Travel Purpose Research Articles

The relationship between the degree of ethnic enclaves and travel mode choices

ABSTRACT This article aims to highlight how the degree of ethnic enclaves plays an important role in travel mode choices according to racial/ethnic groups in the Atlanta metropolitan areas in the US. This study finds that ethnic enclaves play a different role in travel mode choices according to the degree of ethnic enclaves, racial/ethnic groups, and travel purposes. For example, white people in low enclaves are more likely to take public transit for work (0.390), and black people in high enclaves are more apt to use household carpool, inter-household carpool, public transit, and walk/bike for school (1.545, 1.725, 1.205, 1.659, respectively) and public transit and walk/bike for other purposes (0.273 and 0.233, respectively). Asian people in high enclaves are more inclined to take household carpool for leisure (3.480), and Hispanic people in low enclaves are more likely to use inter-household carpool for shopping (6.377).

The impact of the maritime border on the development of Po- land’s border areas in terms of expenses of foreigners and the COVID-19 pandemic

The aim of this paper is to present the issue of border traffic of foreigners and their expenses on the Polish maritime border. The study is based on quantitative data on border traffic and expenses of foreigners in Poland. The time of the study was 2014–2021. Thus, the impact of the coronavirus disease (COVID-19) pandemic on foreigners’ mobility and spending in Poland was additionally taken into account. For the purpose of this paper, the following study methods were adopted: reference literature analysis, secondary data analysis and comparative analysis. The paper mainly uses data from official statistics and the Border Guard. The study has shown that the Polish maritime border generates the smallest volume of border traffic, however, its share increased during the analysed period from 0.7% in 2014 to 0.8% in 2021. Nevertheless, it is foreigners crossing the maritime border who on average spend in Poland more than twice as much as foreigners crossing the Polish land border. In 2020, they spent the most on non-food goods (40%) and services (34.7%). As the study results reveal, their spending patterns and travel purposes are different from those of foreigners crossing the land border. The potential that foreign visitors bring is significant for the development of border areas in such sectors as tourism, services and trade. The analysis of the motivation of foreigners crossing the sea border makes it possible to shape the regional development policy of this particular area.

Keeping two balls in the air: The bleisure travel experience

This study examines bleisure travel across pre-trip, during, and post-trip phases. After interviewing 23 bleisure travelers, the study found that in the pre-travel phase, bleisure travelers had more complex planning and needed more coordination. Therefore, they were well-organized, futuristic, and competent in travel arrangements. During the travel, they sought ‘educational experiences’, ‘short-squeezed leisure plans’, ‘family and kids-oriented activities’, and preferred ‘not trying one-time events’. They used strategies such as ‘flexibility in travel arrangement’, ‘clarification of travel companions’, ‘mixing business and leisure activity’, and ‘remote work’ to balance the two travel purposes. Finally, in the post-trip phase, bleisure travelers described feeling ‘physically tired but mentally rejuvenated’ with a sense of being ‘free of the guilt’ of their absence from home.

ASSAMESE TRAVEL LITERATURE TREND, CLASSIFICATION AND CHARACTERISTIC

Travel literature is one of the latest genres of literature which has been written only after travelling by human. Basically, the humans’ history is traveler’s history and it is related to the primitive needs like food, cloth and residence. In the modern world, the purpose of travel has explored the diverse cultures and creates a sense of understanding. Besides all this, people like to travel as per their current requirements in the 21st century. It is mentionable that the governance of Assam has handed over to British in 1826 and a new law and order has formed. Assamese society is changed in all fields i.e. political, economic, social, educational and cultural etc. Due to all these, the people of Assam showed their interest in many fields and necessarily made some internal and external journey. As the result of this, the Assamese travel writings have gradually increased. Assamese travel literature is the next form of that tradition which have a unique identity and characteristics. As time passes through, the tendencies of primitive travel has converted into practical travel and the Assamese people have gathered some experiences which are the main sources of writings. Mentionable that these writings can be perfect travel literature only after merged with literary virtue. It is true even in the context of Assamese travel literature too and the prosperity and development of this genre is proved today. But a deep study of this genre has not done yet and that’s why the necessity of the study encourages for this research paper where the pre and post-Independence Assamese travel literature will be analytically discussed as Assamese Travel literature-trend, classification and characteristic.

