• Journal of Platform Technology
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• v.11 no.6
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• pp.21-27
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• 2023

In this paper, we introduce a reinforcement learning-based algorithm for personalized tourist path recommendations. The algorithm employs a reinforcement learning agent to explore tourist regions and identify optimal paths that are expected to enhance tourism experiences. The concept of tourism experience is defined through points of interest (POI) located along tourist paths within the tourist area. These metrics are quantified through aggregated evaluation scores derived from reviews submitted by past visitors. In the experimental setup, the foundational learning model used to find tour paths is the Deep Q-Network (DQN). Despite the limited availability of historical tourist behavior data, the agent adeptly learns travel paths by incorporating preference scores of tourist POIs and spatial information of the travel area.

• Journal of Korea Entertainment Industry Association
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• v.13 no.2
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• pp.47-56
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• 2019

This study conducted in-depth interviews with employees who worked at three different travel agencies in Seoul and had different employment years. In order to resolve emotional labor, we investigated and analyzed the positive effects of forest healing programs and tried to help both travel agents and forest healing operators. According to the analysis, the person who was selected for the H travel agency was interviewed at the forest healing experience center in Gapyeong in April 2018 and her job as an operator for the fifth year since she joined the company. He said that he suffered from depression due to emotional labor in the customer service process and that it has a positive effect through forest healing. Next, M travel agents conducted an interview in May 2018 at the forest experience center in Uiwang, and their job as a woman was product planning. In the case of emotional labor and physical illness in the work with customers, participants were interested in forest stability, people's humanity, and wooden crafts in particular. Lastly, participants of R travel agency conducted an interview at the forest experience center in Seoul in June 2018 and it was their 10th year as a man and their job as a tour contact. In order to solve the difficulties, I experienced forest experience in close geographical areas and received great help.

• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.199-209
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• 2008

This paper is described the concepts and limitations for the traditional directional design hour volume estimation. The main objective of this paper is to establish an estimation method of probabilistic directional design hour volume in order to improve the limitation for the traditional approach method. To express the traffic congestion of specific road segment, this paper proposed the link travel time as the probability that the road capacity can accommodate a certain traffic demand at desired service level. Also, the link travel time threshold was derived from chance-constrained stochastic model. Such successive probabilistic process could determine optimal ranked design hour volume and directional design hour volume. Therefore, the probabilistic directional design hour volume can consider the traffic congestion and economic aspect in road planning and design stage. It is hoped that this study will provide a better understanding of various issues involved in the short term prediction of directional design hourly volume on different types of roads.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.12
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• pp.537-546
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• 2014

The purpose of this study is to develope an algorithm which generates trip plans based on rating points of travel app users and travel experts to help potential travellers experience their desired destinations in advance. This algorithm uses the above rating points and the gradually created hierarchy to generate the most preferred and efficient trip courses. Users can go through video clips or panoramic VR videos of the actual destinations from their trip plans generated by the algorithm which may add excitement to their actual trips. With our heuristic methods, the more users input their ratings, the better trip plans can be generated. This algorithm has been tested on android OS and proven efficient in generating trip plans. This research introduces a way to experience travel destinations with panoramic VR video and proposes the algorithm which generates trip plans based on users' ratings. It is expected to be useful for travellers' trip planning and to contribute growth in the travel market.

• Journal of Korean Society of Transportation
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• v.22 no.4 s.75
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• pp.117-134
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• 2004

Truck traffic accounts for a substantial fraction of the traffic stream in many regions and is often the source of localized traffic congestion, potential parking and safety problems. Truck trips tend to be ignored or treated superficially in travel demand models. It reduces the effectiveness and accuracy of travel demand forecasting and may result in misguided transportation policy and project decisions. This paper presents the development of speed-flow relationships with truck impacts based on CORSIM simulation results in order to enhance travel demand model by incorporating truck trips. The traditional BPR(Bureau of Public Road) function representing the speed-flow relationships for roadway facilities is modified to specifically include the impacts of truck traffics. A number of new speed-flow functions have been developed based on CORSIM simulation results for freeways and urban arterials.

