• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1D
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• pp.1-9
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• 2009

Bus information system(BIS), as a part of the intelligent transportation system(ITS), is one of the most advanced public transportation systems which provide the real-time bus traffic information for the users waiting the buses at the bus stop. However, correct bus information data, such as the present bus location, the user waiting time, the bus arrival time, etc. are not provided for the bus users because the proper bus arrival time predictive models are not used yet in most of the cities operating the bus information system, including the metropolitan City of Ulsan. Thus, the purpose in this study is to investigate real-time bus traffic characteristic data for identifying the bus operation characteristics on the arterial under the study in the metropolitan City of Ulsan, analyze real-time bus traffic characteristic data on the ID locations of the arterial under the study, construct the optimal unit segment models for the unit segments which are the bus stop, node and travel section using the exponential smoothing, weighted smoothing and Kalman Filter methods, respectively, and finally suggest the optimal integrated model for predicting the real-time bus arrival time at the bus stop of the arterial under the study.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.643-654
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• 2018

In order to understand and manage traffic flows in urban areas in the past, a variety of traffic engineering theoretical indicators such as intersection lag and highway speed have been applied. However, these theories and indicators have been developed under the constraints of traffic engineering research before the construction of intelligent transportation system. Since the ATIS system currently exists, it is necessary to introduce a separate traffic engineering technology that utilizes the data. In this paper, it is aimed to confirm whether it is applicable to intermittent flow (approach road, intersection, control group, main road axis) by using 'congestion intensity' which is already used in traffic engineering field. The results of this study are as follows: (1) The traffic signal improvement effect of urban road access road, intersection road, control group, Two verification studies were performed to verify the derived congestion intensity index. (1) verification of congestion intensity threshold value analysis and (2) crossing improvement using the congestion intensity. Through verification, it was confirmed that it is possible to apply the congestion intensity in the inter - city intermittent flow using the 5 - minute unit speed data so as to be able to escape from the existing traffic signal operation management which is past passive and manpower limit.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.44-57
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• 2019

Various studies have been conducted to solve traffic congestions in many metropolitan cities through accurate traffic flow prediction. Most studies are based on the assumption that past traffic patterns repeat in the future. Models based on such an assumption fall short in case irregular traffic patterns abruptly occur. Instead, the approaches such as predicting traffic pattern through big data analytics and artificial intelligence have emerged. Specifically, deep learning algorithms such as RNN have been prevalent for tackling the problems of predicting temporal traffic flow as a time series. However, these algorithms do not perform well in terms of long-term prediction. In this paper, we take into account various external factors that may affect the traffic flows. We model the correlation between the multi-dimensional context information with temporal traffic speed pattern using deep neural networks. Our model trained with the traffic data from TOPIS system by Seoul, Korea can predict traffic speed on a specific date with the accuracy reaching nearly 90%. We expect that the accuracy can be improved further by taking into account additional factors such as accidents and constructions for the prediction.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.1D
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• pp.43-50
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• 2011

The monitoring system for link travel speed using taxi probe is one of key sub-systems of ITS. Link travel speed collected by taxi probe has been widely employed for both monitoring the traffic states of urban road network and providing real-time travel time information. When sample size of taxi probe is small and link travel time is longer than a length of time interval to collect travel speed data, and in turn the missing state is inevitable. Under this missing state, link travel speed data is real-timely not collected. This missing state changes from single to multiple time intervals. Existing single interval prediction techniques can not generate multiple future states. For this reason, it is necessary to replace multiple missing states with the estimations generated by multi-interval prediction method. In this study, a multi-interval prediction method to generate the speed estimations of single and multiple future time step is introduced overcoming the shortcomings of short-term techniques. The model is developed based on Non-Parametric Regression (NPR), and outperformed single-interval prediction methods in terms of prediction accuracy in spite of multi-interval prediction scheme.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.4
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• pp.1-9
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• 2015

Since logical ground and method to deduct location to install Integrated Standard Controller under development to combine physically and functionally multiple enclosures of controllers for various ITS equipments on streets into one enclosure, there are much difficulty to apply it to site. Particularly we need to establish standardized methodology to deduct optimal location to integrate individual controllers installed dispersedly on streets such as node part and line part. Accordingly this study has the purpose of suggesting more reasonable and efficient methodology to determine location for Integrated Standard Controller to be developed newly. For this, new solution has been searched by using centroid which is utilized to decide facility location in urban planning. As the result of analysis, central point among centroid, in which equal connection to each equipment is possible is proved to be the most reasonable and correct coordinate and zone is proposed as installation location through designating coordinate system. The methodology suggested by this study seems to have high degree of utilization in site according to expansion of integrated standard controller market.