APLIKASI SPPD PADA PT PLN (PERSERO) UP2D S2JB BERBASIS WEB

The official trip that will be carried out by agency employees is a trip that will be assigned somewhere (Foreign Service) to the agency. These duties include tasks that are important to related agencies, such as attending seminars, providing training, exp loring collaboration, attending ceremonies, and carrying out other activities. To organize and prepare for a successful business trip, all necessary steps must be taken, the first must be the preparation of the necessary documents, confirming travel ticket s and accommodation, preparation of a predetermined itinerary, and all other elements necessary for a successful and comfortable working environment. The purpose of travel and its introduction when employees go to work is to facilitate the employee's journey to work. The method to be used is the SDLC method with the Waterfall Model which processes systematically and sequentially. The system facilitates the effectiveness of employee performance in making SPPD at PT. PLN (Persero) UP2D S2JB, and can store data properly., facilitate access and and guarantee the security of stored data and can solve problems that exist in the company in inputting data and m aking reports on time and accurate.

The effect of China’s travel purposes on Hungary’s inbound tourism

This paper conduct an empirical research and investigate the effect of China’s travel purposes on Hungary’s inbound tourism during a period of uncertainty. The study takes into account the influence of the Covid-19 and Ukraine-Russia war on inbound tourism. The short-run monthly sample from January 2018 to July 2022 has been utilzed to capture the uncertain period. A Poisson Regression Model has been applied to calculate the efficacy of China’s travel purposes on Hungary’s inbound tourism. This study finds that China’s leisure and business trips have positive and significant effect on Hungary’s inbound tourism. China’s business trips has a large contribution to Hungary’s inbound tourism than leisure trips. During the uncertain period, the Covid-19 and Ukraine-Russia war had a negative and significant effect on arrivals of tourists to Hungary. Inbound tourism from China to Hungary had contributed positively to the economic performance of Hungary’s tourism sector.

The Relationships between Adverse Weather, Traffic Mobility, and Driver Behavior

Adverse weather conditions impact mobility, safety, and the behavior of drivers on roads. In an average year, approximately 21% of U.S. highway crashes are weather-related. Collectively, these crashes result in over 5300 fatalities each year. As a proof-of-concept, analyzing weather information in the context of traffic mobility data can provide unique insights into driver behavior and actions transportation agencies can pursue to promote safety and efficiency. Using 2019 weather and traffic data along Colorado Highway 119 between Boulder and Longmont, this research analyzed the relationship between adverse weather and traffic conditions. The data were classified into distinct weather types, day of the week, and the direction of travel to capture commuter traffic flows. Novel traffic information crowdsourced from smartphones provided metrics such as volume, speed, trip length, trip duration, and the purpose of travel. The data showed that snow days had a smaller traffic volume than clear and rainy days, with an All Times volume of approximately 18,000 vehicles for each direction of travel, as opposed to 21,000 vehicles for both clear and wet conditions. From a trip purpose perspective, the data showed that the percentage of travel between home and work locations was 21.4% during a snow day compared to 20.6% for rain and 19.6% for clear days. The overall traffic volume reduction during snow days is likely due to drivers deciding to avoid commuting; however, the relative increase in the home–work travel percentage is likely attributable to less discretionary travel in lieu of essential work travel. In comparison, the increase in traffic volume during rainy days may be due to commuters being less likely to walk, bike, or take public transit during inclement weather. This study demonstrates the insight into human behavior by analyzing impact on traffic parameters during adverse weather travel.

