• Journal of Information Technology Applications and Management
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• v.20 no.1
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• pp.87-106
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• 2013

This study is to explore the airline network patterns of Incheon and Narita International Airports using passenger flight departure and arrival data of the two airports. The so-called Origin-Destination data is collected from the airports' websites and some of the important data items are flight number, city of origin, destination city, departure/arrival time, number of flights, and delay time. A snowflake schema dimensional model is proposed and implemented. Tableau Public, a well-known visual analytic tool, is used to connect the dimensional model and played an important role in navigating the data space to find interesting and visual patterns among corresponding airports and airlines. For the efficiency of analyzing this spacious data mart, data visualization method was used. Four types of visualization method proposed by Yau was used; visualizing patterns over time, visualizing proportions, visualizing relationships, and visualizing spatial relationships. The strength of connectivity of each flight segments is calculated to evaluate the degree of globalization of Seoul and Tokyo. We anticipate that various patterns and new findings produced by the data mart would provide airline managers, airport authorities, and policy makers in the field of travel and transportation with insightful information.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.755-761
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• 2006

Though freight origin and destination data is essential for analysing transport investment and planning logistics facilities, the study on the establishment of the freight origin and destination data is very rare. The purpose of this study is to introduce a method on weight and expansion of sample freight data focusing on the Seoul metropolitan area. In particular, this study suggests the weight and expansion method which consider truck and commodity tonnage together. This paper also discuss the origin and destination trips in Seoul metropolitan area. This paper will contribute to establish more reliable freight origin and destination data.

• Journal of Korean Society of Transportation
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• v.14 no.3
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• pp.7-26
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• 1996

The estimation of real-time Origin-Destination(O-D) parameters, which gives travel demand between combinations of origin and destination points on a urban freeway network, from on-line surveillance traffic data is essential in developing an efficient ATMS strategy. On this need a real-time O-D parameter estimation model is formulated as a parameter adaptive filtering model based on the extended Kalman Filter. A Monte Carlo test have shown that the estimation of time-varying O-D parameter is possible using only traffic counts. Tests with field data produced the interesting finding that off-ramp volume predictions generated using a constant freeway O-D matrix was replaced by real-time estimates generated using the parameter adaptive filter.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.4
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• pp.81-99
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• 2020

Origin-destination data have been collected and utilized for demand analysis and service design in various fields such as public transportation and traffic operation. As the utilization of big data becomes important, there are increasing needs to store raw origin-destination data for big data analysis. However, it is not practical to store and analyze the raw data for a long period of time since the size of the data increases by the power of the number of the collection points. To overcome this storage limitation and long-period pattern analysis, this study proposes a methodology for compression and origin-destination data analysis with the compressed data. The proposed methodology is applied to public transit data of Sejong and Seoul. We first measure the reconstruction error and the data size for each truncated matrix. Then, to determine a range of principal components for removing random data, we measure the level of the regularity based on covariance coefficients of the demand data reconstructed with each range of principal components. Based on the distribution of the covariance coefficients, we found the range of principal components that covers the regular demand. The ranges are determined as 1~60 and 1~80 for Sejong and Seoul respectively.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.17 no.6
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• pp.14-24
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• 2018

Pedestrian movement of passengers using smart card within stations can be divided into three types of activities - straight ride and alight, line transfer, and station transfer. Straight ride and alight is transfer activity for which the card terminal and embarking line are identical. In this case, straight ride occurs at the origin station and straight alight occurs at the destination station. Line transfer refers to activity in which the subway line embarked on by the passenger is different from that which is disembarked. Succinctly, line transfer is transfer at a middle station, rather than at origin or destination stations. Station transfer occurs when the card terminal line and embarking line are different. It appears when station transfer happens at the origin station as starting transfer, and at the destination station as destination transfer. In the case of Metropolitan smart card data, origin and destination station card terminal line number data is recorded, but subway line data does not exist. Consequently, transportation card data, as it exists, cannot adequately be used to analyze pedestrian movement as a whole in subway stations. This research uses the smart card data, with its constraints, to propose an analysis model for passenger pedestrian movement within subway stations. To achieve this, a path selection model is constructed, which links origin and destination stations, and then applied for analysis. Finally, a case study of the metropolitan subway is undertaken and pedestrian volume analyzed.

