• Proceedings of the KOR-KST Conference
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• 1998.10a
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• pp.21-30
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• 1998

본 연구는 기존 구간 속도 예측기법의 고찰을 통하여 검지기에서 올라오는 교통제어변수를 이용하여 구간 속도 예측모형을 연구하는데 목적이 있다. 이를 위한 교통 제어변수로는 연속류 제어에서 통상적으로 사용되는 교통량, 점유율, 밀도, 속도 등을 사용한다. 공간적 범위로는 서울 올림픽대로의 17개의 영상 검지기 중 #3과 #16검지기에서 올라오는 속도, 점유율, 교통량 자료를 토대로 1998년 6월 11일 오전 7시부터 11시까지의 4시간동안 예측을 실시하며 Historical Traffic Pattern과 시험차량, 자동차 번호판 조사를 통한 구간 실측조사 자료를 토대로 예측을 위한 자료를 구축한다. 기존의 예측기법인 시계열 분석, 신경망 이론, 평활법과 칼만필터링을 고찰하였고, 가장 좋은 예측력을 보여주는 기법은 칼만필터링 모형이었다. 이를 토대로 Case Study를 통해 여러 구간의 다주기 예측을 통해 단기간(short-term)의 구간 속도를 예측하고 각 해당 검지기별 실측자료를 통해 비교분석을 실시하였다. 결과적으로 도출된 칼만필터링 모형의 다주기 예측을 통한 구간 통행속도의 예측이 기존의 구간 통행속도 산출 방법보다 더 나은 예측력을 보여주고 있다.

• Journal of Korean Society of Transportation
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• v.28 no.5
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• pp.141-153
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• 2010

The study is about the development of operating speed prediction models aimed for an evaluation of design consistency of four lane rural roads. The main differences of this study relative to previous research are the method of data collection and classification of road alignments. The previous studies collected speed data at several points in the horizontal curve and approaching tangent. This method of collection is based on the assumption that acceleration and deceleration only occurs at horizontal tangents and the speed is kept constant at horizontal curves. However, this assumption leads to an unreliable speed estimation, so drivers' behavior is not well represented. Contrary to the previous approach, speed data were collected with one and data analysis using a speed profile is made for data selection before building final models. A total of six speed prediction models were made according to the combination of horizontal and vertical alignments. The study predicts that the speed data analysis and selection for model building employed in this study can improve the prediction accuracy of models and be useful to analyze drivers' speed behavior in a more detailed way. Furthermore, it is expected that the operating speed prediction models can help complement the current design-speed-based guidelines, so more benefits to drivers as real road users, rather than engineers or decision makers, can be achieved.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.103-114
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• 2023

This study collected various data of urban roadways to analyze the effect of travel speed change, and a GRU-based short-term travel speed prediction model was developed using such big data. The baseline model and the double exponential smoothing model were selected as comparison models, and prediction errors were evaluated using the RMSE index. The model evaluation results revealed that the average RMSE of the baseline model and the double exponential smoothing model were 7.46 and 5.94, respectively. The average RMSE predicted by the GRU model was 5.08. Although there are deviations for each of the 15 links, most cases showed minimal errors in the GRU model, and the additional scatter plot analysis presented the same result. These results indicate that the prediction error can be reduced, and the model application speed can be improved when applying the GRU-based model in the process of generating travel speed information on urban roadways.

• 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.19 no.1
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• pp.1-16
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• 2020

With the advent of the fourth industrial revolution era, there has been a growing interest in deep learning using big data, and studies using deep learning have been actively conducted in various fields. In the transportation sector, there are many advantages to using deep learning in research as much as using deep traffic big data. In this study, a short -term travel speed prediction model using LSTM, a deep learning technique, was constructed to predict the travel speed. The LSTM model suitable for time series prediction was selected considering that the travel speed data, which is used for prediction, is time series data. In order to predict the travel speed more precisely, we constructed a model that reflects both temporal and spatial effects. The model is a short-term prediction model that predicts after one hour. For the analysis data, the 5minute travel speed collected from the Seoul Transportation Information Center was used, and the analysis section was selected as a part of Gangnam where traffic was congested.