Effects of Social Media in Tourism Marketing: Outlook on User Generated Content

Social media has transformed the tourism industry, shifting it from its conventional model into an extreme knowledge pattern. This study focuses on the use of social media for tourism marketing to enhance tourist satisfaction through user-generated content. A mixed research method was employed, with an exploratory approach used to identify the critical success factors of social media user-generated content (UGC) for marketing tourism products. Purposive sampling was utilized, and data were collected from 200 respondents. To analyze the data, frequency distribution, multiple regression, and hypothesis testing were employed for user-generated content on social media for tourism marketing. The results of the study reveal that people spend an average of one to six hours daily on social media platforms. Graduate students aged 21-30 and employed individuals are more likely to use social media for travel purposes. The study also indicates that factors such as advocacy, a positive brand impression, travel inspiration, and careful content development are crucial for social media in tourism marketing. The travel industry provides visitors with personalized services, offering a social media network that systematically shares users' and like-minded people's experiences, allowing users to customize their anonymity and add value to their personal details.

Customers’ behavioral intentions when visiting upscale restaurants: Enjoying the experience or posturing?

High-level gastronomy is booming and, in many cases, has become the main purpose for travel. This research aims to investigate the relationships between the performance of high-level restaurants, the affective components they generate, and how they can affect customer behavior both in regard to the restaurant and the destination in which it is located. The empirical work was analyzed by means of a multiple indicators multiple causes model (MIMIC) and structural equation modeling (SEM). The survey was administered to customers who visited Michelin-star restaurants. The main results indicate that enjoyment of the experience and the status it provides are important factors motivating consumers to visit these types of restaurants and that their behavioral intentions can influence their family, friends, acquaintances, and destination. Therefore, it is suggested that restaurant owners and destination managers develop joint strategies beneficial for the gastronomic tourism sector.

Effects of a price incentive policy on urban rail transit passengers: A case study in Nanjing, China

To alleviate travel congestion at peak periods or on congested routes, some measures for urban rail transit (URT) systems including fare incentive schemes and subsidy policies have been widely adopted. Existing measures can alleviate demand during peak periods or on congested routes to a certain extent, but they do not consider the passengers’ satisfaction with their travel experience. Consequently, this study aims to investigate passengers’ acceptance of a fare incentive policy offering a discounted fare during off-peak periods or on uncongested routes before it is implemented in Nanjing, China. To understand passengers’ acceptance of the policy, this study explores the effects of the policy on passengers’ route choices. Furthermore, the differences among passengers with respect to different travel purposes and travel times in route choices have been analyzed. A revealed preference (RP) survey and a stated preference (SP) survey consisting of 463 samples from URT passengers in Nanjing, China, were conducted and analyzed using a random-parameter multinomial logit (RPMNL) model. Results show that socio-economic variables, travel characteristics, travel cost, departure time, and travel distance significantly affect passengers’ route choices. Furthermore, the route choices of passengers with different travelling times and purposes vary. Commuters are most sensitive to travel cost during off-peak periods, followed by non-commuters who travel during peak periods. Departure time and travel distance most significantly affect the route choices of non-commuters during peak periods and off-peak periods, respectively. The findings of this study could assist transit agencies in designing attractive fare incentive policies for public transit passengers and offer valuable insights into effective demand management strategies. Moreover, the lessons derived from this study may guide the implementation of fare incentive policies in Nanjing, China, and elsewhere.

Resurgence in paradise: decoding the patterns of arrivals with different trip purposes in Hawaii’s post-pandemic tourism recovery

ABSTRACT This study provides a comprehensive investigation into the resurgence of Hawaii’s tourism industry following the COVID-19 pandemic, utilizing Seasonal Autoregressive Integrated Moving Average models with intervention analysis. We conducted an in-depth analysis of Hawaii’s tourism data, encompassing the volume of domestic arrivals and diverse travel purposes before and after the pandemic, subsequently estimating the recovery timeframe for each travel purpose. Enriching theoretical discourse and practical applications, this study furnishes a nuanced understanding of Hawaii’s tourism resurgence while concurrently establishing a framework for modelling, predicting, and planning tourism recovery in the face of future global crises akin to the recent pandemic.

