• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.3
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• pp.1-19
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• 2021

The domastic hard shoulder running(HSR) System has been gradually expanding since its initial implementation in September 2007 with the aim of increasing capacity and resolving congestion. Hard Shoulder is used as a space for driver's visual comfort and a place for vehicles to evacuate in case of emergency, but it is replaced by a space for driving when the HSR System is implemented. Therefore, it was intended to determine the improvement effect before and after implementation of the HSR system through safety analysis and mobility analysis. The safety analysis analyzed the impact of traffic accidents by comparing HSR sections and similar sections. The mobility analysis was to determine the improvement effect by quantifying the speed and traffic volume changes before and after HSR System implementation. According to safety yanalysis, there is no effect of reducing traffic accidents when implementing the HSR System. In mobility analysis, the implementation of the HSR System significantly improved the speed of traffic during peak hours and significantly reduces slow and delay hours.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.1
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• pp.82-96
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• 2024

While analyzing traffic flow, speed, traffic volume, and density are important macroscopic indicators, and acceleration and spacing are the important microscopic indicators. The speed and traffic volume can be collected with the currently installed traffic information collection devices. However, acceleration and spacing data are necessary for safety and autonomous driving but cannot be collected using the current traffic information collection devices. 'You Look Only Once'(YOLO), an object recognition technique, has excellent accuracy and real-time performance and is used in various fields, including the transportation field. In this study, to measure acceleration and spacing using YOLO, we developed a model that measures acceleration and spacing through changes in vehicle speed at each interval and the differences in the travel time between vehicles by setting the measurement intervals closely. It was confirmed that the range of acceleration and spacing is different depending on the traffic characteristics of each point, and a comparative analysis was performed according to the reference distance and screen angle to secure the measurement rate. The measurement interval was 20m, and the closer the angle was to a right angle, the higher the measurement rate. These results will contribute to the analysis of safety by intersection and the domestic vehicle behavior model.

• Journal of Korean Society of Transportation
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• v.22 no.1 s.72
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• pp.43-62
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• 2004

Conventionally the estimation method of the origin-destination Matrix has been developed by implementing the expansion of sampled data obtained from roadside interview and household travel survey. In the survey process, the bigger the sample size is, the higher the level of limitation, due to taking time for an error test for a cost and a time. Estimating the O-D matrix from observed traffic count data has been applied as methods of over-coming this limitation, and a gradient model is known as one of the most popular techniques. However, in case of the gradient model, although it may be capable of minimizing the error between the observed and estimated traffic volumes, a prior O-D matrix structure cannot maintained exactly. That is to say, unwanted changes may be occurred. For this reason, this study adopts a conjugate gradient algorithm to take into account two factors: estimation of the O-D matrix from the conjugate gradient algorithm while reflecting the prior O-D matrix structure maintained. This development of the O-D matrix estimation model is to minimize the error between observed and estimated traffic volumes. This study validates the model using the simple network, and then applies it to a large scale network. There are several findings through the tests. First, as the consequence of consistency, it is apparent that the upper level of this model plays a key role by the internal relationship with lower level. Secondly, as the respect of estimation precision, the estimation error is lied within the tolerance interval. Furthermore, the structure of the estimated O-D matrix has not changed too much, and even still has conserved some attributes.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.3 no.1 s.4
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• pp.45-52
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• 2004

There are many cities installing ITS(Intelligent Transportation Systems) and running TMC(Trafnc Management Center) to improve mobility and safety of roadway transportation by providing roadway information to drivers. There are many devices in ITS which collect real-time traffic data. We can obtain many valuable traffic data from the devices. But it's impossible to avoid missing traffic data for many reasons such as roadway condition, adversary weather, communication shutdown and problems of the devices itself. We couldn't do any secondary process such as travel time forecasting and other transportation related research due to the missing data. If we use the traffic data to produce AADT and DHV, essential data in roadway planning and design, We might get skewed data that could make big loss. Therefore, He study have explored some imputation techniques such as heuristic methods, regression model, EM algorithm and time-series analysis for the missing traffic volume data using some evaluating indices such as MAPE, RMSE, and Inequality coefficient. We could get the best result from time-series model generating 5.0$\%$, 0.03 and 110 as MAPE, Inequality coefficient and RMSE, respectively. Other techniques produce a little different results, but the results were very encouraging.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.10 no.5
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• pp.124-134
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• 2011

To make popular the NEV(Neighborhood Electric Vehicles) uses, it must be considered the supply of infrastructure and the political decision for NEV. However, the guidelines of infrastructure for NEV are not prepared. The guidelines of infrastructure for NEV should be performed in many research and case. The purpose of this study is to reveal the influence of NEV on the two-lane highway traffic flows by TWOPAS simulation model. The main items to check the influence are Average Travel speed, Percent Time Spent Following and Total Delay. The scenario were setup by traffic volume. And the NEV percentages are changed from 1% ~ 30%. The scenario 1 which traffic volume are 650veh/h and the scenario 4 which traffic volume are 2,600veh/h are less influenced by NEV, compare to scenario 2, scenario 3. Because the scenario 1 is more free to make passing other cars and Scenario 4 is fully saturated with existing traffic volumes. The urban two-lane highway which has much traffic volume and the rural two-lane highway which has little traffic volume has affinity for NEV than the other two-lane highway.

