• Journal of Korean Society of Transportation
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• v.26 no.6
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• pp.7-15
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• 2008

Due to the limitations in the statistical data, the existing studies adopted rough methodologies with strong assumptions in the estimation of international passenger travel demand forecast in the ASEAN region. This study aims to develop international passenger travel demand models using scientific methodologies. This study proposes a direct demand model using the immigration and emigration data between countries in the region. This is because of the difficulty of estimating trip generation and trip distribution separately due to the data limitation in the region. As there does not exist the mode choice model for the region, this study estimates a mode choice model using the Stated Preference technique. The mode choice model is separated into three categories of models according to the range of distance between the origin and destination of travel; this is to reflect the different behavior in mode choice according to the travel distance. The result of model estimations suggests that the estimated models produce resonable results statistically. It is expected that the proposed models are useful for the future travel demand estimation in the ASEAN region.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.3
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• pp.316-323
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• 2010

We propose a method to find flows of transit users in the subway transportation network of the metropolitan Seoul and analyze the passenger flows on some central links of the network. The transportation network consists of vertices for subway stops, edges for links between two adjacent subway stops, and flows on the edges' Each subway transit user makes a passenger flow along edges of the shortest path from the origin stop to the destination stop in his trip. In this paper, we have developed a new algorithm to find the passenger flow of each link in the subway network from a large trip-transaction database of subway transit users. We have applied the algorithm to find the passenger flows from one day database of about 5 million transactions by the subway transit users. As results of the experiments, the travel behavior on 4 central subway links is analyzed in passenger flows and top 10 flows among all subway links are explained in a table.

• Journal of Information Technology Services
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• v.20 no.6
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• pp.83-95
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• 2021

In this study, various usage patterns were analyzed after establishing a data mart for micro mobility analysis based on the rental history of public bicycles in Seoul. Rental history data is origin-destination data that includes the rental location and time, and the return location and time. About 1500 rental locations were classified according to the characteristics of the location to create a 'station type' dimension. We also created a 'path type' dimension that displays whether the rental location and return location are the same. In addition, a derived variable called speed, which is obtained by dividing the distance used by the time used, is added, and through this, the characteristics of the riding area and the reason for the rental can be estimated. Meanwhile, administrative district link, administrative neighborhood link, and station type link were created to apply network analysis. Through this analysis, the roles and proportions of administrative districts, public facilities, and private facilities engaged in micro-mobility services were visualized. 49.9% of rentals occur at rental offices near transportation facilities, and half of them occur at rental offices near subway stations. The number of rentals during the evening rush hour is more than double that of the morning rush hour. When the path type is unidirectional, there is a fixed destination, so the distance and time used are short, and the movement speed tends to be high. In the case of round-trip, the purpose of use is exercise or leisure, so the distance and time used are long, and the movement speed is slow. It is expected that the results of the analysis can be used as reference materials for selecting new rental locations, providing convenient services for users, and developing user-specialized products.

• Journal of Korean Society of Transportation
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• v.36 no.3
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• pp.203-215
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• 2018

This study suggests a method to construct large scale dynamic O/D reflecting the characteristic that the passengers' travel patterns change according to the land use patterns of the destination. There are limitations in the existing research about dynamic O/D estimation method, such as the difficulty of collecting data, which can be applied only to a small area, or limiting to a specific transportation network such as highway networks or public transportation networks. In this paper, we propose a method to estimate dynamic O/D without limitation of analysis area based on transportation resources that can be easily collected and used according to the big data era. Clustering analysis was used to calculate the departure time trip distribution ratio based on arrival time and departure time trip distribution function was estimated by each cluster. As a result of the comparison test with the survey data, the estimated distribution function was statistically significant.

• KIISE Transactions on Computing Practices
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• v.20 no.12
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• pp.652-657
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• 2014

Given a starting point, destination point and various Points Of Interest (POIs), which contain a full or partial order, for a user to visit we wish to create, a sequenced route from the starting point to the destination point that includes one member of each POI type in a particular order. This paper proposes a method for finding the approximate shortest route between the start point, destination point and one member of each POI type. There are currently two algorithms that perform this task but they both have weaknesses. One of the algorithms only considers the distance between the visited POI (or starting point) and POI to visit next. The other algorithm chooses candidate points near the straight-line distance between the start point and destination but does not consider the order of visits on the corresponding network path. This paper outlines an algorithm that chooses the candidate points that are nearer to the network path between the start point and destination using network search. The algorithm looks for routes using the candidate points and finds the approximate shortest route by assigning an adaptive priority to the route that visits more POIs in a short amount of time.