• Journal of Korean Society of Transportation
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• v.24 no.5 s.91
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• pp.109-121
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• 2006

The road should be designed in the consistent alignment which the driver can drive safely. Also, proper highway environments in order to maintain optimal operational speeds on highway sections should be provided In design stage, for highway environments, it is essential for an operational speed estimation model to different highway environments. If a method which could evaluate the status of the road safety is developed through this operational speed estimation model, it is possible to provide safe and more comfortable highways to road users. In the study factors to effect on operational speeds are classified into three groups horizontal & vertical alignments and traffic operation characteristic factors. Factors are chosen to effect on operational speeds by using collation analysis as classifications of tangent sections, horizontal curve sections and vertical curve sections. In order to develop operational speed estimation models in express highways, multi-regression analysis has been used in this study using the selected factors. This study has meaning that the developed estimation models for operational speeds and evaluation of degree of safety to horizontal and vortical alignments simultaneous. In order to represent whole area of the country with the developed models, the models should be re-analyzed with vast data related with road alignment factors in the near future.

• Journal of Korean Society of Transportation
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• v.23 no.7 s.85
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• pp.17-33
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• 2005

Highway design speed is a very important design element which determines highway design level. When determining highway design speed, one would estimate it utilizing the most likelihood of design speed and vehicle operating speed relationship. Existing operating speed prediction models only include highway geometric characteristics and their impacts on speed, which usually can not consider the impact of highway design speed on surrounding roadway environment and land use pattern. If this happens, excessive highway construction cost and huge environmental impact can occur. In this research project, a new vehicle operating speed prediction model was developed which can reflect the effect of surrounding roadway environment into vehicle speed prediction. The followings are the research findings : Firstly, highway terrain types and land use pattern on national roads were classified and integrated into drivers' visual recognition pattern. This was performed using a data management software. Secondly, the developed highway terrain types and land use pattern were related to vehicle speeds and it was found that there were significant statistical differences among vehicle speed for each different terrain and land use pattern. Thirdly. the General Linear Model analysis was employed to analyze the effects of highway geometric features, terrain types, and land use patterns. For two-lane highway and four-lane highway tested in this research project, it was found that R squares were 0.67 and 0.85, respectively. Additionally an optimal highway design speed range table, based on this research project. was proposed for practical use. This table can be reliably used on South Korean national road design, but discretion is required for applying this table to other types of highways including provincial roads and municipal roads.

• 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.

• Journal of Korean Society of Transportation
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• v.28 no.1
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• pp.87-96
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• 2010

The impact speed estimation practice used in the car-bicycle accident analysis practice in Korea was mainly dependent on foreign study results which were tested with limited speed ranges and vehicle types, but the characteristics of roadway, human body, and vehicle performance were quite different. This study developed an impact speed estimation model using the car-bicycle accident field data. For this, a regression analysis was performed using the impact speed and bicycle throw distance collected from 23 real accident data, and statistical test was also conducted. For the verification of the induced model, the impact speeds derived from the model were compared with the true impact speeds estimated from skid marks of two accident cases. The result showed that the two speeds were very close to each other. It is believed that the model could be included in the car-bicycle accident analysis practice.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1177-1190
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• 2009

The traffic operating speed is one of important information to measure a road capacity. When we supply the information of the road of high traffic by using navigation, offering the present traffic information and the forecasted future information are the outstanding functions to serve the more accurate expected times and intervals. In this study, we proposed the traffic speed forecasting model using the accumulated traffic speed data of the road and highway and forecasted the average speed for each the road and high interval and each time interval using Fourier transformation and time series regression model with trigonometrical function. We also propose the proper method of missing data imputation and treatment for the outliers to raise an accuracy of the traffic speed forecasting and the speed grouping method for which data have similar traffic speed pattern to increase an efficiency of analysis.