• 도로교통
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• s.94
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• pp.30-40
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• 2003

• Journal of Environmental Policy
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• v.8 no.2
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• pp.63-82
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• 2009

Because many people suffer from physical and mental damage caused by the noise created by transportation infrastructure, including road traffic, rail, and aircraft, developed countries have conducted research on predicting and solving the impact to human health from being exposed to transportation noise. Therefore, this study suggests a fundamental plan to assess the health impact of transportation noise on the basis of domestic and foreign prediction results regarding the health impact of transportation noise. The domestic and foreign exposure-response expressions, including the noise index and the health impact indicator of annoyance and sleep disturbance, are compared, and it is found that domestic individuals show a more sensitive response to transportation noise. Based on domestic and foreign research, and a case study regarding the health impact of transportation noise, a fundamental plan to assess the health impact of transportation noise comprises the preparation of objective assessment standards through the improvement of exposure-response models, and the establishment of reduction measures which can improve the quality of the transportation noise environment.

• The Journal of the Korea Contents Association
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• v.20 no.4
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• pp.25-37
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• 2020

As social costs of traffic congestion increase, various studies are underway to predict road speed. In order to improve the accuracy of road speed prediction, unexpected traffic situations need to be considered. In this paper, we propose a road speed prediction scheme considering traffic incidents affecting road speed. We use not only the speed data of the target road but also the speed data of the connected roads to reflect the impact of the connected roads. We also analyze the amount of speed change to predict the traffic congestion caused by traffic incidents. We use the speed data of connected roads and target road with input data to predict road speed in the first place. To reduce the prediction error caused by breaking the regular road flow due to traffic incidents, we predict the final road speed by applying event weights. It is shown through various performance evaluations that the proposed method outperforms the existing methods.

• Journal of Korean Society of Transportation
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• v.27 no.4
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• pp.207-215
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• 2009

Generally, traffic accidents can be influenced by variables driving conditions including geometric, roadside design, and traffic conditions. Under the circumstance, homogeneous roadway segments were firstly identified using typical geometric variables obtained from field data collections in this study. These field data collections were conducted at highways located in several areas having various regional conditions for examples, outside metropolitan city; level and rolling rural areas. Due to many zero cells in crash database, a Zero Inflated Poisson model was used to develop crash prediction model to overestimated results in this study. It was found that EXPO, radius, grade, guardrail, mountainous terrain, crosswalk and bus-stop have statistically significant influence on vehicle to vehicle crashes at rural multi-lane roadway segments.

• Proceedings of the KAIS Fall Conference
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• 2006.05a
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• pp.462-465
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• 2006

차가 증가함에 따라, 교통은 혼잡하게 되고, 교통 체증은 더욱 심화된다. 만약에, 교통 체증이나 도로의 속도를 이전의 통계를 이용하여 예측할 수 있다면 상당히 도움이 될 것이다. 본 논문은 다양한 종류의 도로 중 고속도로의 속도에 영향을 주는 요소를 분석하여 상호 영향을 주는 요소를 고찰한다. 이를 수행하기 위해 고속 도로 교통에 대한 데이터베이스를 구축하며, 도로 교통 데이터베이스에 교통 체증의 시간대의 가설을 적용하고, 다양한 데이터 마이닝의 연산을 사용하여 결과를 도출한다.

• KDI Journal of Economic Policy
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• v.35 no.4
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• pp.31-61
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• 2013

The purpose of this study is to calculate the economic value of transport demand forecasting risks in the road PPP project. Under the assumption that volatility of the road PPP project value occurs only in regard with uncertainty of traffic volume forecasting, this study calculates the economic value of the traffic forecasting risks in the case of the road PPP project. To that end, forecasted traffic volume is assumed to be a stochastic variable and to follow the Geometric Brownian motion as time passes. In particular, this study attempts to differentiate itself from existing studies that simply use an arbitrary assumption by presenting the application of different traffic volume growth volatility and the rates before and after the ramp-up period. Analysis of the case projects reveals that the risk premium related to traffic volume forecast of the project turns out as 7.39~8.30%, without considering option value-such as minimum revenue guarantee-while the project value volatility caused by transport demand forecasting risks is 17.11%. As the discount rate grows higher, the project value volatility tends to decrease and volatility in project value is always suggested to be larger than that in transport volume influenced by leverage effect due to fixed expenditure. The market value of transport demand forecasting risk-calculated using the project value volatility and risk premium-is analyzed to be between 0.42~0.50, implying that a 1% increase or decrease in the transport amount volatility would lead to a 0.42~0.50% increase or decrease in risk premium of the project.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.163-164
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• 2021

본 논문에서는 날씨와 상관관계를 갖는 교통사고에 대한 예측을 진행하는 Web Site 개발을 제안한다. 날씨에 영향을 받는 교통사고에 대한 일일 사망자 수, 교통사고 발생률의 각각의 예측값을 딥러닝 모델을 이용한다. 위의 모델을 작성하기 위하여 본 논문에서는 Anaconda 기반의 Jupyter Notebook에서 Python Tensorflow 모델을 작성하여 테스트하고, 만들어진 모델을 웹 사이트에서 불러오기 위해 Python 기반 Flask Web Framework를 통하여 웹 사이트를 개발한다. 개발된 웹 사이트는 사용자들은 Web Site에 날씨 정보를 입력하여 교통사고 발생률을 예측하고 볼 수 있다.

• Journal of Korean Society of Transportation
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• v.31 no.3
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• pp.86-95
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• 2013

In this study, a model of compensation and amendment of forecasted travel demand was developed to calculate the range of values depends on the changes in the risk factors, selecting factors that might affect traffic demand changes among risk factors. Selected factors are as follows: influenced area population, the number of registrated vehicle per person, ratio of service industry workers, and city intervals. Then this model is applied to six routes of expressway and the calculated value were compensated with error rate being reflected on each quartile value with respect to influenced area population (200,000 people standards). Result from appling developed model to Cheongwon-Sangju expressway suggests that the model could compensate the error rate by more than 50%, which in turn validate the effectiveness of the model developed. Some limitations and future research agenda have also been identified.

• International Journal of Highway Engineering
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• v.12 no.4
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• pp.1-9
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• 2010

Predictive information on the freeway incident impacts can be a critical criterion in selecting travel options for users and in operating transportation system for operators. Provided properly, users can select time-effective route and operators can effectively run the system efficiently. In this study, a model is proposed to predict freeway incident impacts. The predictive model for incident impacts is based on short-term prediction. The proposed models are examined using MARE. The analysis results suggest that the models are accurate enough to be deployed in a real-world. The development of microscopic models to predict incident effects is expected to help minimize traffic delay and mitigate related social costs.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.4
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• pp.765-773
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• 2009

Currently Telematics traffic information services have been various because we can collect real-time traffic information through Intelligent Transport System. In this paper, we proposed and implemented a short-term traffic information prediction model for giving to guarantee the traffic information with high quality in the near future. A Short-term prediction model is for forecasting traffic flows of each segment in the near future. Our prediction model gives an average speed on the each segment from 5 minutes later to 60 minutes later. We designed a Bayesian network for each segment with some casual nodes which makes an impact to the road situation in the future and found out its joint probability density function on the supposition of GMM(Gaussian Mixture Model) using EM(Expectation Maximization) algorithm with training real-time traffic data. To validate the precision of our prediction model we had conducted various experiments with real-time traffic data and computed RMSE(Root Mean Square Error) between a real speed and its prediction speed. As the result, our model gave 4.5, 4.8, 5.2 as an average value of RMSE about 10, 30, 60 minutes later, respectively.