• Proceedings of the Korean Institute of Landscape Architecture Conference
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• 2014.10a
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• pp.44-46
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• 2014

• Journal of Korean Society of Transportation
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• v.21 no.1
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• pp.53-63
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• 2003

The Origin-Destination(OD) matrix is very important in describing transport movements in a region. The OD matrix can be estimated using traffic counts on links in the transport network and other available information. This information on the travel is often contained in a target OD matrix and traffic counts in links. To estimate an OD matrix from traffic counts, they are the major input data which obviously affects the accuracy of the OD matrix estimated, Generally, the quality of an estimated OD matrix depends much on the reliability of the input data, and the number and locations of traffic counting points in the network. Any Process regarding the traffic counts such as the amount and their location has to be carefully studied. The objective of this study is to select of the optimal location of traffic counting points for the OD matrix estimation. The model was tested in nationwide network. The network consists of 224 zones, 3,125 nodes and 6,725 links except to inner city road links. The OD matrix applied for selection of traffic counting points was estimated to 3-constrained entropy maximizing model. The results of this study follow that : the selected alternative to the best optimal counting points of six alternatives is the alternative using common links of OD matrix and vehicle-km and traffic density(13.0% of 6,725 links), however the worst alternative is alternative of all available traffic counting points(44.9% of 6,725 links) in the network. Finally, it should be concluded that the accuracy of reproduced OD matrix using traffic counts related much to the number of traffic counting points and locations.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.2
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• pp.31-37
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• 2012

Because of road construction and expansion, Update of the road traffic facilities DB is steadily increased each year, and, Increasing drivers and cars, safety signs for traffic safety are required management and additional installation continuously. To update Safety Sign database promptly, we have developed auto recognition function of safety sign, and analyzed coordinates accuracy. The purpose of this study was to propose methods to update about road traffic facilities efficiently. For this purpose, omni-directional camera was calibrated for acquisition of 3-dimensional coordinates, integrated GPS/IMU/DMI system and applied image processing. In this experiment, we proposed a effective method to update database of road traffic facilities for digital map.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.4
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• pp.667-675
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• 2016

The goal of this study is to be used as baseline data for transportation demand management. At the present time the number of registered car and householding car is increasing, so there is a need to analyze the car ownership pattern through household car hold status. Also, it is necessary to analyze the factor of increasing car. The research is proceeded with classifying as the household which is holding private cars or holding passenger cars and non passenger cars based on the result of the research of the household travel survey data. The result of this study is shown as follows. According to car ownership pattern, there are more households holding passenger cars only when they are holding less than 2 cars. Otherwise there are more households holding passenger car and non passenger car when they are holding more than 3 cars. Using the Ordered Logit Model, there are more differences in factors affects holding cars by variables of housing type and household properties.

• Journal of Navigation and Port Research
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• v.47 no.4
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• pp.212-219
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• 2023

Vessel Traffic Service (VTS) area presents a complex traffic pattern due to ships entering or leaving the port to utilize port facilities, as well as ships passing through the coastal area. To ensure safe and efficient management of maritime traffic, VTS operators continuously monitor and control vessels in real time. However, during periods of high traffic congestion, the workload of VTS operators increases, which can result in delayed or inadequate VTS services. Therefore, it would be beneficial to predict traffic congestion and congested areas to enable more efficient traffic control. Currently, such prediction relies on the experience of VTS operators. In this paper, we defined vessel traffic congestion from the perspective of a VTS operator. We proposed a method to generate traffic networks using historical navigational data and predict traffic congestion and congested areas. Experiments were performed to compare prediction results with real maritime data (Daesan port VTS) and examine whether the proposed method could support VTS operators.

• Journal of Korean Society of Transportation
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• v.36 no.1
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• pp.51-65
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• 2018

Intentional aggressive driving (IAD) is defined as a hazardous driving event that the aggressive driver intentionally threatens neighbor drivers with abrupt longitudinal and lateral maneuvering. This study developed a methodology for detecting IAD events based on the analysis of interactions between aggressive driver and normal driver. Three major aggressive events including rear-close following, side-close driving, and sudden deceleration were analyzed to develop the algorithm. Then, driving simulation experiments were conducted using a multi-agent driving simulator to obtain data to be used for the development of the detection algorithm. In order to detect the driver's intention to attack, a relative evaluation index (Erratic Driving Index, EDI) reflecting the driving pattern was derived. The derived IAD event detection algorithm utilizes both the existing absolute detection method and the relative detection method. It is expected that the proposed methodology can be effectively used for detecting IAD events in support of in-vehicle data recorder technology in practice.