• Journal of Broadcast Engineering
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• v.27 no.5
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• pp.738-750
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• 2022

In this study, we reviewed and discussed whether auditory stimuli with short length can evoke emotion-related neurological responses. The findings implicate that if personalized sound tracks are provided to XR users based on machine learning or probability network models, user experiences in XR environment can be enhanced. We also investigated that the arousal-relaxed factor evoked by short auditory sound can make distinct patterns in functional connectivity characterized from background EEG signals. We found that coherence in the right hemisphere increases in sound-evoked arousal state, and vice versa in relaxed state. Our findings can be practically utilized in developing XR sound bio-feedback system which can provide preference sound to users for highly immersive XR experiences.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.315-315
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• 2023

급격한 기후변화에 기인하여 전 세계적으로 거듭되는 돌발홍수로 인한 피해가 급격히 증가하고 있는 실정이다. 우리나라에서도 최근 중소 규모의 하천 뿐 아니라 도시 생활하천, 도심지 저지대에서 갑작스런 홍수와 침수로 인해 많은 인명과 재산 피해를 경험하고 있다. 이런 문제를 인식하여 최근 정부차원에서 다양한 센서와 인공지능에 기반하는 많은 인프라 및 연구 투자가 이루어지고 있지만, 높은 설치 및 운영 비용과 우리나라의 복잡한 하천 환경 특성으로 인해 소하천이나 도심지 저지대에서는 그 효율성을 제대로 발휘하지 못하고 있다. 따라서 본 논문에서는 주변환경의 변화에 강인한 복합 센서단말을 통해, 하천 정보(유량, 유속, 수위 등)을 실시간 측정하고, 해당지역의 특성을 고려한 하천 또는 저지대의 위험도를 스스로 판단할 수 있는 기술을 제안한다. 또한, 본 논문에서는 제안한 저비용 초소형의 단말 장치로 지점의 하천 정보를 실시간 측정하여 IoT망을 통해 3차원 하천 디지털트윈 모델로 전달하여, 유속과 수위를 그대로 재연함으로써, 하천 침수 위험 감시의 효율성을 검증하였다. 3차원 DEM(Digital Elevation Models) 데이터와 실제 하천을 관측한 데이터를 이용한 디지털트윈 검증 결과, 데이터 전송 지연시간을 감안하여 3초 이내에 하천의 수위와 유속이 3차원 모델에 반영되는 것을 확인하였다. 이 결과로부터 열악한 환경에서도 실시간 하천 상황을 원거리에서 모니터링 할 수 있으며, 강우와 유출에 따른 하천 홍수 메카니즘을 새롭게 시뮬레이션할 수 있는 방법론을 제시할 수 있을 것으로 기대한다.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.3
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• pp.1115-1122
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• 2013

Recently the demands for traffic information tend to increase, and travel time might one of the most important traffic information. To effectively estimate exact travel time, highly reliable traffic data collection is required. BIS(Bus Information System) data would be useful for the estimation of the route travel time because BIS is collecting data for the bus travel time on the main road of the city on real-time basis. Traditionally use of BIS data has been limited to the realm of bus operating but it has not been used for a variety of traffic categories. Therefore, this study estimates a route travel time on road networks in urban areas on the basis of real-time data of BIS and then eventually constructs regression models. These models use an explanatory variable that corresponds to bus travel time excluding service time at the bus stop. The results show that the coefficient of determination for the constructed regression model is more than 0.950. As a result of T-test performance with assistance from collected data and estimated model values, it is likely that the model is statistically significant with a confidence level of 95%. It is generally found that the estimation for the exact travel time on real-time basis is plausible if the BIS data is used.

