• Environmental and Resource Economics Review
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• v.21 no.4
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• pp.981-998
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• 2012

In this paper, several count data model of travel cost recreation demand with Poisson and negative binominal specification are applied to estimate the value of access to the estuary area of Yeongsan river from visitor survey data. The results show that the negative binomial model that accounts for truncation and overdispersion provides the better goodness-of-fit, and therefore the value per visit(i.e. consumer surplus) is 89,350 won for resident of Jeolla province and 432,526 won for that of other provinces. If don't correct overdispersion by relying on Poisson estimates, the consumer surplus will be underestimated. Whereas the consumer surplus will be overestimated unless correct truncation by using estimates of untruncated models. As a result, the truncated negative binomial model should be applied to estimate the travel demand and the consumer surplus per visit by using survey data from single site visitors.

• Journal of Korean Society of Transportation
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• v.7 no.1
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• pp.35-42
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• 1989

The multinomial logit model has been applied for various choice problems. Among others, the joint destination mode choice, the mode choice and the route choice are the three major modeling topics for korean transportation planners. This paper examines with real world data (the Olympic road and its competing two major arterials) the usefulness of a Logit route choice model. Quites surpisingly, it is found that the multinomial route choice behavioral model calibrated for this study based on (0,1) individula data base can not provide a good estimate for O-D trips less than 6㎞. 400data points and 3case studies might not be sufficient for a sound conclusion. It is, however, believed from a series of similar studies conducted by the authors that the route choice behavior is more sensitive (more demand elastic with respect to travel time changes) than the mode choice and the shorter trip, the more sensitive. The travel time parameters for destination choice models are usually smalle than the travel time parameters for mode choice models and these parameters (for mode choice models) turn our smaller than the travel time parameters for route choice models from this study. Table 2 in this paper shows parameter changes for three different markets and Table 3 shows the modeling errors when the estimated individual probabilities are aggregated into a route level.

• Journal of Korean Institute of Industrial Engineers
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• v.18 no.2
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• pp.23-29
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• 1992

To compute the expected travel time of storage and retrieval (S/R) machine in automated warehousing systems most of the previous studies assumed that equal number of rack openings are required regardless of the nature of storage assignment rules. It is known that randomized storage assignment rule usually needs less storage to space than needed for full turnover-based assignment rule. The objective of this paper is compute the expected travel time of each assignment rule more equitably by taking into account the storage space required for each rule. First, the rack storage space is determined which satisfies a given service level. Then based on the standard Economic Ordering Quantity model, trade-off analysis is carried out which relates the storage space to the expected travel time of the S/R machine. Finally, example problems are solved to compare the performance of each assignment rule under varying conditions of demand pattern and service level.

• Journal of Korean Society of Transportation
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• v.25 no.4
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• pp.99-108
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• 2007

This paper presents an elastic demand stochastic user equilibrium traffic assignment that could not be easily tackled. The elastic demand coupled with a travel performance function is known to converge to a supply-demand equilibrium, where a stochastic user equilibrium (SUE) is obtained. SUE is the state in which all equivalent path costs are equal, and thus no user can reduce his perceived travel cost. The elastic demand SUE traffic assignment can be formulated based on a dynamic system, which is a means of describing how one state develops into another state over the course of time. Traditionally it has been used for control engineering, but it is also useful for transportation problems in that it can describe time-variant traffic movements. Through the Lyapunov Function Theorem, the author proves that the model has a stable solution and confirms it with a numerical example.

• Journal of Distribution Science
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• v.10 no.9
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• pp.5-11
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• 2012

Purpose - The objective of the paper is to explain both the price sensitivity of international tourists to South Korea and the price sensitivity of Korean tourists to international travel. The study examines long-run equilibrium relationships and Granger causal relationships between foreign exchange rates and inbound and outbound tourism demand in South Korea. Research design/ data / methodology - The study employs monthly time series data from January 1990 to September 2010. The paper examines the long-run equilibrium relationship using the Johansen cointegration test approach after unit root tests. The short-run Granger causality was tested using the vector error correction model with the Wald test. Results - Hypothesis 1 testing whether there is a long-run equilibrium relationship between exchange rates, inbound and outbound tourism demand is supported. Hypothesis 2 testing whether exchange rates lead to a change in touristarrivals and expenditure is not supported. Hypothesis 3 testing whether exchange rates lead to a change in tourist departures and expenditure is supported. Conclusions - The findings of this study show that the impacts of tourism price competitiveness are changing quite significantly with regard to destination competitiveness. In other words, the elasticity of tourism price over tourism demand has been moderated.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.12 no.3
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• pp.114-128
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• 2013

