• Proceedings of the KOR-KST Conference
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• 1998.10a
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• pp.196-205
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• 1998

교통신호에서 주로 고려되는 변수는 신호주기(cycle length), 녹색시간(green split), 옵셋(offset)그리고 좌회전 현시순서(left-turn phase sequence)로 구성된다. 기존의 대부분의 연동 모델들은 고정된 주기하에서 평균적인 유입 교통량을 측정한 후, 선형최적화 이론을 적용하여 최적 신호를 산출한다. 그러나 이 방법은 어디까지나 평균적인 데이터에 대해서 계산을 한 것이기 때문에 실시간 최적화를 제공하기가 어렵다. 본 연구에서는 평균 차량 통행량 대신 실시간으로 입력되는 차량 대기행렬, 차량 도착률을 기초로 대기차량을 최소화하는 동적 신호시간 산출 모델을 개발하였다. 본 모델에서는 Peytechew가 제안한 각 진입로에서의 대기 차량 예측 모델을 기초로 하여 다음 주기에서의 차량 대기 행렬을 예측한 후, 선형 최적화 이론을 적용하여 신호시간을 산출한다. 본 모델에서 산출된 신호주기와 녹색시간은 대기차량길이를 최소화하는 신호 시간으로서 교차로간의 연동효과를 고려하여 실시산 교통상황에 따라 주기별로 변화한다. 본 모델은 3개의 교차로로 구성된 네트워크를 대상으로 적용하였다. 실험 네트워크의 주도로 교통량은 부도로의 교통량 보다 많다고 가정하였으며 각 링크사이에서의 차량 진출입은 없다고 보았다.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.19-27
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• 2021

In this paper, we present a method for effectively predicting traffic volume based on vehicle location data that are collected by using LDP (Local Differential Privacy). The proposed solution in this paper consists of two phases: the process of collecting vehicle location data in a privacy-presering manner and the process of predicting traffic volume using the collected location data. In the first phase, the vehicle's location data is collected by using LDP to prevent privacy issues that may arise during the data collection process. LDP adds random noise to the original data when collecting data to prevent the data owner's sensitive information from being exposed to the outside. This allows the collection of vehicle location data, while preserving the driver's privacy. In the second phase, the traffic volume is predicted by applying deep learning techniques to the data collected in the first stage. Experimental results with real data sets demonstrate that the method proposed in this paper can effectively predict the traffic volume using the location data that are collected in a privacy-preserving manner.

• Journal of Korean Society of Transportation
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• v.17 no.3
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• pp.117-130
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• 1999

Earthquakes damage roadway bridges and structures, resulting in significant impacts on transportation system Performance and regional economy. Seismic risk analysis (SRA) procedures establish retrofit priorities for vulnerable highway bridges. SRA procedures use average daily traffic volumes to determine the relative importance of a bridge. This research develops a cost-effective transportation network analysis (TAN) procedure for evaluating numerous traffic flow analyses in terms of the additional system cost due to failure. An important feature of the TNA Procedure is the use of an associative memory (AM) approach in the artificial intelligence held. A simple seven-zone network is developed and used to evaluate the TNA procedure. A subset of link failure system states is randomly selected to simulate synthetic post-earthquake network flows. The performance of different AM model is evaluated. Results from numerous link-failure scenarios demonstrate the applicability of the AM models to traffic flow estimation.

• Journal of Korean Society of Transportation
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• v.27 no.6
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• pp.139-146
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• 2009

While air pollutants emission caused by the traffic is one of the major sources, few researches have done. This study investigated the extent to which traffic and road related characteristics such as traffic volumes, speeds and road weather data including wind speed, temperature and humidity, as well as the road geometry affect the air pollutant emission. We collected the real time air pollutant emission data from Seoul automatic stations and real time traffic volume counts as well as the road geometry. The regression air pollutant emission models were estimated. The results show followings. First, the more traffic volume increase, the more pollutant emission increase. The more vehicle speed increase, the more measurement quantity of pollutant decrease. Secondly, as the wind speed, temperature, and humidity increase, the amount of air pollutant is likely to decrease. Thirdly, the figure of intersections affects air pollutant emission. To verify the estimated models, we compared the estimates of the air pollutant emission with the real emission data. The result show the estimated results of Chunggae 4 station has the most reliable data compared with the others. This study is differentiated in the way the model used the real time air pollutant emission data and real time traffic data as well as the road geometry to explain the effects of the traffic and road characteristics on air quality.

• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.207-218
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• 2001

As a city and industries are developed rapidly, a traffic accident and congestion take places on the road link become serious and it can be a large problem of the society in the future. Especially, most of the traffic accidents on the signalized intersection are caused by the human factor, vehicle and environmental factor mutually. The relation of the traffic accident and volume is acting on the outbreak of the traffic accident and the mistake of driver altogether as a major cause. The purpose of this paper is to develop a model for the forecasting of the traffic accident and to use research data gained to predict many traffic accidents. The data of this study were used with real one of the 73 areas of the four-legged signalized intersection in Kwang-ju city from 1996 to 1998 for three years to develop a model for the forecasting of the traffic accident. The statistical methods used in this paper are the principal component, regression and correlation analysis. We studied accident models to find out useful data from the statistics method and applied the data to the different area of the Choun-La province for the verification of the model. So, the result of this paper showed a reasonable model for the forecasting or the traffic accident and possibility of the model for simulating on real case. Finally, This study would be made of a study continually for the safe design and plan for the four-legged signalized intersection.