• Journal of the Korean Regional Science Association
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• v.37 no.4
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• pp.75-93
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• 2021

By defining a travel disparity based on the degree to which travelers diversify their destinations, this paper examines how socioeconomic and spatial factors affect the travel disparity among the elderly in Seoul. This paper uses the COVID-19 pandemic as a natural experiment which can bring about different behavioral responses among the older travelers. Using the smart card data, we compare the destination diversification patterns before and after the pandemic. In the early morning(4:30-9:00), the degree of destination diversification varies between the core and the periphery and this trend persists through the pandemic. In the late morning(9:00-12:00), a new trend of disparity appeared after the pandemic. Although those who hold higher socioeconomic status and live closer to the core have a larger range of choices for destinations, the difference of range did not lead to differences in diversification before the pandemic, due to the discretionary nature of the elderly's trip. In contrast, as the elderly were forced to search alternative destinations right after the outbreak of the pandemic, the range of choices became an important factor causing observable differences in destination diversification. The findings suggest that the travel disparity observed during the pandemic is due to the difference in the range of choices by socioeconomic and spatial factors.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.563-570
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• 2011

This study analyze the transfer patterns of passengers in Seoul based on transit smart card data that was observed in 2010. The smart card records maximum four times of transfer and reports that approximately 90% of trips were less than one transfer and the remains were more than 2 transfers. We focus on trips with more than 3 transfers to figure out the relationship between transit service and regional connectivities. The results show that the average travel time, distance, fare are 45 minutes, 18.3km, and 1,119(KW) respectively. We develope a map for investigating transfer patterns at a regional level(dong and gu). By doing this, three types of transfers are observed as: 1) trips of which origin and destination is either same or near, 2) trips with short distance, and 3) long distance trip with low transit connectivities.

• Journal of Korean Society of Transportation
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• v.14 no.1
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• pp.43-50
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• 1996

This paper is concerned with assessing goodness of fit of gravity models. The Chi-square test, or one of its asymptotic equivalents, is usually recommended for the purpose. A difficulty that frequently arises, particularly when working with urban travel data, is that the expected number of trips for most origin-destination(O-D) pairs are small. In order to test goodness of fit of gravity model, a simple approach, which depends on the number of O-D pairs and certain trip totals being large, is proposed in this paper. In addition, derivation of variance of Chi-square ratio is proposed to test the confidence interval of Chi-square ratio and application of its results with simulated data set is made to verify the usefulness of the results.

• Proceedings of the KSR Conference
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• 2005.05a
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• pp.1073-1083
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• 2005

Since the operation of subway line 1 between Seoul and Cheongryangri stations in 1974, the rail length and riderships have been increased during the three decades. Furthermore, it was a remarkable increase of line length between 1995 and 2004 by the operation of Lines 5, 6, 7, and 8 within Seoul metropolitan area. This study reports the variations of riderships by the changing urban spatial structure. According to analysis results by station-to-station origin-destination trip tables, there were increasing ridership changes in new stations within new subcenters, while there were little ridership changes in old stations within Seoul and existing subcenters. The levels of competition between the existing and new lines brought about the difference of ridership changes.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.14 no.3
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• pp.193-200
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• 2021

Recently, posts and opinions on tourist attractions are actively shared on social media. These social big data provide meaningful information to identify objective images of tourist destinations recognized by consumers. Therefore, an in-depth understanding of the tourist image is possible by analyzing these big data on tourism. The study is to analyze destination images in Gangwon-do using big data from social media. It is wanted to understand destination images in Gangwon-do using semantic network analysis and then provided suggestions on how to enhance image to secure differentiated competitiveness as a destination for tourists. According to the frequency analysis results, as tourism in Gangwon-do, Sokcho, Gangneung, and Yangyang were mentioned at a high level in that order, and the purpose of travel was restaurant tour, gourmet food, family trip, vacation, and experience. In particular, it was found that they preferred day trips, weekends, and experiences. Four suggestions were made based on the results. First, it is necessary to develop various types of hotels, accommodation facilities and experience-oriented tour packages. Second, it is necessary to develop a day-to-day travel package that utilizes proximity to the Seoul metropolitan area. Third, it is necessary to promote traditional restaurants and local food. Finally, it is necessary to develop tourist package suitable for healing and family travel. Through this research, the destination image of Gangwon-do was identified and a tourism marketing strategy was presented to improve competitiveness. It also provided a theoretical basis for the use of the big data of tourism consumers in the field of tourism business.