• 한국ITS학회:학술대회논문집
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• 2003.11a
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• pp.234-240
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• 2003

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.1-6
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• 2020

Road movement pollutant air environment big data is a link between real-time traffic data such as vehicle type, speed, and load using AVC, VDS, WIM, and DTG, which are always traffic volume survey equipment, and road shape (uphill, downhill, turning section) data using GIS. It consists of traffic flow data. Also, unlike general data, a lot of data per unit time is generated and has various formats. In particular, since about 7.4 million cases/hour or more of large-scale real-time data collected as detailed traffic flow information are collected, stored and processed, a system that can efficiently process data is required. Therefore, in this study, an open source-based data parallel processing performance optimization study is conducted for the visualization of big data in the air environment of road transport pollution.

• The Korean Journal of Applied Statistics
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• v.16 no.2
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• pp.203-215
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• 2003

In order to provide the drivers with more reliable transportation information in NHTMS(National Highway Transportation Management System), it is important to estimate the expected passage time by using the traffic volume and speed. In this study, we analyze the characteristics of the traffic volume in the national highway and we investigate two real-time adjustment methods: the average adjustment method and the auto-regressive adjustment method. In addition, we compare them using the real data collected at the National Highway Route 3 in 2000.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.86-99
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• 2021

One of the methods to alleviate traffic congestion is to increase the efficiency of the roads by providing traffic condition information on road user and distributing the traffic. For this, reliability must be guaranteed, and quantitative real-time traffic speed prediction is essential. In this study, and based on analysis of traffic speed related to traffic conditions, historical data correlated with traffic flow were used as input. We developed an LSTM model that predicts speed in response to normal traffic conditions, along with a CNN-LSTM model that predicts speed in response to incidents. Through these models, we try to predict traffic speeds during the hour in five-minute intervals. As a result, predictions had an average error rate of 7.43km/h for normal traffic flows, and an error rate of 7.66km/h for traffic incident flows when there was an incident.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2013.10a
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• pp.71-73
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• 2013

선박 및 VTS에서 선박교통량이 밀집되는 혼잡해역의 선박교통밀도 평가는 중요하다. 본 연구에서는 선박 충돌 회피를 위한 적절한 반경인 Ship Domain 영역과 혼잡구역 내 선박 체류시간 및 전 방위 통항류를 고려하여 혼잡해역의 항로가동률 및 실시간 해상교통밀도 산출 모델을 제안하고자 한다. 또한 제안된 모델식을 기반으로 시뮬레이터를 프로그래밍하여, 실 해역 해상교통 데이터를 적용하여 제안한 모델식의 유효성을 평가하고자 한다.

• Journal of Korean Society of Transportation
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• v.27 no.2
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• pp.133-144
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• 2009

In Korea, the ITS project has been progressed to improve traffic mobility and safety. Further, it is to relieve traffic jam by supply real time travel information for drivers and to promote traffic convenience and safety. It is important that the traffic information is provided accurately. This study was conducted outlier elimination and missing data adjustment to improve accuracy of raw data. A method for raise reliability of travel time prediction information was presented. We developed Historical Profile model and adjustment formula to reflect quality of interrupted flow. We predicted travel time by developed Historical Profile model and adjustment formula and verified by comparison between developed model and existing model such as Neural Network model and Kalman Filter model. The results of comparative analysis clarified that developed model and Karlman Filter model similarity predicted in general situation but developed model was more accurate than other models in incident situation.

• Proceedings of the KOR-KST Conference
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• 1998.10b
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• pp.297-297
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• 1998