Characteristics of business, pleasure, and VFR segments who travel during risky times

PurposeThis study aims to explore what may have contributed to risk-taking travel behavior during the COVID-19 pandemic and what may be the drivers of satisfaction and intention to travel again. The study compares travelers based on their travel purposes (business, pleasure and visiting friends and relatives [VFR]) and explore the influence of subjective well-being received from their travel activities.Design/methodology/approachData from a structured survey of 323 US residents who traveled during the COVID-19 pandemic were collected on MTurk. Respondents were asked travel behavior questions related to their favorite trips and to rate several multi-item scales measuring the benefits expected and received from their trip, the trip’s contribution to their positive mental and subjective psychological well-being, as well as their satisfaction with the trip and intention to travel in the future. In addition, personality measures focused on risk-taking, thrill-seeking and self-confidence, as well as additional risk-related concepts of optimism bias, probability neglect and proximity to self.FindingsData analysis revealed some differences among respondents who traveled for business, pleasure and VFR purposes. Business travelers were more risk takers and thrill seekers; pleasure travelers achieved more well-being benefits from their favorite trips during the pandemic; however, VFR travelers’ satisfaction and intention to go on similar trips were explained more by the benefits they received from their favorite trips.Originality/valueAlthough several studies addressed consumers’ travel motivation during the COVID-19 pandemic, there is a lack of empirical research comparing the characteristics of travelers based on their travel purposes, as well as their sociodemographics, personality traits and the expected and perceived well-being benefits from traveling.

Forecasting market opportunities for urban and regional air mobility

Analysts predict that future air taxis will fundamentally change the travel behavior of society. However, current market forecasts for air taxi demand varies widely because of inconsistencies in assumptions about travel purpose, technology acceptance, time savings, affordability, and safety. To successfully deploy Advanced Air Mobility (AAM), stakeholders need to reliably forecast routes where demand will be highest. To increase reliability, this study focuses on the Uber Elevate multimodal use case and combines top-down and bottom-up methodologies to forecast demand. The hybrid methodology forecasts demand within four distance bands from 100 miles to 400 miles, in 100-mile increments. Forecasting within distance bands informs a range roadmap for electrified vertical takeoff and landing (eVTOL) aircraft. Geographic information system (GIS) and network trimming techniques identify 2083 viable routes among 859 U.S. cities. The findings are that approximately 78,000 passengers daily will access at most 4214 vertipads to fly on 3023 four-passenger eVTOL aircraft. Serving routes within the first 100-mile band will require two and five times more capital for aircraft and vertiports, respectively, than for longer routes. AAM stakeholders can utilize the hybrid methodology to forecast demand for specific routes in other regions of the world and for additional use cases.

Dynamics of Campus Travel Behavior under the COVID-19 Pandemic

The COVID-19 pandemic has shown to be a global challenge that, in addition to other effects, has influenced travel behavior. This study examines factors affecting academic travelers’ mode choice before and during the pandemic and factors contributing to sustainable transportation on campus. By examining their travel patterns and behaviors, we contribute to understanding transportation preferences and identifying opportunities for sustainable transportation on university campuses. Studying academic travelers is crucial as they are significant daily travelers with a substantial impact on transportation systems and the environment. Understanding their mode choices helps transportation planners and policymakers promote sustainable transportation options. The literature has identified influential factors in making trips to university campuses, including age, gender, accommodation, cost, and travel time. However, cross-sectional studies involving comprehensive variables are lacking and the influence of the COVID-19 pandemic on transportation has not been thoroughly evaluated. To address this gap, the current study aims to evaluate novel variables, including intra-transport modes, entry permits, accessibility, parking availability, occupations, level of study, travel purpose, and visit frequency. The University of Isfahan, accessible by all modes of transport, was selected as the study area. After analyzing the questionnaire and variables using SPSS software (IBM SPSS Statistics for Windows, Version 22.0 Released 2013), travel behavior was studied by discrete choice models and the models’ coefficients were estimated using NLOGIT. The finding demonstrated that using private modes (taxi, private vehicle, and active modes) increased in response to the pandemic, while using public modes (bus or subway) represented a decline. Before and during the pandemic, most people who had the same trip purpose shifted from taking the bus to using private vehicles and active transportation. Generally, people became more inclined to walk on campus during the pandemic. This study aimed to examine the travel behavior of academic travelers, who possess diverse travel choices compared with typical commuters, thus providing valuable insights into how the broader population might respond to different transportation options. The findings offer a novel perspective for university and city planners, enabling more informed decisions regarding sustainable development in campus areas.