• International Commerce and Information Review
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• v.18 no.3
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• pp.47-72
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• 2016

The Government of Korea has always focused on developing and maintaining a surplus on the balance of payments as a successful trade policy. The focus should now be on spatial information hiding, revealing patterns in trade activities that enable viewing trade in a more sophisticated manner. This study utilizes trade statistical data such as the United States-South Korea imports and exports from 2003 to 2015 officially released by the two countries. It allows us to analyze and extract the spatial information pertaining to the origin, transit, and destination. First, in the case of export data to the United States, the origin of the trade goods has expanded and decentralized from the metropolitan area. With regard to transit, in 2003, most of the exported goods were shipped by ocean vessels and arrived at the ports on the western coast of the United States. However, trade patterns have changed over the 12-year period and now more of that trade has moved to the southern ports of the United States. In terms of destination, California and Texas were importing goods from South Korea. With the development of the automotive industry in Georgia and Alabama, these two states also imported huge volumes of automobile parts. Second, in case of import data, most imported goods from the United States originated from California and Texas. In this case, 40% of goods were shipped by air freight and arrived at the Incheon-Seoul International Airport; most ocean freight was handled at the Port of Busan. The purpose of this study is to decompose the spatial information from the trade statistics data between Korea and the United States and to depict visualized bilateral trade structure by origin, transit, and destination.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.219-232
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• 2023

Mobility services need to change according to the regional characteristics of the target service area. Accordingly, analysis of mobility patterns and characteristics based on Origin-Destination (OD) data that reflect travel behaviors in the target service area is required. However, since conventional methods construct the OD data obtained from the administrative district-based zone system, it is hard to ensure spatial homogeneity. Hence, there are limitations in analyzing the inherent travel patterns of each mobility service, particularly for new mobility service like Demand Responsive Transit (DRT). Unlike the conventional approach, this study applies a data-driven clustering technique to conduct spatial analyses on OD travel patterns of regional mobility services based on reconstructed OD data derived from re-aggregation for original OD distributions. Based on the reconstructed OD data that contains information on the inherent feature vectors of the original OD data, the proposed method enables analysis of the spatial characteristics of regional mobility services, including public transit bus, taxi and DRT.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.4
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• pp.1559-1569
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• 2013

this study is to deduct the difference between regional and urban commercial trips by analyzing the characteristics of the regional and urban truck movements. To achieve this, we investigated the relation between the number of truck trips and various truck generation attributes such as truck attributes, origin and destination attributes, and commodity type using ordered logit models, which are separately estimated for regional and urban truck movements using truck diary data of Korea Transport Database (KTDB). According to the estimation results, regional and urban truck movements have different characteristics in truck attributes, origin and destination attributes and commodity type. Especially, the number of regional trucks trips increased as origin and destination are manufactural area and as the total value of products of industrial area in origin and destination increase.

• Journal of the Korea Society of Computer and Information
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• v.23 no.8
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• pp.95-99
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• 2018

This research is related to GPS(global positioning system) enabled device navigation service and consists of two parts. The first is the logic that records the route guidance video and records GPS information in time, and the second is the logic that outputs the created video data based on real time GPS. The recording logic first determines the origin and destination, records the video from the origin to the destination and it adjusts the speed of the image in a specific area so that the user can see it easily. And insert ancillary information and advertisements that can help guide the route. In the output logic, we provide navigation services using the video and GPS data tables we created, and it receives user's GPS information in real time and corrects it based on the recent user location to reduce errors. This provides local guidance services to people who lack language skills like foreigners.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.2
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• pp.119-131
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• 2024

Understanding accurate traffic performance is crucial for ensuring efficient highway operation and providing a sustainable mobility environment. On the other hand, an immediate and precise estimation of highway traffic performance faces challenges because of infrastructure and technological constraints, data processing complexities, and limitations in using integrated big data. This paper introduces a framework for estimating traffic performance by analyzing real-time data sourced from toll collection systems and dedicated short-range communications used on highways. In particular, this study addresses the data errors arising from segmented information in data, influencing the individual travel trajectories of vehicles and establishing a more reliable Origin-Destination (OD) framework. The study revealed the necessity of trip linkage for accurate estimations when consecutive segments of individual vehicle travel within the OD occur within a 20-minute window. By linking these trip ODs, the daily average highway traffic performance for South Korea was estimated to be248,624 thousand vehicle kilometers per day. This value shows an increase of approximately 458 thousand vehicle kilometers per day compared to the 248,166 thousand vehicle kilometers per day reported in the highway operations manual. This outcome highlights the potential for supplementing previously omitted traffic performance data through the methodology proposed in this study.