth interview, in xi'an university students as a sample, from the outbreak of the tourism behavior change, tourism behavior intention and may influence its behavior decision factor selection of three dimensions, with the method of mathematical statistical method analysis. Studies show that the consumption behavior, destination choice, travel time and frequency, and travel willingness of college students have all changed during the normalization of the epidemic. And from the government, tourism enterprises, college students themselves and other three dimensions put forward solutions.

Research on the Impact of Social Propaganda and Guidance Policies on Public Green Travel Intention: Evidence From China

Social propaganda and guidance policies have important practical significance for promoting public green travel. In recent years, the rapid development of digital technology, network, and mobile communication technology has promoted a revolutionary change in the dissemination of information and services. However, in the era of new media communication driven by emerging technologies, there is still a lack of research on the impact of social propaganda and guidance policies on public willingness to travel green. Based on the theory of planned behavior and the survey research of China's urban residents, this study investigates the impact of social propaganda and guidance policies on public willingness to travel green. Regression analysis is used to identify the influencing factors of public green travel willingness, and to reveal the internal impact mechanism of social propaganda and guidance policies on public green travel willingness. The mediating role of public green travel attitude, subjective norm, and perceived behavior control in social propaganda and guidance policy and travel willingness are also empirically analyzed. The results show that social propaganda and guidance policies have a significant positive impact on green travel attitudes, subjective norms, perceived behavior control, and green travel intentions. Green travel attitude, subjective norm, and perceived behavior control play a mediating role in the relationship between social propaganda and guiding policies and green travel willingness, but the mediating effect of the subjective norm is relatively weak.

Determining the Antecedents Influences on Travel Intention and Willingness to Pay during the Pandemic

ABSTRACT The study addresses the modulating effects of the impact of boredom and travel motivation on travel intention; additionally, the resulting model includes the impact of this intention and the fear of COVID-19 on the willingness to pay (WTP) more to benefit from additional safety measures at the destination. Furthermore, the moderating role of fear of COVID-19 was also tested as well as the mediating role of travel motivation in the model. Overall, 549 valid data were collected from potential tourists who live in Turkey and Russia. In the light of the findings of the study, the boredom was found to have a positive and significant effect on the travel motivation and intention. On the other hand, WTP is positively affected by travel intention and fear of COVID-19. Moreover, it was revealed that while the fear of COVID-19 had a moderating role, the travel motivation had a mediating role. The empirical results provide theoretical and practical implications for tourism managers for the development of safety measures in tourism services and the design of effective actions to restore tourism.

A Study on the Decay Model of Multi-Block Taxi Travel Demand under the Influence of Major Urban Public Health Events.

A sudden major public health event is likely to have a negative impact on public transport travel for residents, with public travel modes such as the metro and conventional buses experiencing varying degrees of decline in patronage. As a complement to public transport, taxi travel will suffer the same impact. Land use and population density among various functional blocks in a city are different, and therefore their changing rates in taxi travel demand are varied. This paper reveals the taxi travel demand correlations between urban blocks and then constructs a taxi travel demand decay model based on the Dynamic Input-Output Inoperability Model (DIIM) to simulate the decay degree of taxi travel demand in each block. When a major public health event occurs, the residential panic levels in different functional blocks may vary. It results in variable changing speeds of residential travel demand in each block. Based on this assumption, we use the intensity of travel demand as a correlation strength factor between blocks, and equate it with the technical coefficient in the DIIM model. We also define other variables to serve in model construction. These variables include the decay degree of travel demand intensity, residential travel willingness, coefficient of travel demand decay, derivative coefficient of travel demand interdependency, and demand perturbation coefficient. Lastly, we select a central area of Ningbo as the study area, and use taxi travel data in Ningbo during the COVID-19 pandemic of 2020 as input, simulate taxi travel demand dynamics, and analyze the accuracy and sensitivity of the model parameters. The relative errors between the five types of blocks and the actual decay of travel demand intensity are 8.3%, 3.8%, 8.7%, 5.5%, and 5.3%, respectively, which can basically match the actual situation, proving the validity of the model. The results of the study reveal the pattern of taxi travel demand decay among various blocks after major public health events. It provides methodological reference for decision makers to understand the development trend of multi-block taxi travel demand, so as to help form effective emergency plans for different blocks.

Analyzing Rail Traffic Diversion Based on Machine Learning Technique considering Transportation Security

Based on a stated preference survey, we comprehensively analyze the travel psychology of residents and the advantages and disadvantages of rail transit and conventional buses, travel time, travel cost, travel security, and vehicle comfort and investigate the relationship between the relevant influencing factors and the transition probability from rail transit to buses. A stochastic utility theory is introduced to describe the transfer behavior pertaining to travel modes, and a binary Logit model for diversion transfer is constructed. The decision tree is also used to predict the diversion transfer. Then, based on the large amount of travel willingness data obtained through the stated preference survey, a maximum likelihood estimation method is used to calibrate the parameters of the binary Logit model. The performance of the binary Logit proves to be better than that of the decision tree. Results show that the travel time most notably affects the passenger flow transfer, followed by the vehicle comfort. Finally, Guangzhou Rail Transit Line 3 is considered an example, and the diversion route planning and design are performed according to the constructed diversion transfer probability model to verify the effectiveness and practicability of the model. The research provides an effective theoretical basis and technical reference for other cities to perform rail traffic diversion planning. Based on these results, the following suggestions can be made: (1) the organization of public transportation routes, delivery volume, and travel speed outside should be improved; (2) undertaking combined operation of bus and rail transportation and integrated development is preferred; (3) the transportation management should focus on the comprehensive function development and hardware support of public transportation stations. The convenience and comfort of rail transit are closely related to the facilities and functions of the stations and their connections, which should be highly valued.