고속도로의 교통혼잡을 관리하기 위해서는 근본적으로 혼잡지점 상류부의 진입교통량을 제어해야 한다. 이를 위한 효과적인 램프미터링 운영전략이나 고속도로 교통정보제공방안을 수립하기 위해서는 혼잡영향권(대기행렬길이)에 관한 신뢰성 있는 데이터가 반드시 필요하다. 고속도로의 대기행렬길이를 산정하기 위해 일반적으로 충격파이론과 Queueing이론을 제시하고 있다. 그러나, 기존의 충격파 이론을 포물선형의 교통량-밀도관계식을 근거로 하고 있어 충격파간에 발생하는 부수적인 충격파를 해석하는 과정이 수학적으로 불가능하여 실질적인 목적으로 사용할 수 없음은 이미 잘 알고 있는 사실이다. 최근에 이러한 한계를 극복할 수 있는 새로운 방법으로 교통량 밀도간의 관계식을 삼각형으로 가정하고 교통량 대신에 누적교통량을 사용하는 Simplified Theory of Kinematic Waves In Highway Traffic이 개발(Newell, 1993)되었지만, 이 방법을 적용하기 위해서는 기본적으로 대상 고속도로 구간의 교통량-밀도관계식을 규명해야 하는 어려움이 있다.(사실 실시간으로 밀도데이터를 수집하기란 불가능하다.) Queueing이론에서 제시하는 대기행렬은 모두 대기차량이 병목지점에 수직으로 정렬하여 도로를 점유하지 않는 Point Queue(혹은 Vertical stack Queue)로서 실제로 도로상에 정렬된 대기행렬(Real Physical Queue)과는 전혀 다르다. 이미 입증된 바 있어, Queueing이론을 이용함은 타당성이 없다. 이러한 사실에 근거하여 본 연구는 고속도로 대기행렬길이를 산정할 수 있는 모형개발을 위한 기초연구로서 혼잡상태의 연속류 특성을 분석하는데 목적이 있다. 이를 위해, 본 연구에서는 서울시 도시고속도로에서 수집한 실제 데이터를 이용하여 진입램프지점의 혼잡상태에서 대기행렬의 증가 또는 감소하는 과정을 분석하였다. 주요 분석결과는 다음과 같다. 1. 혼잡초기의 대기행렬은 다른 혼잡시기에 비해 상대적으로 급속한 속도로 증가함. 2. 혼잡초기의 대기행렬의 밀도는 다른 혼잡시기에 비해 비교적 낮음. 3. 위의 두 결과는 서로 관계가 있으며, 혼잡시 운전자의 행태(차두간격)과 혼잡기간중에도 변화함을 의미함. 4. 교통변수 중에서 대기행렬길이를 산정하는데 적합한 교통변수를 교통량과 밀도로 판단됨. 5. Queueing이론에서 제시하는 대리행렬길이 산정방법인 대기차량대수$\times$평균차두간격은 대기행렬내 밀도가 일정하지 않아 부적합함을 재확인함. 6. 혼잡초기를 제외한 혼잡기간 중 대기행렬길이는 밀도데이터 없이도 혼잡 상류부의 도착교통량과 병목지점 본선통과교통량만을 이용하여 추정이 가능함. 7. 이상에 연구한 결과를 토대로, 고속도로 대기행렬길이를 산정할 수 있는 기초적인 도형을 제시함.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.3
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• pp.15-28
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• 2008

According to the accident statistics published by the National Police Agency in 2006, it can be recognized that drivers' characteristics and driving behaviors are the most causational factors on the traffic accidents. At present, although many recording tools such as digital speedometer or black box are distributed in the market to meet social requests of decreasing traffic accidents and increasing safe driving behaviors, it is also true that it still lacks in obvious categories for dangerous driving types and then, the efficiency of the categories to be studied has been low. In this study, dangerous driving types are redefined. They are grouped into 7 classifications in the first level, and the seven classifications are regrouped into 16 in more detail. To verify the redefined dangerous driving types, a Data-logger is developed to receive and analyze the data that occur from the driving behaviors of the test vehicle. The developed Data-logger can be used to construct a real time warning system and safe driving management system with dangerous driving patterns based on acceleration, deceleration, Yaw rate, image data, etc.

• Journal of Korean Society of Transportation
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• v.31 no.5
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• pp.3-15
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• 2013

The purpose of this study is to propose a methodology for operating variable speed limit (VSL) strategies in freeway work zones. A notable feature in this study is to incorporate a multi-criteria decision making process into deriving better VSL strategies. Decision criteria in this study include operational efficiency, safety, and environmental impacts. Travel speed, acceleration noise, and CO2 were used as performance measures for evaluating VSL strategies. A multi-criteria value function was developed through an analytical hierarchical process (AHP) for representing expert's knowledge. Then, a variety of VSL operations scenarios were investigated utilizing a microscopic traffic simulation suite, VISSIM. The proposed methodology would be useful in supporting more efficient, safer, and more environment-friendly traffic operations and control in freeway work zones.

• Journal of Korean Society of Transportation
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• v.17 no.2
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• pp.179-191
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• 1999

To device effective freeway traffic flow control strategies and freeway traffic information dissemination strategies, it is very important to estimate real physical queue length on the freeway. Shock wave theory and queueing theory have limitation to be used to estimate the queue length. The primary objective of this study is to develop a reliable method for estimating the physical queue length and level of congestion. Queueing propagation processes were analysed by using such traffic data as main line traffic volume, ramp volume, density. speed, and physical queue length collected by video photographing on Olympic Freeway. As a result of analysis, it has been confirmed that the real queue length can be estimated by using the traffic counts arriving the congested region and passing a bottleneck location. Further more, a reliable method for estimating the level of congestion could be developed on the basis of real-time traffic counts.