• Journal of Smart Tourism
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• v.1 no.3
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• pp.31-44
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• 2021

The emergence of the digital economy and easy accessibility to Web 2.0 tools has seen an expansion of the influencer ecosystem within the travel and tourism industry. Founded on the principles of reference groups and peer reference there is a growing trend amongst industry practitioners who are now opting to move away from many of the traditional approaches used to market their products and services and are instead taking advantage of the concept of e-word-of-mouth (eWOM). Whilst there is a growing body of academic literature addressing the notion of influencer marketing, there is little understanding of influencer marketers themselves. Consequentially, this study addresses this gap in the literature through the quantitative examination of those who promote products, services, or companies by distributing eWOM through their online digital channels and presence; otherwise known as travel influencers. A quantitative research approach involving an online survey yielded 255 responses from travel influencers. The research findings indicate that those who work in this field prefer not to be awarded the label "travel influencer," focusing instead on their specific method of influencing, such as blogging and vlogging or sharing Instagram updates. The research also demonstrates how the new influencers have a strong role in generating travel urge and desire. The research contributes to the wider body of academic literature and travel industry practitioners by establishing the general profile of influencers and their increasingly specialized role in tourism and hospitality marketing.

• Convergence Security Journal
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• v.21 no.3
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• pp.67-73
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• 2021

According to a survey by the Korea Tourism Organization in 2018 and 2019, traveling to places recorded in TV programs or influential videos has become an important travel trend and several studies show that watching a travel program improves the intention to visit the places featured on the program. This study designed a travel program-linked data service that provides additional information on the places and events to the viewers of the travel program. Specifically, the additional information of the travel program was defined in a formal manner by dividing it into places and events, and a method of exposure of the additional information conformed to the contents of the program was designed. We also devised an international standard DVB-based data transmission method that provides the additional information to data services appropriately in time for program broadcasting. This study is significant in that it tested new applications of data services for travel programs.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.32-48
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• 2023

Autonomous driving technology is shaping the future of personalized travel, encouraging personalized travel, and traffic impact could be influenced by individualized travel behavior during the transition of driving entity from human to machine. In order to evaluate traffic impact, it is necessary to estimate the total number of trips based on an understanding of individual travel characteristics. The Activity-based model(ABM), which allows for the reflection of individual travel characteristics, deals with all travel sequences of an individual. Understanding the relationship between travel and travel must be important for assessing traffic impact using ABM. However, the ABM has a limitation in the data hunger model. It is difficult to adjust in the actual demand forecasting. Therefore, we utilized a Tour-based model that can explain the relationship between travels based on household travel survey data instead. After that, vehicle registration and population data were used for correction. The result showed that, compared to the KTDB one, the traffic generation exhibited a 13% increase in total trips and approximately 9% reduction in working trips, valid within an acceptable margin of error. As a result, it can be used as a generation correction method based on Tour, which can reflect individual travel characteristics, prior to building an activity-based model to predict demand due to the introduction of autonomous vehicles in terms of road operation, which is the ultimate goal of this study.

• Journal of Korean Society of Rural Planning
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• v.22 no.3
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• pp.97-106
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• 2016

The study attempts to estimate the recreational benefits of Chilgap multi-purpose reservoir using the on-site survey sample of 130 visitors. The individual travel cost method is used for measuring the recreational benefits of Chilgap multi-purpose reservoir and a zero-truncated negative binomial model is used to elicit the travel demand function. The price elasticities of visit demand are ranged from 0.29 to 0.39. Recreational benefits are ranged from 119 to 156 thousand won per visit and are ranged from 292 to 383 thousand won per annual. When the number of annual visitors to Chilgap reservoir is appled, then the recreational benefits are ranged from 2.7 to 3.6 billion won. This study could contribute to the advancement of post-construction evaluation in the public construction field similar to Chilgap reservoir.

• Journal of the Korean Regional Science Association
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• v.31 no.3
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• pp.39-54
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• 2015

To assess an economic value of Cheonggyecheon river restoration project, an in-depth exit survey data was collected to apply travel cost method in this study. Poisson model, Negative Binomial, Zero-truncated Poisson, and Zero-truncated Negative Binomial model were executed due to the nature of count data. Empirical results showed that regressors were statistically significant and corresponded to general consumer theory. Since our survey data showed over-dispersion, Zero-truncated Negative Binomial was selected as an optimal one to analyze travel demand of Cheonggyecheon by model goodness of fit test among those aforementioned empirical models. Estimating an economic value of Cheonggyecheon river restoration project, which is known as an ecological river restoration project, we used annual visit of individual traveler and an optimal model. Suffice to say that the annual economic value of Cheonggyecheon river restoration project was estimated as 193.4 billion won in 2013.

• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.117-125
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• 2022

Recently, as users' interest for travel increases, research on a travel route recommendation service that replaces the cumbersome task of planning a travel itinerary with automatic scheduling has been actively conducted. The most important and common goal of the itinerary recommendations is to provide the shortest route including popular tour spots near the travel destination. A number of existing studies focused on providing personalized travel schedules, where there was a problem that a survey was required when there were no travel route histories or SNS reviews of users. In addition, implementation issues that need to be considered when calculating the shortest path were not clearly pointed out. Regarding this, this paper presents a quantified method to find out popular tourist destinations using social big data, and discusses problems that may occur when applying the shortest path algorithm and a heuristic algorithm to solve it. To verify the proposed method, 63,000 places information was collected from the Gyeongnam province and big data analysis was performed for the places, and it was confirmed through experiments that the proposed heuristic scheduling algorithm can provide a timely response over the real data.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1799-1810
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• 2011

Origin-Destination(OD) trip survey being used in travel demand forecasting has been obtained through totalizing process with direct sample survey techniques such as plate license survey, roadside interview, household travel survey, and cordon line counts. However, the OD survey has many discrepancies in sampling, totalizing process, and such discrepancies contains problems of difference between forecasted traffic volume and observed data. On the other hand, transit smart card data recently collected has credible resource of obtaining travel information for bus and metro. This paper presents parameter estimation of gravity model by using transit smart card data. Through the parameter estimation method, we estimated =0.57, ${\beta}$=0.14 of gravity model for bus, and ${\alpha}$=-0.21, ${\beta}$=0.05 for metro. The statistical test such as T-test, coefficient of correlation, Theil`s inequality coefficient showed no difference between observed volume and estimated volume. Elasticities of bus and metro derived in this paper are also reasonable.

• Korean Journal of Agricultural Science
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• v.31 no.2
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• pp.123-134
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• 2004

Since 1989, 9 major complex have been invested with government funds, for the purpose of developing agricultural tourism to increase agricultural income and job opportunity, as well as providing urban population leisure opportunity. However, systematic and comprehensive approaches were rarely done in analyzing its economic impacts. This study, therefore, focuses on analyzing tourism effect and its economic value and implication for a representative rural tourism site,"Daeho rural tourism complex". To analyze travel pattern, expenditure pattern, and degree of satisfaction from travel to Daeho complex, Travel Cost Method(TCM) was employed based on survey method. Results from linear model with statistical significance implies that tourism benefit for each visitor is 28,373 won and total annual benefit for the Daeho site is 7 billion won. Considering annual benefit stream, the present value of total benefits are 132.9 billion won and 70 billion won at 5% and 10% of discounting rate, respectively. Using the values of benefit estimated from this study and investment cost, B/C ratio, IRR, and NPV were calculated to be 1.01, 1.67 billion won, and 5.19% at 5% of discounting rate. These results could be directly compared with the previous analyses.

• Journal of the Society of Disaster Information
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• v.20 no.2
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• pp.257-269
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• 2024

Purpose: The purpose of this study is to explain the pivotal role of the travel forecasting process in urban transportation planning. This study emphasizes the use of travel forecasting models to anticipate future traffic. Method: This study examines the methodology used in urban travel demand modeling within transportation planning, specifically focusing on the Urban Transportation Modeling System (UTMS). UTMS is designed to predict various aspects of urban transportation, including quantities, temporal patterns, origin-destination pairs, modal preferences, and optimal routes in metropolitan areas. By analyzing UTMS and its operational framework, this research aims to enhance an understanding of contemporary urban travel demand modeling practices and their implications for transportation planning and urban mobility management. Result: The result of this study provides a nuanced understanding of travel dynamics, emphasizing the influence of variables such as average income, household size, and vehicle ownership on travel patterns. Furthermore, the attraction model highlights specific areas of significance, elucidating the role of retail locations, non-retail areas, and other locales in shaping the observed dynamics of transportation. Conclusion: The study methodically addressed urban travel dynamics in a four-ward area, employing a comprehensive modeling approach involving trip generation, attraction, distribution, modal split, and assignment. The findings, such as the prevalence of motorbikes as the primary mode of transportation and the impact of adjusted traffic patterns on reduced travel times, offer valuable insights for urban planners and policymakers in optimizing transportation networks. These insights can inform strategic decisions to enhance efficiency and sustainability in urban mobility planning.

• Journal of the Korea Fashion and Costume Design Association
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• v.19 no.2
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• pp.117-128
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• 2017

This study confirmed the types of shopping information sources during travel abroad, and developed a profile of tourists in terms of demographics, travel, and shopping behavior. Shopping information sources and characteristics of shopping products were identified first. Thereafter, travelers were segmented by their information-seeking behavior. An online survey method was used to get data from Korean vacationers in their 20s-50s, while factor analysis, cluster analysis, ${\chi}^2$ test and ANOVA were applied to analyze data. The results were as follows. First, the shopping information sources of overseas tourists were composed of four factors including sources from travel agents/media, information from travel books and local sources, and word-of-mouth sources. Also, four factors in product types and four product attributes were identified. Second, tourists were clustered into two groups, active and passive shopping information seekers, based on shopping source behavior. Third, two groups differed in terms of demographics, showing an older age and higher income for active shopping source seekers. Active shopping information users tended to join package trips with family members, and they were more satisfied with the trip. With regard to shopping, active shopping source seekers spent more money for shopping and preferred all kinds of shopping goods with an emphasis on travel shopping. In conclusion, shopping information sources seemed to be a meaningful tool for segmenting tourists. Rich, older, family tourists would be an major target market for local retailers.