• Journal of Smart Tourism
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• v.2 no.3
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• pp.31-38
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• 2022

Understanding demand for travel websites can help hoteliers better evaluate their own multi-channel distribution and reputation management strategies. In this study, we estimated demand for eight major travel websites based on the user percentage in 3,120 U.S. counties. Results highlighted the importance of four types of factors: sociodemographic, economic, Internet use-related, and travel-related. Differences between websites were also compared based on estimated coefficients. For example, the demand for Expedia.com appeared to be driven by age, education background, income, and foreign travel history, whereas the demand for Hotels.com was driven by the proportion of the African American population and domestic travel history. Lastly, implications are provided.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.167-176
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• 2009

Several travel demand management schemes have been used for controlling overloaded traffics on urban area. To maximize efficiency of the travel management, traffic manager has to set target level that we try to arrive in advance, and then to find optimal variable to attain this goal. In this regard, this paper presents two travel demand management models, expressed by mathematical program, and also presents their solution algorithms. The first is to find optimal travel demand for origin-destination (OD) pair, based on average travel time between the OD pair, and the second is based on the ratio of volume over capacity on congested area. An example is given to test the models.

• Korean Management Science Review
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• v.25 no.1
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• pp.93-106
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• 2008

This study is focused on the problem of seat allocation for group travel demand in airlines. We first explain the characteristic of group demand and its seat allocation process. The group demand in air travel markets can be classified into two types : incentive and package groups. Allocating seats for group demand depends on the types of group demand and the relationship between airlines and travel agents. In this paper we concentrate on the package group demand and develop an optimization model for seat allocation on the demand to maximize the total revenue. With some assumptions on the demand distribution and the linear approximation technique, we develop a mixed IP model for solving our problem optimally. From the computational experiments, we can find our optimization model can be applied well for real-world application.

• Journal of the Korean Operations Research and Management Science Society
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• v.34 no.1
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• pp.61-83
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• 2009

This study developed a variable demand traffic assignment model by stable dynamics. Stable dynamics, suggested by Nesterov and do Palma[19], is a new model which describes and provides a stable state of congestion in urban transportation networks. In comparison with the user equilibrium model, which is based on the arc travel time function in analyzing transportation networks, stable dynamics requires few parameters and is coincident with intuitions and observations on congestion. It is therefore expected to be a useful analysis tool for transportation planners. In this study, we generalize the stable dynamics into the model with variable demands. We suggest a three stage optimization model. In the first stage, we introduce critical travel times and dummy links and determine variable demands and link flows by applying an optimization problem to an extended network with the dummy links. Then we determine link travel times and path flows in the following stages. We present a numerical example of the application of the model to a given network.

• Journal of Korean Port Research
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• v.8 no.2
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• pp.79-105
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• 1994

Today travel demand in the City of Pusan is increasing with the wide scope of activity and the better living circumstances as a big city with a population of about 4 million. Also, the transportation problem of Pusan city becomes one of the severest urban problems with travel demand increasing in spite of the continuous expansion of the transportation facilities. The purpose in this study is to find the travel characteristics on the arterial under the study, construct the optimal Transportation System Management(TSM) which could increase the travel capacity of the arterial based upon the optimal travel systems constructed. Thus, it was concluded that more emphasis should be of the automobiles into the Downtown areas, increase the travel capacity of the arterial through Mass Transportation System(MTS) including the HOV lanes or the Bus Exclusive Lanes instead.

• Journal of Korean Society of Transportation
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• v.26 no.5
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• pp.163-174
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• 2008

The conventional four-step travel demand model is still widely used as the state-of-practice in most transportation planning agencies even though it does not provide reliable estimates of travel demand. In order to improve the accuracy of travel demand estimation, implementing an alternative approach would be critical as much as acquiring reliable socioeconomic and travel data. Recently, the role of travel demand model is diverse to satisfy the needs of microscopic analysis regarding various policies of travel demand management and traffic operations. In this context, the activity-based approach for travel demand estimation is introduced and a case study of developing a spatial-temporal activity presence-based approach that estimates travel demand through forecasting number of people present at certain place and time is accomplished. Results show that the spatial-temporal activity presence-based approach provides reliable estimates of both number of people present and trips actually people made. It is expected that the proposed approach will provide better estimates and be used in not only long-term transport plans but short-term transport impact studies with respect to various transport policies. Finally, in order to introduce the spatial-temporal activity presence-based approach, the data such as activity-based travel diary and land use based on geographic information system are essential.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1989.10a
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• pp.5-5
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• 1989

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.11a
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• pp.239-242
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• 2003

The supply chain of the airline industry includes multiple airline companies, travel agencies and customers. Various air fares are offered by travel agencies. The travel agencies grant deposit to the airline company in advance and preoccupy seats with deep discount, which called group-discount-ticket. The group-discount-ticket offers the lowest air fare but it does not base on real demand. So the information flow generated by group-discount-ticket can cause the information distortion in airline supply chain. This study analyzes the bullwhip effect in the airline industry. A mathematical model is formulated and managerial suggestions is provided.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.1
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• pp.61-73
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• 2006

This paper deals with traveling salesman problem(TSP) with the stochastic travel time. Practically, the travel time between demand points changes according to day and time zone because of traffic interference and jam. Since the almost pervious studies focus on TSP with the deterministic travel time, it is difficult to apply those results to logistics problem directly. But many logistics problems are strongly related with stochastic situation such as stochastic travel time. We need to develop the efficient solution method for the TSP with stochastic travel time. From the previous researches, we know that Q-learning technique gives us to deal with stochastic environment and neural network also enables us to calculate the Q-value of Q-learning algorithm. In this paper, we suggest an algorithm for TSP with the stochastic travel time integrating Q-learning and neural network. And we evaluate the validity of the algorithm through computational experiments. From the simulation results, we conclude that a new route obtained from the suggested algorithm gives relatively more reliable travel time in the logistics situation with stochastic travel time.

• Korean Management Science Review
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• v.17 no.2
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• pp.87-95
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• 2000

This paper presents two analytical approaches to determine the vehicle fleet size for container shuttle service. The shuttle service can be defined as the repetitive travel between the designated places during working period. In the first approach, the transportation model is adopted in order to determine the number of vehicles required. Its advantages and disadvantages in practical application are also discussed. In the second approach, a logical network which is oriented on job is transformed from a physical network which is focused on demand site. Nodes on the logical network represent jobs which include loaded travel, loading and unloading and arcs represent empty travel for the next jobs which include loaded travel, loading and unloading and arcs represent empty travel for the next job. Then a mathematical formulation is constructed similar to the multiple traveling salesman problem (TSP). A solution procedure is carried out based on the well-known insertion heuristic with the real world data.