Greenhouse gas emissions have been an important issue in different countries because of their effects on global warming. The government has to organize greenhouse gas reduction measures suitable to regional characteristics by establishing annual implementation plans and comprehensive policies based on the UNFCCC. The transportation sector is one of the major contributors of air pollution; hence increasing need to estimate current and future traffic emissions precisely. Under these circumstances, a number of emission models have been developed recently. However, current methods of estimation cannot carry out effective analyses because it does not reflect vehicle movement characteristics. This study aims to present a new method for calculating road traffic emissions in Goyang city. A travel demand model is utilized to carry out GHG emission estimates according the traffic data (fleet composition, vehicle kilometers travelled, traffic intensity, road type, emission factors and speed). This study evaluates two approaches to estimate the road traffic emissions in Goyang City: Pollution-Emis and the Handbook of Emission Factors for Road Transport (HBEFA v.3.1) which is representative of the "average speed" and the "traffic situation" model types. The evaluation of results shows that the proposed emission estimation method may be a good practice if vigilant implementation of model inputs is observed.

• The Journal of Fisheries Business Administration
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• v.38 no.2
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• pp.79-101
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• 2007

The purpose of this study is to estimate the economic value of the Songieong Beach in Off-season, using a Individual Travel Cost Model(ITCM). Songieong Beach is located in Busan but far away from city. These days, however, the increased rate of traffic inflow to the Songieong beach and the five-day working week are reflected in the trend analysis. Moreover, people have changed psychological value. For that reason, visitors are on the increase on the beach in off-season. The ITCM is applied to estimate non-market value or environmental Good like a Contingent Valuation Method and Hedonic Price Model etc. The ITCM was derived from the Count Data Model(i.e. Poisson and Negative Binomial model). So this paper compares Poisson and negative binomial count data models to measure the tourism demands. The data for the study were collected from the Songjeong Beach on visitors over the a week from November 1 through November 23, 2006. Interviewers were instructed to interview only individuals. So the sample was taken in 113. A dependent variable that is defined on the non-negative integers and subject to sampling truncation is the result of a truncated count data process. This paper analyzes the effects of determinants on visitors' demand for exhibition using a class of maximum-likelihood regression estimators for count data from truncated samples, The count data and truncated models are used primarily to explain non-negative integer and truncation properties of tourist trips as suggested by the economic valuation literature. The results suggest that the truncated negative binomial model is improved overdispersion problem and more preferred than the other models in the study. This paper is not the same as the others. One thing is that Estimating Value of the Beach in off-season. The other thing is this study emphasizes in particular 'travel cost' that is not only monetary cost but also including opportunity cost of 'travel time'. According to the truncated negative binomial model, estimates the Consumer Surplus(CS) values per trip of about 199,754 Korean won and the total economic value was estimated to be 1,288,680 Korean won.

• The Journal of Bigdata
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• v.9 no.1
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• pp.31-47
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• 2024

With the advancement of artificial intelligence, the travel and hospitality industry is also adopting AI and machine learning technologies for various purposes. In the tourism industry, demand forecasting is recognized as a very important factor, as it directly impacts service efficiency and revenue maximization. Demand forecasting requires the consideration of time-varying data flows, which is why statistical techniques and machine learning models are used. In recent years, variations and integration of existing models have been studied to account for the diversity of demand forecasting data and the complexity of the natural world, which have been reported to improve forecasting performance concerning uncertainty and variability. This study also proposes a new model that integrates various machine-learning approaches to improve the accuracy of hotel sales demand forecasting. Specifically, this study proposes a new time series forecasting model based on XGBoost that selectively utilizes a local model by clustering with DTW K-means and a global model using the entire data to improve forecasting performance. The hotel demand forecasting model that selectively utilizes global and regional models proposed in this study is expected to impact the growth of the hotel and travel industry positively and can be applied to forecasting in other business fields in the future.

• Journal of the Korean Society for Railway
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• v.12 no.2
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• pp.302-308
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• 2009

This paper presents a comprehensive analysis of intercity rail passengers' and travel patterns based on the 2001 Seoul-Busan rail passengers' Travel Survey. Results representing personal characteristics such as age and income seem to affect on destination the income was not seen to be a critical effect on destination choice. The variables such as travel time, transfer status, and date for travel seem to be and recreation activity. However, the destination choice would be relationship between Seoul and all four destination cities. The insights gained of an activity-based rail travel demand model.

• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.145-153
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• 2008

This study proposes a facility location model in consideration of spatial coverage and travel cost as an effort to make objective and effective decisions of natural gas filling stations. The proposed model is developed for fixed stations and consists of two stages. The first stage employs a heuristic algorithm to find a set of locations which satisfy the spatial coverage constraints determined by the maximum travel distance between the filling stations and bus depots. In the second stage, the optimal location of filling stations is determined based on the minimum travel cost estimated by using a modified transportation problem as well as the construction and maintenance costs of the filling stations. The applicability of the model is analyzed through finding the optimal location of filling stations for the city of Anyang, a typical medium-sized city in metropolitan Seoul, based on the demand of natural gas buses. This study is expected to help promote the spread of natural gas buses by providing a starting point of a objective and reasonable methodological perspective to address the filling station location problem.