An electric vehicle charging load prediction model for different functional areas based on multithreaded acceleration

This paper proposes an electric vehicle (EV) charging load prediction model for different functional areas based on multithreaded technology. This model comprehensively incorporates the preference characteristics of EV users in charging behavior and mode, as well as accounting for diverse travel purposes. It is worth emphasizing that a comprehensive analysis of the spatiotemporal travel characteristics exhibited by users during different seasons and on working days versus rest days is provided. In terms of temporal scale, refined probability models are employed to accurately fit the user's travel time variables. In terms of the spatial scale, the initial position and spatial transfer probability of users are analyzed. Subsequently, leveraging graph theory, a regional transportation network model is established. On this basis, a novel improved time optimal path decision-making algorithm is proposed by holistically considering factors such as road grade, saturation, traffic conditions, one-way/two-way streets, and delay at signalized intersections. Additionally, the impact of temperature on the upper limit capacity of batteries is analyzed, as well as driving/air conditioning energy consumption models are established. It is noteworthy that a two-stage charging power variation model is introduced, enhancing the precision of charging power calculation. Finally, the effectiveness of the method is validated through case analysis. The results demonstrate a significant disparity in load demand among various functional areas, and the spatiotemporal distribution of load demand is significantly influenced by seasons and working days versus rest days. Furthermore, when employing 4 threads, the proposed method achieves a speedup ratio exceeding 2.5 compared to conventional serial methods.

Quantifying saturation point of Beijing bike-sharing market from environmental benefit: A data mining framework

This study quantitatively estimates the carbon dioxide (CO2) emissions savings from ride records for passengers whose travel behavior shifted from polluting modes (public transport and private car) to bike-sharing in Beijing. We present a framework for examining how travel time, distance, purpose, frequency, weather, and demographics affect passenger usage and estimate environmental benefits. The framework comprises modules of association rules, density-based spatial clustering, random forest, and CO2 emission estimation. Our findings show that commuters with a trip distance of 1–2 km are more likely to change their behavior patterns. Therefore, more CO2 emission savings accrue in developed districts where residential density and employment rates are higher, than in central districts. Beijing saves 4322.38 kg CO2 per day. In contrast, four districts are oversupplied and have reached saturation points in the number of bikes. Implications for planners suggest that they will be able to better control the number of bikes launched.

Does Perioperative Administration of Rabies Vaccine in Dogs Undergoing Surgical Sterilization Induce an Adequate Antibody Response?

High-volume spay/neuter events may facilitate access to free-roaming dogs to administer rabies vaccination, but important questions remain regarding the effect of surgery and anesthesia on the immune response to a vaccine administered in the perioperative period. This study evaluated the immunogenicity of primary rabies vaccination in dogs when administered during the immediate perioperative period at the time of surgical sterilization (ovariohysterectomy/orchidectomy). Healthy dogs of both sexes presenting for surgical sterilization who had never been vaccinated against rabies virus were eligible for enrollment in the study. Fifty dogs ranging in age from 5 to 96 months were enrolled and were vaccinated against rabies virus during the recovery period following anesthesia and surgery. Rabies virus neutralizing antibody (RVNA) titers were measured preoperatively and 28 days postoperatively. This cohort was compared to a historical control cohort of 57 dogs who received primary rabies vaccination for travel purposes and had RVNA titers measured at the same laboratory as the study group 28-35 days post-vaccination. After controlling for age and sex, there was no statistically significant difference in immunogenicity of a rabies vaccine administered to dogs during the perioperative period in comparison to dogs that received the rabies vaccine for travel alone in the absence of surgery. Perioperative administration of a rabies vaccine in dogs undergoing surgical sterilization induces an adequate antibody response. We recommend that rabies vaccine be administered perioperatively during spay/neuter campaigns in canine rabies endemic areas if other opportunities to access veterinary care and rabies vaccination are limited.

