• Journal of Korean Society of Transportation
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• v.26 no.3
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• pp.187-197
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• 2008

While driving a highway, A driver gets lots of information through geometrical structure, traffic situation, signs on the road. He gets most of the information by visual sense. Acceleration or deceleration and driving direction depend on sight distance. Therefore, it's essential to secure a driver's sight distance for a safe drive. However, design guides of geometrical structure and sight distance suggest respective standards of horizontal and vertical alignment. They do not indicate quantitative standard of combined alignment. Currently, element separated on a two-dimensional projected plane are available, but they do not guarantee safe and pleasant design. I will use the existent model analysing three-dimensional sight distance through mathematical calculation and sort a variety of geometrical structure element and type. In these researches, we will look at how much three-dimensional sight distance is overestimated or underestimated compared to two-dimensional. I will develop a program which predicts traffic velocity on the curvature of two-lane provincial road. stopped sight distance and three-dimensional sight distance will be compared at a predicted drive velocity. I will suggest the way to evaluate road consistency.

• Proceedings of the KOR-KST Conference
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• 1998.10a
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• pp.265-274
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• 1998

본 논문은 도로를 주행하는 차량의 지점속도에 대하여 단기간(short-term)으로 예측하는 네 가지의 모형들에 대한 개발 및 결과의 비교하고 평가했다. 사용된 기법들로는 다중회귀분석, 시계열분석(ARIMA), 인공 신경망, 칼만필터링 등이며, 모형의 구출을 위하여 다수의 독립변수 및 입력변수가 요구되는 다중회귀분석과 인공 신경망에서는 연속방정식에서 고려되는 변수들간의 단순상관계수 및 편상관계수의 계산을 통해서 입력변수가 설정이 되었으며, 시계열분석(ARIMA)과 칼만필터링 등 단일 입력 변수만을 요하는 모형에서는 바로 전 시간대와 현재시간대의간격동안 속도의 변화량을 입력변수로 설정하였다. 속도를 비롯해서 교통 데이터는 현장자료를 사용하였는데, 이는 서울의 한강 옆에 위치한 올림픽대로 중 한강대로에 위치한 검지기 3개를 통해서 천호동 방면으로 이동하는 교통류에 대해서 17시간 (00시~17시)동안 수집했다. 17시간 수집했는데 그중에 검지된 속도는 14km/h에서 98km/h까지 변하는 등, 수집된 자료에는 다양한 교통상태가 포함되어 있는데 이는 각 모형들의 정확한 예측력과 적응성을 평가하기 위함이었다. 각 모형은 예측하고자 하는 시점으로부터 1, 5, 10, 15분 후의 속도를 예측하는 것으로 총 4가지의 예측시간간격으로 각각 실험되었다. 결과는 전반적으로 신뢰성 있게 나왔으나 그중에서도 정확성면에서는 인공신경망과 칼만필터링이 우수했고 적응성면에서는 칼만필터리딩 탁월했다. 또한 1분 후의 속도를 예측하는 결과들은 모형들간에 거의 비슷한 정확도를 보여주었는데 이는 입력변수의 설정이 중요한 것임을 보여주는 것이라 판단된다. 있는 기법이다.적으로 세부적 차종분류로 접근한다.의 영향들을 고려함으로써 가로망 설계 과정에서 가로망의 상반된 역할인 이동성과 접근성의 비교가 가능한 보다 현실적인 가로망 설계 모형을 구축하고자 한다. 지금까지 소개된 가로망 설계모형들은 용량변화에 대한 설계변수의 형태에 따라 이산적 가로망 설계 모형과 연속적 가로망 설계모형으로 나뉘어지게 된다. 본 논문의 경우, 계산속도의 향상 측면에서는 연속적 가로망 설계 모형을 도입할 수 있지만, 이때 요구되는 도로용량이 이산적인 변수(차선 수)로 결정되어야만 신호제어 변수를 결정할 수 있기 때문에, 이산적 가로망 설계 모형이 사용된다. 하지만, 이산적 설계모형의 경우 조합최적화 문제이므로 정확한 최적해를 구하기 위해서는 상당한 시간이 소요되며, 경우에 따라서는 국부 최적해에 빠지게 된다. 이러한 문제를 극복하기 위해, 우선 이상적 모형의 근사화, 혹은 조합최적화문제를 위해 개발된 Simulated Annealing기법의 적용, 연속적 모형의 변수를 이산화하는 방법 등 다양한 모형들을 고려해 본 뒤, 적절한 모형을 적용할 것이다. 가로망 설계 모형에서 신호제어를 고려하기 위해서는 주어진 가로망에 대한 통행 배정과정에서 고려되는 통행시간을 링크통행시간과 교차로 지체시간을 동시에 고려해야 하는데, 이러한 문제의 해결을 위해서 최근 활발히 논의되고 있는 교차로에서의 신호제어에 대응하는 통행배정 모형을 도입하여 고려하고자 한다. 이를 위해서 지금까지 연구되어온 Global Solution Approach와 Iterative Approach를 비교, 검토한 뒤 모형에 보다 알맞은 방법을 선택한다. 차량의

