• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.15 no.4
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• pp.44-53
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• 2016

The era of Big Data has come and the importance of Big Data has been rapidly growing. The part of transportation, the Four-Step Travel Demand Model(FSTDM), a traditional Trip-Based Model(TBM) reaches its limit. In recent years, a traffic demand forecasting method using the Activity-Based Model(ABM) emerged as a new paradigm. Given that transportation means the spatial movement of people and goods in a certain period of time, transportation could be very closely associated with spatial data. So, I mined Spatial Big Data from SNS. After that, I analyzed the character of these data from SNS and test the reliability of the data through compared with the attributes of TBM. Finally, I built a database from SNS for the operation of ABM and manipulate an ABM simulator, then I consider the result. Through this research, I was successfully able to create a spatial database from SNS and I found possibilities to overcome technical limitations on using Spatial Big Data in the transportation planning process. Moreover, it was an opportunity to seek ways of further research development.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.4
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• pp.687-698
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• 1997

In this paper, a Variation of Blast with Go-Back-N(V-BGBN) protocol is proposed, which differs from Blast with Go-Back-N(BGBN) and Blast with Full Retransmission on Error(BFRE) protocols in the retransmission strategy of packets. Performances of these three protocols under correlated packet errors are analyzed. Throughput efficiency of an arbitrary packet is obtained under the assumption that the round trip delay and the packet length are respectively constant. Recursive formula and difference equations are used as analytical tools. Correlation of packet errors is modelled by a two state Markov chain. The throughput efficiencies under these protocols are compared. V-BGBN protocol is shown to be superior to other two protocols in high speed network.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.6
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• pp.11-30
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• 2019

Smartcards are used as the basic data for utilizing the various transportation policies and evaluations, etc. and provided the transportation basic statistics index. However, the main problem of the smartcard data is that the most of users do not take the alighting tag at the stop, so there is a limit to the scope of use for the total O-D trip data because incomplete O-D traffic data of transportation card users. In this study, a decision tree of learning model is estimated for the alighting stop of smartcard users. The model estimation accuracy in range less than 2 stops interval was 89.7% on average. By eliminating the incompleteness alighting stop of smartcard data through this model, it is expected to be used as the basic data for various transportation analyses and evaluations.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.2
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• pp.97-103
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• 2009

Improving TCP performance has long been the focus of many research efforts in 802.11 wireless networks study. Hop count and Round Trip Time (RTT) are the critical sources which serious affect the TCP performance on end to end connection. In this paper, we analytical derived the affection and based on the analysis we propose TCP should Change its Expected Value (TCP-CEV) when hop count and RTT change by setting a reasonable CWND change rate to improve the performance. The proposed scheme is applicable to a wide range of transport protocols using the basic TCP mechanism, and the protocol behavior is analytically tractable. We show that our simple strategy improves TCP performance at least over 12% in a chain topology, 4.9% in a grid topology and improve the TCP convergence.

• Journal of the Korean Society for Railway
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• v.20 no.5
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• pp.692-702
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• 2017

Recently, there has been a growing interest in using smart card data. However, there are restrictions on the utilization of data in many areas outside the Seoul metropolitan area because the data does not contain alighting information. This paper presents a methodology for estimating alighting stops of smart card data. Estimation results were verified by smart card data from Seoul and Gwangju. The estimation rates were 78.2% and 81.6% in Seoul and Gwangju, respectively. The matching accuracy was 54.2% and 33.4%, respectively. However, if up to two stops of error are allowed, the accuracy values were 93.6% and 94.0%, respectively. We also discussed changes in estimation results due to adjusting the allowable walking distance, which is a key parameter of trip chaining methods. As the allowable walking distance increases, the estimation rate increases, while the accuracy decreases, and it is found that the estimation results change by around 500m.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.141-156
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• 2023

This study aims to improve understanding of freight movement, crucial for logistics facility investment and policy making. It addresses the limitations of traditional freight truck traffic data, aggregated only at city and county levels, by developing a new methodology. This method uses trip chain data for more detailed, facility-level analysis of freight truck movements. It employs DTG (Digital Tachograph) data to identify individual truck visit locations and creates H3 system-based polygons to represent these visits spatially. The study also involves an algorithm to dynamically determine the optimal spatial resolution of these polygons. Tested nationally, the approach resulted in polygons with 81.26% spatial fit and 14.8% error rate, offering insights into freight characteristics and enabling clustering based on traffic chain characteristics of freight trucks and visited facility types.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.5
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• pp.12-28
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• 2017