• Proceedings of the KOR-KST Conference
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• 1998.10b
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• pp.275-284
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• 1998

신호화 교차로는 한정된 시간 자원을 각 접근로별로 서로 다른 수요에 적절하게 효과적으로 배분함으로써 그 운영효율이 극대화될 수 있다. 현재 서울시 기존 전자신호시스템은 검지기체계를 갖추고 있으나 주로 시간대별 제어방식(TOD제어)을 사용하고 있다. 이에 대한 대안으로 실시간 교차로신호제어시스템인 이른바 첨단신호시스템이 개발되어 서울시 강남지역 61개소에서 시범운영중에 있다. 첨단신호시스템은 접근로별 수요에 따라 녹색시간을 배분하는 방식을 적용하고 있는데, 본 논문에서는 첨단신호시스템의 운영효율성을 평가하기 위하여 실제 교통수요와 운영녹색시간을 비교·분석하였다. 그 결과로 얻은 결론은 다음과 같다. 첫째, 방출차량에 의한 포화도의 비율을 고려하여 주기 및 녹색시간을 결정하는 첨단신호제어시스템의 알고리즘은 비포화시 직진이동류에 대한 녹색시간은 수요에 비해 과대산출운영되고 있다. 둘째, 좌회전의 경우 대기차량의 패턴이 불규칙할 때, 실시간 녹색시간제어기능이 미흡하다. 따라서, 향후에는 교통수요를 고려할 수 있는 알고리즘의 보다 심도있는 연구·개발이 요구되며, 또한 비포화 상황이 아닌 과포화 상황에도 적용될 수 있는 알고리즘의 개발이 이루어져야할 것으로 보인다.

• Journal of Korean Society of Transportation
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• v.22 no.2 s.73
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• pp.77-86
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• 2004

In this study, we applied the Bayesian Network for the case of the mode choice models using the Seoul metropolitan area's house trip survey Data. Sex and age were used lot the independent variables for the explanation or the mode choice, and the relationships between the mode choice and the travellers' social characteristics were identified by the Bayesian Network. Furthermore, trip and mode's characteristics such as time and fare were also used for independent variables and the mode choice models were developed. It was found that the Bayesian Network were useful tool to overcome the problems which were in the traditional mode choice models. In particular, the various transport policies could be evaluated in the very short time by the established relation-ships. It is expected that the Bayesian Network will be utilized as the important tools for the transport analysis.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.3
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• pp.59-73
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• 2021

There are many lives lost due traffic accidents, and which have not decreased despite advances in technology. In order to prevent traffic accidents, it is necessary to accurately forecast how they will change in the future. Until now, traffic accident-frequency forecasting has not been a major research field, but has been analyzed microscopically by traditional methods, mainly based on statistics over a previous period of time. Despite the recent introduction of AI to the traffic accident field, the focus is mainly on forecasting traffic flow. This study converts into time series data the records from 1,339,587 traffic accidents that occurred in Korea from 2014 to 2019, and uses the AI algorithm to forecast the frequency of traffic accidents based on driver's age and time of day. In addition, the forecast values and the actual values were compared and verified based on changes in the traffic environment due to COVID-19. In the future, these research results are expected to lead to improvements in policies that prevent traffic accidents.

• Journal of Korean Society of Transportation
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• v.33 no.1
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• pp.14-28
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• 2015

Numerous research has been conducted using internet search data since the mid-2000s. For example, Google Inc. developed a service predicting influenza patterns using the internet search data. The main objective of this study is to prove the hypothesis that highway traffic indices are similar to the internet search patterns. In order to achieve this objective, a model to predict the number of vehicles entering the expressway and space-mean speed was developed and the goodness-of-fit of the model was assessed. The results revealed several findings. First, it was shown that the Google search traffic was a good predictor for the TCS entering traffic volume model at sites with frequent commute trips, and it had a negative correlation with the TCS entering traffic volume. Second, the Naver search traffic was utilized for the TCS entering traffic volume model at sites with numerous recreational trips, and it was positively correlated with the TCS entering traffic volume. Third, it was uncovered that the VDS speed had a negative relationship with the search traffic on the time series diagram. Lastly, it was concluded that the transfer function noise time series model showed the better goodness-of-fit compared to the other time series model. It is expected that "Big Data" from the internet search data can be extensively applied in the transportation field if the sources of search traffic, time difference and aggregation units are explored in the follow-up studies.