An integration modeling framework for individual-scale daily mobility estimation

This paper presents a novel microsimulation framework (BayABM) that integrates population synthesis, Bayesian Networks (BNs), and activity-based models (activity-BM) to model individual heterogeneity in mobility. The population synthesis simulates a synthetic population with socioeconomic and household-level attributes. The Bayesian Network is adopted to learn and compute the probability distributions of the mobility attributes and infer the mobility that most likely arises at a given time. A hierarchical location model with a random forest is developed to refine the destination choice based on the probability obtained from BN. A total of six mobility attributes are simulated, i.e., travel frequency, travel purpose, travel destination location, travel mode, activity duration, and travel duration. The strengths of the proposed framework are: (1) it can model the individual mobility dynamics; (2) it can generate individual mobility for a large-scale population; and (3) it has broader coverage with better representativeness and less strict data requirement. The proposed model is tested with a Miami-Dade County, Florida case study. A total of 7,253,672 travels for 2,680,607 persons and 860,380 households across the county are generated from a public travel diary survey. Results show that individuals at 20–50 age and 0–20 age travel most frequently, with a total of 3,413,710 and 2,074,677 activities per day. The travel peak of a day occurs at 11:00 and 18:00. The results demonstrate the efficacy of the enhanced model, especially in large-scale applications.

Inferring nonwork travel semantics and revealing the nonlinear relationships with the community built environment

The relationship between the built environment and nonwork travels reveals potential supply-demand status between urban functional facilities and diverse living travels, which provide critical indicative information for urban spatial structure optimization. Benefiting from the large-scale travel datasets, it would be possible to investigate deeper research by modeling the implied nonwork travel semantics (such as travel purposes). This study designs a framework to reveal the nonlinear relationships between the community built environment and the potential semantics of nonwork travel trajectories. First, we construct indicators to characterize the built environment of individuals’ living communities. Then, focusing on the definition of activity areas around drop-off points, latent Dirichlet allocation (LDA) is utilized to infer the implied semantics of nonwork travel. The random forest model is employed to quantify the relationships between the community built environment and nonwork travel with various semantics. The experimental results obtained with real-life datasets from Haikou, China uncover both compensatory nonwork travel derived from diverse facility accessibility and the contributions of housing attributes to travel semantic discrepancies. The findings provide scientific references for the optimization of urban spatial layouts of both functional facilities and residential communities to meet the living demands of diverse residents without long-distance intracity travel.

Impact of land use on bike-sharing travel patterns: Evidence from large scale data analysis in China

With bike sharing flourishing around the world, researches focus on the optimisation of station layouts, but seldom explore the dynamical features of residents’ bike-sharing travel habits. Land use factors probably indicate the activities and reflect the reasons behind residents’ travel habits; however, few studies provide empirical evidences, especially with big data analysis technology. Thus, we conduct a big data-driven empirical research to clarify residents’ travel patterns and the effects of land use thereon, which is based on the full system records of one typical bike-sharing city in China. K-modes clustering method is used to identify residents’ single and daily bike-sharing travel patterns. Three multinomial logit regression models are applied to exploring the selection mechanisms of the travel patterns. Eventually, we find that with the reference of ST pattern, the TPT-ENW pattern selection is most affected by the number of accommodation service places around travel destinations and one standard deviation increase will increase relative risk of this pattern by 1.57467. The MNT-SC pattern is most affected by the number of corporations and the other five single travel patterns are all significantly affected by the number of catering service places, which indicates commuting and dining are the travel purposes of these patterns. In addition, we find a multinomial logit model with individual fixed effects can explain residents’ daily travel pattern selection better than a pooled multinomial logit model. The analysis of residents’ travel routines explains the distribution of bike-sharing mobility and provides basis for bike-sharing system policy formulation.