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.264-264
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• 2017

국외의 상수도 원격검침 시스템 내 데이터 전송방식은 도시 규모, 계량기의 밀도, 전력공급 여부 및 통신망의 설치 여부 등을 종합적으로 고려하여 결정되었다. 대부분의 스마트워터미터 제조업체들은 계량기의 부호기가 공급하는 판독 내용(데이터)을 전송할 검침단말기와 근거리 통신망(neighborhood area network)을 연계하여 개발 및 판매하였으며, 자체 소유 통신 프로토콜을 사용하여 라디오 주파수(RF) 통신 기술을 사용하고 있다. 광역통신망(wide area network)의 경우, 노드(말단의 계량기 및 센서)들과 이에 연결된 통신망 들을 포함한 네트웍의 배열이나 구성이 스타(star), 메쉬(mesh), 버스(bus), 나무(tree) 등의 형태로 통신망이 구성되어 있으나, 스타와 메쉬형 통신망 구성형태가 가장 널리 활용되는 것으로 조사되었다. 시스템 통합운영관리 업체들인 IBM, Oracle, Itron 등은 용수 인프라 관리 또는 통합네트워크 솔루션 등의 통합 물관리 시스템(integrated water management system)을 개발하여 현장적용을 하고 있으며, 원격검침 시스템을 통해 고객들의 현재 소비량과 과거 누적 소비량, 누수 감지 서비스 및 실시간 요금 고지 등을 실시간으로 웹 포털과 앱을 통해 제공하고 있다. 또한, 일부 제조업체들은 도시 용수공급/소비 관리자가 주민의 용수사용량을 모니터링하여 일평균 용수사용량 및 사용 경향을 파악하고, 누수를 검지하여 복구 및 용수 사용 지속가능성 지수를 제시하고, 실시간으로 주민의 용수사용량 관련 데이터를 모니터링하여 용수공급의 최적화를 위한 의사결정지원 서비스를 용수공급자에게 제공하고 있다. 최근에는 인공지능을 활용해 가정용수의 용도별(세탁용수, 화장실용수, 샤워용수, 식기세척용수 등) 사용량 곡선을 패터닝하여 profiling 기법을 도입해, 스마트워터미터에서 용수사용량이 통합되어 검지될 시 용수사용량의 세부 용도별 re-profiling 기법을 도입하여 가정용수내 과소비되는 지점을 도출 후 절감을 유도하는 기술이 개발 중이다. 또한, 미래 용수 사용량 예측을 위해 다양한 시계열 자료를 분석하는 선형 종속 모형(자기회귀모형, 자기회귀이동평균모형, 자기회귀적분이동평균모형 등)과 비선형 종속 모형(Fuzzy Logic, Neural Network, Genetic Algorithm 등)을 활용한 예측기능이 구축되어 상호 비교하여 최적의 용수사용량 예측 도구를 제공되고 있다.

• Journal of the Korea institute for structural maintenance and inspection
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• v.23 no.7
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• pp.66-71
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• 2019

In this paper, a study was conducted on the method of using GPR data to predict rebar thickness inside a facility. As shown in the cases of poor construction, such as the use of rebars below the domestic standard and the construction of reinforcement, information on rebar thickness can be found to be essential for precision safety diagnosis of structures. For this purpose, the B-scan data of GPR was obtained by gradually increasing the diameter of rebars by making specimen. Because the B-scan data of GPR is less visible, the data was converted into the heatmap image data through migration to increase the intuition of the data. In order to compare the results of application of commonly used B-scan data and heatmap data to CNN, this study extracted areas for rebars from B-scan and heatmap data respectively to build training and validation data, and applied CNN to the deployed data. As a result, better results were obtained for the heatmap data when compared with the B-scan data. This confirms that if GPR heatmap data are used, rebar thickness can be predicted with higher accuracy than when B-scan data is used, and the possibility of predicting rebar thickness inside a facility is verified.