The core of the transportation service, so-called Mobility 4.0 is the flexibility of the entire mobility and its implementation. By doing so, the most essential element is to build a platform to link a supply and a demand simultaneously. In other word, a comprehensive analytical framework is to be set with a data repository which can be periodically updated. With such circumstances, the entire trip chain including pedestrian movements is required to be thoroughly investigated and constructed at the viewpoint of the intermodal transit station. A few studies, however, have been attempted. In this study, the comprehensive analytical framework with the integrated mobility at intermodal transit station was proposed, which consisted of the three modules; 1) Data Repository Extracting from Smart Card DB, 2) Framework of Analyzing Integrated Mobility, and 3) Interpretation of the Integrated Mobility with GIS information and the other factors. A case study with the seven railway stations (Sadang, Sindorom, Samseong, Gwanghwanoon, Gangnam, Jamsil, Seoul Nat'l Univ. of Education) was conducted. The stations of the case study were clustered by the three groups with the statistical ground, and it is most likely to understand the effect of a variety of factors and a comprehensive data-driven analyses with the entire trip stages.

• Journal of Korean Society of Transportation
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• v.31 no.2
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• pp.45-59
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• 2013

This study deals with the comparative analysis between two freight demand estimation methods : Unimodal O/D based method and P/C based method. The data of access/egress truck trips has been omitted from the Korean freight unimodal O/D of KTDB. This is because KTDB's unimodal O/D has not marked the series of unlinked trips down as the whole freight intermodal transport and surveyed only the main-haul trips of them. For these reasons, freight intermodal transport mechanism has not been analysed perfectly with Korean unimodal O/D data. This study tries to estimate P/C table of Korean Import/Export container freight and develop the MCC(Multimodal Channel Choice) model. Then, comparing unimodal O/D based method and P/C based method in terms of the switch commodities between production point(the initial point of freight transport) and consumption point(the terminal point of freight transport), unimodal commodities, and commodities on links is conducted. The results show that the P/C based method is able to simulate the freight intermodal transport.

• Journal of Korean Society of Transportation
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• v.29 no.4
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• pp.29-41
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• 2011

This study investigates the factor that influences commuting time to work when individuals allocate their time for different types of activities. The commuting time is an important indicator for an individual to determine the residence and choose the means of transportation. The analysis uses the data collected from people who live in Seoul metropolitan area including Seoul, Incheon and Gyeonggi Province, and commute to work and making the simple linkage travel (home-work-home) within the area. For the analysis, the Cox hazard proportional methodology was adopted. The method is known to be well applied without assuming any distribution in case of the dependent variable being continuous. For the covariate, the interaction effect between the space variable of the work place and the variable of transportation has been also included in the model. The commuting time to work has been estimated for both 1) the whole metropolitan area and 2) the separate regions i.e., Seoul, Incheon and Gyeonggi-Do. The result reveals that characteristic variables related to individual, household and travel properties influence the mode of transportation and the time allocated for commuting to work (p<0.01). This study also demonstrates the usefulness of the Cox hazard proportional model. The data used in this study is the actual household travel data surveyed in 2006 in the metropolitan area, and analyzing the survey data in 2010 is currently in progress. Comparison of the two survey data sets seeking any behavioral change is suggested for the future study.

• Journal of Korean Society of Transportation
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• v.32 no.5
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• pp.544-553
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• 2014

Activity-based models analyze individuals' various daily activities that are identified as a decision-making unit for transportation planning. In other words, it is the model that determines the types of activities according to the social, economic and situational characteristics of the groups with the same activity patterns and predicts individuals' activity time, distance, spatial movement and transportation mode. The activity-based model is a method of estimating more efficient and realistic demand in transportation forecasting because traffic is regarded as a complex decision-making process that an individual and other people participate in. In this paper, we grasp the factors affecting choice behavior of activity pattern and analyze choice behavior of activity pattern based on multi-dimensional characteristic of each person. First, we classify activity types of reviewing the trip chain and activity purpose. Next, we identified preferable activity types using complicated characteristics of main agent of activity. We concluded that choice behavior of activity pattern is dependent on complex characteristics of each agent, and further multi-dimensional characteristics of each person are affected over the whole decision process of activity schedule.