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.6
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• pp.91-99
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• 2015

This study intends to build a traffic accident predictive model considering road geometrics, traffic and enviromental characteristics and identify the relationship of 4-legs intersection accidents in Seoul and Busan metropolitan area. The RPNB(Random Parameter Negative Binomial) model shows improvement over the fixed NB(Negative Binomial) and out of 53 variables, 10 variables (main road number of lane, main road vehicle traffic volume(left), minor road vehicle traffic volume(right), main road drive restriction, minor road sight distance, minor road median strip, minor road speed limit, minor road speed restriction) showed to have significant variables affecting traffic accident occurrences in 4-legs signilized intersections. Also, among 10 significant variables, 2 variables(minor road sight distance, minor road speed restriction) found to be random parameters.

• Journal of Korean Society of Transportation
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• v.22 no.3 s.74
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• pp.109-125
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• 2004

In recent decades, microscopic simulation models have become powerful tools to analyze traffic flow on highways and to assist the investigation of level of service. The existing microscopic simulation models simulate an individual vehicle's speed based on a constant free-flow speed dominantly specified by users and driver's behavior models reflecting vehicle interactions, such as car following and lane changing. They set a single free-flow speed for a single vehicle on a given link and neglect to consider the effects of highway design elements to it in their internal simulation. Due to this, the existing models are limitted to provide with identical simulation results on both curved and tangent sections of highways. This paper presents a model developed to estimate the change of free-flow speeds based on highway design elements. Nine neural network models were trained based on the field data collected from seven different freeway curve sections and three different locations at each section to capture the percent changes of free-flow speeds: 100 m upstream of the point of curve (PC) and the middle of the curve. The model employing seven highway design elements as its input variables was selected as the best : radius of curve, length of curve, superelevation, the number of lanes, grade variations, and the approaching free-flow speed on 100 m upstream of PC. Tests showed that the free-flow speeds estimated by the proposed model were statistically identical to the ones from the field at 95% confidence level at each three different locations described above. The root mean square errors at the starting and the middle of curve section were 6.68 and 10.06, and the R-squares at these points were 0.77 and 0.65, respectively. It was concluded from the study that the proposed model would be one of the potential tools introducing the effects of highway design elements to free-flow speeds in simulation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.14 no.6
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• pp.21-36
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• 2015

These days Automated Vehicle(AV) has been receiving attention as a fundamental solution to resolve the various transportation problems and various researches related to the benefits of AV have been done. However, previous researches mainly analyzed the effects in the virtual network. The purpose of this research is to predict and to find out the benefits by introducing the Automated Vehicle to present road traffic system. Thus, the study analyzes the traffic-flow changes of Gyeongbu freeway Seoul-Singal basic section which is planned for setting the test-bed. The results show that Automated Vehicle can have negative effects on the traffic-flow in low volume of LOS A and B. However, the average speed increases and the traffic density decreases in more than LOS C, the traffic volume increase. Therefore, the introduction of Automated Vehicle achieves positive effect on various transportation problems such as the traffic congestion.