• Journal of Korean Society of Transportation
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• v.26 no.2
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• pp.147-156
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• 2008

It has been widely known that there are so many factors making travel demand errors in transportation forecasting steps. One of the reasons may stem from the level of aggregation of zone and network in analysis process. This paper investigates the effect of level of aggregation considering with number of zones in travel demand forecasting by expanding or reducing the zone and network gradually. Numerical results show that the aggregation could not make a significant impact on the travel demand, while disaggregation does. These results imply that a careful manipulation is required to add or to reduce zones and links in transportation planning process.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.2166-2178
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• 2010

The Korea Transportation Database (KTDB) is used to obtain data on the origin and destination (OD) of inter-city travel, which are currently used in railroad planning when estimating traffic demand. The KTDB employs the trip assignment method, whereby the total traffic volume researched for inter-city travel in Korea is divided into road, rail and air traffic, etc. However, as regards rail travel, the railroad stations are not identical to the existing zones or the connector has not been established because there are several stations in one zone as such, certain problems with the applicable methods have been identified. Therefore, estimates of the volume of railroad traffic using the KTDB display low reliability compared to other modes of transportation. In this study, these problems are reviewed and analyzed, and use of the aggregate model method to estimate the direct demand for rail travel is proposed in order to improve the reliability of estimation. In addition, a method of minimizing error in traffic demand estimation for the railroad field is proposed via an analysis of the relationship between the aggregate model and various social-economic indicators including population, distances, numbers of industrial employees, numbers of automobiles, and the extension of roads between cities.

• Journal of Korean Society of Transportation
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• v.33 no.2
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• pp.170-181
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• 2015

This study explores passengers' travel characteristics at six bus stops on the Seoul Ring Expressway using smart traffic card data. Based on the characteristics, political strategies to improve bus facilities on expressway are suggested. Firstly, among them two bus stops of Guri and Uiwang-Cheonggye have higher transfer rate of passengers, 37.56% and 36.9% of the total intercity (red) bus passengers on the expressway, respectively. Secondly, Uiwang-Cheonggye bus stop has the highest transfer rate, and Guri bus stop has also higher transfer rate and the highest on-and-off passengers. It implies that both bus stops need to be prioritized for improving bus stop facility, access roads, and connection facility between the bi-directional stops. Thirdly, Guri (down) bus stop has relatively longer waiting time for transfer, thus shorter bus headways running the bus stop should be considered. Lastly, most passengers using both stops come to and from Bundang-gu in Geongnam City. Overall, the results of this study would be helpful for transport planners to develop effective bus route policies and bus operation.

• Journal of Korean Society of Transportation
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• v.30 no.1
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• pp.19-30
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• 2012

The majority of daily travel demands concentrate at particular time-periods, which causes the difficulties in the travel demand analysis and the corresponding benefit estimation. Thus, it is necessary to consider time-specific traffic characteristics to yield more reliable results. Traditionally, na$\ddot{i}$ve, heuristic, and statistical approaches have been applied to address the peak-hour ratio. In this study, a hybrid clustering model which is one of the statistical methods is applied to calculate the peak-hour ratio and its duration. The 2009 national 24-hour traffic data provided by the Korea institute of Construction Technology are used. The analysis is conducted dividing vehicle types into passenger cars and trucks. For the verification for the usefulness of the methodology, the toll collection system data by the Korea Express Corporation are collected. The result of the research shows lower errors during the off-peak hours and night times and increasing error ratios as the travel distance increases. Since the method proposed can reduce the arbitrariness of analysts and can accommodate the statistical significance test, the model could be considered as a more robust and stable methodology. It is hoped that the result of this paper could contribute to the enhancement of the reliability for the travel demand analysis.

• Journal of Korean Society of Transportation
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• v.25 no.1 s.94
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• pp.109-119
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• 2007

Increasingly, the emphasis in regional Passenger rail Planning is finding ways to more efficiently use existing facilities, with particular attention being Paid to Policies designed to spread Peak-Period travel demand more evenly throughout the week with consideration of train classification. In this context the individual's choice of time to travel is of crucial significance. This paper investigates the use of multinomial logit analysis to model ridership by rail classification using data collected for travel from Seoul to Busan during the one week in October 2004. The Particular model form that was successfully calibrated was the multinomial logit (MNL) model : it describes the choice mechanism that will Permit rail systems and operations to be planned on a more reliable basis. The assumption of independently and identically distributed(IID) error terms in the MNL model leads to its infamous independence from irrelevant alternatives (IIA) property. Relaxation of the IID assumption has been undertaken along a number or isolated dimensions leading to the development of the MNL model. For business and related rail travel patterns, the most important variables of choice were time and frequency to the chosen destination. The calibrated model showed high agreement between observed and Predicted market shares. The model is expected to be of use to railroad authorities in Planning and determining business strategies in the Increasingly competitive environment or regional rail transport.