• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.4
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• pp.276-286
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• 2004

현재. 미 연방도로부에서는 도로교통소음분석을 위한 예측모형 (TNM ＆ STAMINA)을 미 전 지역에 제공하고 있고, 이와 관련된 여러가지 연구논문들이 수행되고 있는바, 모델을 이용한 예측치와 실측치 간의 비교$.$분석 연구논문을 통하여 차이점이 존재하는 것을 증명하고 있다. 따라서 본 연구논문은 소음예측모형의 핵심자료로 사용될 수 있는 루이빌대(UofL) 회귀모형들을 차종별 (소형, 중형, 대형) 그리고 주별 (아리조나. 콜로라도, 조지아, 캔사스, 와싱톤)로 구분하여 그 차이점을 통계적으로 비교$.$분석$.$결론을 도출하였다. 그 결과 아리조나와 콜로라도(중대형)를 제외한 나머지 개별 State-specific데이터는 통계적으로 서로 다른 것으로 나타났다.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.121-131
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• 2014

Traffic speed is an important measure in transportation. It can be employed for various purposes, including traffic congestion detection, travel time estimation, and road design. Consequently, accurate speed prediction is essential in the development of intelligent transportation systems. In this paper, we present an analysis and speed prediction of a certain road section in Busan, South Korea. In previous works, only historical data of the target link are used for prediction. Here, we extract features from real traffic data by considering the neighboring links. After obtaining the candidate features, linear regression, model tree, and k-nearest neighbor (k-NN) are employed for both feature selection and speed prediction. The experiment results show that k-NN outperforms model tree and linear regression for the given dataset. Compared to the other predictors, k-NN significantly reduces the error measures that we use, including mean absolute percentage error (MAPE) and root mean square error (RMSE).

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.5 no.2
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• pp.223-230
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• 2015

Due to the conversion from the existing address system to road name address system, the versatility of the road name address from the public and private sectors is increasing gradually. Therefor, the reliability of the road name address basic map, of the default data is also an urgent need, this means the accuracy of the data. But, road name address basic map is production time due to the use of different map accuracy is to fall, so is also the situation insufficient of utilization. This study Construction of identify each attempt by the road name address basic map and analysis the location accuracy of road name address basic map. As a result, the RMSE of cadastral survey results showed that appears lower than the RMSE of digital map survey results. The judgment can be utilized as the basis for the proposed improvements for the road name address basic map.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.477-477
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• 2021

지구온난화로 인한 집중호우 및 태풍의 발생 횟수와 강도가 증가함에 따라 홍수피해가 증가하고 있으며, 특히 도로침수는 피해 측면에서 '복구-보상' 중심의 사후처리 체계에서 벗어나 '예방-대응-관리'를 통한 사전 재난대응 체계로 정책 전환이 요구되고 있다. 이에, 도로침수관련 재난정책의 기반기술이 될 수 있는 '도로침수 실시간 예측·감시 및 운영 기술'을 경상남도 진주시를 대상으로 침수피해 관련 지역현안을 해결하고자 하며, 강우예측자료를 활용한 침수해석, CCTV영상을 이용한 AI기반 실시간 침수감시, 공간 빅데이터 기반 침수정보제공, e-SOP 등 다양한 기술이 융합된 실증 연구로 이루어진다. 본 연구결과물이 실용화되어 도로침수통합관리시스템으로 운영된다면 지역 수재해 대응력 향상에 기여할 것으로 판단된다.

• Journal of KIISE
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• v.43 no.1
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• pp.40-46
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• 2016

Digital road map generation is primarily based on artificial satellite photographing or in-site manual survey work. Therefore, these map generation procedures require a lot of time and a large budget to create and update road maps. Consequently, people have tried to develop automated map generation systems using GPS trajectory data sets obtained by public vehicles. A fundamental problem in this road generation procedure involves the extraction of representative trajectory such as main roads. Extracting a representative trajectory requires the base data set of piecewise line segments(GPS-trajectories), which have close starting and ending points. So, geometrically similar trajectories are selected for clustering before extracting one representative trajectory from among them. This paper proposes a new divide- and-conquer approach by partitioning the whole map region into regular grid sub-spaces. We then try to find similar trajectories by sweeping. Also, we applied the $Fr{\acute{e}}chet$ distance measure to compute the similarity between a pair of trajectories. We conducted experiments using a set of real GPS data with more than 500 vehicle trajectories obtained from Gangnam-gu, Seoul. The experiment shows that our grid partitioning approach is fast and stable and can be used in real applications for vehicle trajectory clustering.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.18 no.4
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• pp.71-79
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• 2019

By using convolutional neural networks (CNNs) based on semantic segmentation, road surface damage detection has being studied. In order to generate the CNN model, it is essential to collect the input and the corresponding labeled images. Unfortunately, such collecting pairs of the dataset requires a great deal of time and costs. In this paper, we proposed a road surface damage detection technique based on semi-supervised learning using pseudo labels to mitigate such problem. The model is updated by properly mixing labeled and unlabeled datasets, and compares the performance against existing model using only labeled dataset. As a subjective result, it was confirmed that the recall was slightly degraded, but the precision was considerably improved. In addition, the $F_1-score$ was also evaluated as a high value.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.4
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• pp.97-113
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• 2023

Swift and appropriate responses in emergency situations like objects falling on the road can bring convenience to road users and effectively reduces secondary traffic accidents. In Korea, current intelligent transportation system (ITS)-based detection systems for emergency road situations mainly rely on loop detectors and CCTV cameras, which only capture road data within detection range of the equipment. Therefore, a new detection method is needed to identify emergency situations in spatially shaded areas that existing ITS detection systems cannot reach. In this study, we propose a ResNet-based algorithm that detects and classifies emergency situations from vehicle camera footage. We collected front-view driving videos recorded on Korean highways, labeling each video by defining the type of emergency, and training the proposed algorithm with the data.

• 한국도로학회:학술대회논문집
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• 2008.03a
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• pp.7-14
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• 2008

• 한국도로학회:학술대회논문집
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• 2006.10a
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• pp.429-438
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• 2006

• Journal of the Korea Convergence Society
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• v.11 no.9
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• pp.1-6
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• 2020

In this study, we propose an association inference model based on data bias optimization for road hazard detection. This is a mining model based on association analysis to collect user's personal characteristics and surrounding environment data and provide traffic accident prevention services. This creates transaction data composed of various context variables. Based on the generated information, a meaningful correlation of variables in each transaction is derived through correlation pattern analysis. Considering the bias of classified categorical data, pruning is performed with optimized support and reliability values. Based on the extracted high-level association rules, a risk detection model for personal characteristics and driving road conditions is provided to users. This enables traffic services that overcome the data bias problem and prevent potential road accidents by considering the association between data. In the performance evaluation, the proposed method is excellently evaluated as 0.778 in accuracy and 0.743 in the Kappa coefficient.