• Journal of Korean Society of Transportation
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• v.22 no.4 s.75
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• pp.117-134
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• 2004

Truck traffic accounts for a substantial fraction of the traffic stream in many regions and is often the source of localized traffic congestion, potential parking and safety problems. Truck trips tend to be ignored or treated superficially in travel demand models. It reduces the effectiveness and accuracy of travel demand forecasting and may result in misguided transportation policy and project decisions. This paper presents the development of speed-flow relationships with truck impacts based on CORSIM simulation results in order to enhance travel demand model by incorporating truck trips. The traditional BPR(Bureau of Public Road) function representing the speed-flow relationships for roadway facilities is modified to specifically include the impacts of truck traffics. A number of new speed-flow functions have been developed based on CORSIM simulation results for freeways and urban arterials.

• Journal of Korean Society of Transportation
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• v.16 no.2
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• pp.117-131
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• 1998

지체함수는 교통량과 속도의 관계를 단조 증가함수로 단순화하여 교통수요예측의 교통배정모형에 사용되게 된다. 이 지체함수를 구하는 방법은 두가지로 구분할 수 있는데, 첫째는 교통배정을 통해 구해지는 추정 링크통행량과 실측 교통량을 비교해 가면서 정산하는 방법이고 둘째는 교통량-속도 관계로부터 직접 구하는 방법이다. 첫째 방법은 구해진 O/D 통행량표의 부정확성과 모형에 내재하는 오류가 이 지체함수에 포함될 가능성이 매우 높은 단점을 가지고 있다. 본 연구에서는 교통량-속도 관계로부터 직접 도로유형별 지체함수를 구하여 교통배정에 적용하는 새로운 방법을 정입하였다. 교통망 전체에 대하여 단일 지체함수를 적용하는 기존의 방법은, 교통량 변화에 따른 통행시 간의 변화가 보다 둔감한 고급도로에 변화는 고급도로일수록 둔감하게 나타나며, 교통배정에 도로유형별 지체함수를 적용할 경우 단일 지체함수 적용시에 비하여 고급도로에 더 많은 교통량이 배정되게 된다. 본 연구의 결과, 교통망상에서 보다 현실적인 도로유형별 분담을 이룰 수 있는 방안이 정립됨으로써, 지금까지 교통배정에 있어 상대적으로 과소평가되어 왔던 고속도? 등 고급도로의 실제 타당성을 반영할 수 있게 되어 도로의 기능적 배차구조가 확립된 효율적인 교통망을 구성할 수 있는 계기를 마련한 것으로 판단된다.

• Journal of Korean Society of Transportation
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• v.21 no.5
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• pp.83-95
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• 2003

This study aims to develop travel time estimation and prediction models on the freeway using measurements from vehicle detectors. In this study, we established a travel time estimation model using traffic volume which is a principle factor of traffic flow changes by reviewing existing travel time estimation techniques. As a result of goodness of fit test. in the normal traffic condition over 70km/h, RMSEP(Root Mean Square Error Proportion) from travel speed is lower than the proposed model, but the proposed model produce more reliable travel times than the other one in the congestion. Therefore in cases of congestion the model uses the method of calculating the delay time from excess link volumes from the in- and outflow and the vehicle speeds from detectors in the traffic situation at a speed of over 70km/h. We also conducted short term prediction of Kalman Filtering to forecast traffic condition and more accurate travel times using statistical model The results of evaluation showed that the lag time occurred between predicted travel time and estimated travel time but the RMSEP values of predicted travel time to observations are as 1ow as that of estimation.

• Journal of Navigation and Port Research
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• v.31 no.6
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• pp.447-454
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• 2007

To assess the port development and maritime traffic environment, the future traffic volume has been estimated using the number of inbound and outbound vessel for a specific port. The estimation of future traffic volume should be considered as an important factor to establish the degree of fairway congestion, the determination of fairway width and the operational role. Until now, the number of in and out vessel for the port has been only estimated mainly, but the type and size of inbound and outbound ships are different depending on the port's characteristics. So, it is difficult to estimate the future traffic volume using the change of only one item. This paper calculates the future traffic volume using the marine traffic characteristic factors as the number of coastal ship and ocean-going ship, the size of ship and the change of cargo volume per a ship etc. And it compared with the results of Artificial Neural Network(ANN) for accurate identification of nonlinear system.

• Journal of the Korean Society of Marine Environment & Safety
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• v.19 no.6
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• pp.612-620
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• 2013

In this study, time series analysis was tried, which is widely applied to demand forecast of diverse fields such as finance, economy, trade, and so on, different from previous regression analysis. Future marine traffic volume was forecasted on the basis of data of the number of ships entering Incheon port from January 1996 to June 2013, through courses of stationarity verification, model identification, coefficient estimation, and diagnostic checking. As a result of prediction January 2014 to December 2015, February has less traffic volume than other months, but January has more traffic volume than other months. Also, it was found out that Incheon port was more proper to ARIMA model than exponential smoothing method and there was a difference of monthly traffic volume according to seasons. The study has a meaning in that future traffic volume was forecasted per month with time series model. Also, it is judged that forecast of future marine traffic volume through time series model will be the more suitable model than prediction of marine traffic volume with previous regression analysis.