• Proceedings of the KOR-KST Conference
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• 1998.10b
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• pp.508-508
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• 1998

위험물 차량사고는 일반차량의 교통사고시 발생하는 인명피해, 재산피해, 교통지체 외에 부가적으로 환경적 영향에 의한 엄청난 인명 및 재산손실을 유발시킬 수 있다. 따라서 이러한 위험물차량사고를 예방하고 피해를 최소로 줄이기 위해서는 위험물수송경로의 신중하고 체계적인 결정이 필수적이라 할 수 있다. 외국의 경우, 위험물의 방출이 미치는 환경적 영향에 대한 인식이 확대되면서 위험물 수송시 응급처리에 관한 연구, 위험물 수송에 따른 위험도 평가에 관한 연구, 위험물 수송시 고려해야할 여러 조건에 관한 연구, 위험물 수송경로 설정에 관한 연구 등이 진행되고 있다. 반면에 우리 나라는 위험물차량관리와 사고처리에 대해 실시간적인 관리를 목표로 하는 국가차원의 계획을 수립하고는 있지만, 현재 이와 관련된 연구는 거의 없는 실정이다. 앞으로 산업발달에 따른 위험물수송량의 증가와 환경의식의 변화에 따라 위험물수송 및 사고처리 등에 관한 연구가 필요할 것이다. 따라서, 본 연구는 위험물차량의 운송경로를 결정할 때 고려해야 할 여러 가지의 기준 및 목표에 따라 위험물수송경로를 설정하는 모형을 제시함으로써 위험물수송에 수반되는 위험을 최소화하면서 위험물차량의 통행시간, 거리, 비용 등을 최적화하여 위험물수송의 안전 및 운영효율성을 향상시키고자 한다. 먼저, 위험물 수송경로의 기준지표로 사용될 위험도를 산정하기 위해 링크 주변노출인구, 밀도 등을 변수로 하는 모형식을 제안하고, 두 번째로 산정된 위험도를 기반으로 최적경로를 결정하는 알고리즘을 제안하였다. 마지막으로 가상 네트웍에 본 연구에서 제안된 모형을 적용하고 현재 일반적으로 사용되는 최단경로와 비교·분석하였다.것은 운송거리와 운송비용이 각각 주요한 변수라는 것이다. 모형의 타당성을 검증하기 위해서는 logilikelihood 값을 구하여 $\rho$^2분석을 시행하였다. 여기서는 각 품목별로 $\rho$^2값이 약 0.15~0.3의 비교적 높은 수치를 보여주고 있으므로 모형의 설명력이 어느 정도 있다는 것이 아울러 증명이 되었다. 상관관계에 대한 분석에서는 영업용 차량간의 상관관계가 높게 나타났으며, 이는 곧 영업용 화물차량을 적재중량별로 구분하는 것이 별 의미가 없음을 의미한다. 다시 말하면 자가용 차량을 보유하고 있지 않은 회사는 다른 운송전문업체에 화물운송을 의뢰하게 되므로 출하중량에 따라 화물차량을 구분하는 것에 대해서 그다지 큰 고려를 하지 않는 것으로 해석할 수가 있다.적합함을 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.벌레를 대상으로 처리한 Phenthoate EC가 96.38%의 방제가로 약효가 가장 우수하였고 3월중순 및 4월중순 월동후 암컷을 대상으로 처리한 Machine oil, Phenthoate EC 및 Trichlorfon WP는 비교적 약효가 낮았다.>$^{\circ}$E/$\leq$30$^{\circ}$NW 단열군이 연구지역 내에서 지하수 유동성이 가장 높은 단열군으로 추정된다. 이러한 사실은 3개 시추공을 대상으로 실시한 시추공 내 물리검층과 정압

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.5
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• pp.559-569
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• 2021

In this study, using structural equation modeling, a policy acceptance evaluation model was developed to evaluate the service items of roadsthat affect the acceptance of the Safety Speed 5030 policy. The relationship of influence on policy acceptance was found to be as follows: In the driver group, satisfaction with mobility (0.411), economy (0.217), safety (0.181), and environment (0.089) are in the order of top priority; and in the non-driver group, satisfaction with safety (0.466), mobility (0.223), environment (0.194), and economy (0.111) are in the order of top priority. From these results, in order to increase acceptance of the Safety Speed 5030 policy, it is necessary to differentiate the provision of services according to the characteristics of each road user type. This infers it is important to improve mobility for roads with a high hierarchy mainly used by driver groups, and to improve safety for roads with low hierarchy mainly used by non-driver groups. Therefore, the evaluation model for acceptance of the Safety Speed 5030 policy suggested in this study can be used as basic data for activating the Safety Speed 5030 policy in the future by reflecting the qualitative evaluation of users.

• Journal of Korean Society of Transportation
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• v.22 no.7 s.78
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• pp.155-159
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• 2004

Recently, there are many trials about Artificial neural networks : ANNs structure and studying method of researches for forecasting traffic volume. ANNs have a powerful capabilities of recognizing pattern with a flexible non-linear model. However, ANNs have some overfitting problems in dealing with a lot of parameters because of its non-linear problems. This research deals with the application of a variety of model selection criterion for cancellation of the overfitting problems. Especially, this aims at analyzing which the selecting model cancels the overfitting problems and guarantees the transferability from time measure. Results in this study are as follow. First, the model which is selecting in sample does not guarantees the best capabilities of out-of-sample. So to speak, the best model in sample is no relationship with the capabilities of out-of-sample like many existing researches. Second, in stability of model selecting criterion, AIC3, AICC, BIC are available but AIC4 has a large variation comparing with the best model. In time-series analysis and forecasting, we need more quantitable data analysis and another time-series analysis because uncertainty of a model can have an effect on correlation between in-sample and out-of-sample.