• Spatial Information Research
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• v.23 no.4
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• pp.79-89
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• 2015

This study aims to design and implement a traffic model that can simulate the traffic behavior on the microscopic level by using the GIS. In the design of the model, the vehicle in the simulation environment recognizes the GIS road centerline data as road network data reflecting number of lanes, speed limit and so on. In addition, the behavior model was designed by dividing functions into the environmental perception model, time headway distribution model, car following model, and lane changing model. The implemented model was applied to Jahamun-road of Jongno-gu district to verify the accuracy of the model. As a result, the simulation results on the Jahamun-road had no great error compared with the actual observation data. In the aspect of usability of model, it is judged that this model will be able to effectively contribute to analysis of amount of carbon emission by traffic, evaluation of traffic flow, plans for location of urban infrastructure and so on.

• Journal of Korean Society of Transportation
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• v.28 no.2
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• pp.151-159
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• 2010

This paper presents a research result that was performed to develop a more accurate freeway crash prediction model than existing models. While the existing crash models only focus on developing crash relationships associated with highway geometric conditions found on a short section of a crash site, this research applies a different approach considering the upstream highway geometric conditions as well. Theoretically, crashes occur while motorists are in motion, and particularly at freeways vehicle speed at one specific point is very sensitive to upstream geometric conditions. Therefore, this is a reasonable approach. To form the analysis data base, this research gathers the geometric conditions of the West Seaside Freeway 269.3 km and six years crash data ranging 2003-2008 for these freeway sections. As a result, it is found that crashes fit well into Negative Binomial Distribution, and, based on the developed model, total number of crashes is inversely proportional to highway curve length and radius. Contrarily, crash occurrences are proportional to tangent length. This result is different from existing crash study results, and it seems to be resulted from this research assumption that a crash is influenced greatly by upstream geometric conditions. Also, this research provides the expected effects on crash occurrences of the length of downgrade sections, speed camera placements, and the on- and off- ramp presences. It is expected that this research result is useful for doing more reasonable highway designs and safety audit analysis, and applying the same research approach to national roads and other major roads in urban areas is recommended.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.65-70
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• 2023

In this paper, we propose an autonomous driving system using an end-to-end model to improve lane departure and misrecognition of traffic lights in a vision sensor-based system. End-to-end learning can be extended to a variety of environmental conditions. Driving data is collected using a model car based on a vision sensor. Using the collected data, it is composed of existing data and data with outlayers removed. A class was formed with camera image data as input data and speed and steering data as output data, and data learning was performed using an end-to-end model. The reliability of the trained model was verified. Apply the learned end-to-end model to the model car to predict the steering angle with image data. As a result of the learning of the model car, it can be seen that the model with the outlayer removed is improved than the existing model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6D
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• pp.767-774
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• 2008

This study has been developed the way that evaluates the road safety using the speed of individual vehicles at curve sections in 2-lane rural highways. For this study, we developed variation of operational speed for the individual vehicle using the speed of vehicles in 96points of selected roads. Drawing out of variation of operational speed for the individual vehicle, estimation models for speed variation of individual vehicles considering horizontal alignments and vertical alignments of the roads have been developed. These models presents the way to evaluate road safety out of the operational speed and acceleration of individual vehicles. Considering safety and based on the results of these study above, some regular spots are ranked by "good", "fair", "bad". The results that this study showed in this paper could be useful to derive some particular spots that needs to be improve in terms of safety.

• Journal of Korean Society of Transportation
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• v.27 no.5
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• pp.113-124
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• 2009

This paper suggests a new approach for providing information for driver safety at signalized intersections. Particularly dangerous situations at signalized intersections such as red-light violations, accelerating through yellow intervals, red-light running, and stopping abruptly due to the dilemma zone problem are considered in this study. This paper presents the development of a dangerous vehicle determination algorithm by collecting real-time vehicle speeds and times from multiple point detectors when the vehicles are traveling during phase-change. For an evaluation of this algorithm, VISSIM is used to perform a real-time multiple detection situation by changing the input data such as various inflow-volume, design speed change, driver perception, and response time. As a result the correct-classification rate is approximately 98.5% and the prediction rate of the algorithm is approximately 88.5%. This paper shows the sensitivity results by changing the input data. This result showed that the new approach can be used to improve safety for signalized intersections.